Category: Cancer

Cancer

The phrase everyone dreads and wishes to never hear… you have cancer. Cancer will take you by storm not only affecting you, but your loved ones, nurses, and healthcare providers. It is a beast that can drain you and everyone around you emotionally. Treatment can be exhausting and alters clients and their everyday life. With organizations like The American Cancer Society assist families and clients with everyday needs like transportation and support them in difficult times. Communities really come together in times of stress to help the client and try to relieve the financial burdens of caretakers. This project really opened my eyes and made me a whole new understanding of the trials and tribulations families face in times of adversity.

Health Care Providers and the Effect of Caring for Cancer Patients

Caring for ill patients can be very draining, and it does put a lot of demand on nurses and healthcare providers. Caretakers can experience compassion fatigue,” it can be described as physical, emotional, and spiritual exhaustion as a result of caring too much.”(Kurzen, 2012) Nurses and healthcare providers experience this because they grow to know the clients and their families very well. The nurses take care of the same patients coming in for treatments and checkups, and they begin to feel like a friend. They will see these clients in happy and rough times, especially children and teens. “You will see them miss out on things kids their age should be doing like going to prom or missing out on football games, but as an oncology nurse I realized it was my job to step up and make them feel as normal as possible.” D. Russell, RN

Emotional Effect of Cancer on the Client’s Loved One

Nothing is harder than seeing a person you love to fight this beast called cancer. Judy Alli goes on to tell me, “It wasn’t fair when I found out my adoptive mom and sister had cancer. I remember being angry at the world, how could the people who chose me and loved me die such a painful death?” It is important to support and love your family or friends during this time, but family members tend to get emotionally drained, so it is important for them to get time off and time to themselves. Judy goes on to say, “ I remember the pain, and hurt I felt with them fighting cancer, especially my sister. She refused all treatment, and she had renal cancer that very quickly went terminal. Even though she was hurting and tired she would get dressed up every Sunday and go to church. That was her thing and it is the way I remember her.”

Emotional and Physiological Effects on the Patient Undergoing Treatment

“August 2012, the day I felt like my world fell apart. This is when I was diagnosed with breast cancer.” Judy had a simple mastectomy done, in a simple mastectomy, “all breast tissue is removed. No lymph node dissection performed.”( B. Timby,2018) “I was so embarrassed, more like humiliated. I felt like my womanhood had been taken away from me. It was like some of my identity was taken away, and I was even embarrassed to take my top off in front of my husband of sixty-five years.” Cancer can make it seem like your identity is being completely taken away, and it can cause patients to become insecure and a complex may be formed. “The only person who made me feel beautiful is my husband Joe he has always been my person in life and I love him.”

American Cancer Society and How They Help the Community

Cancer is already a big obstacle on its own, but with organizations, they can really make a big impact and help many patients’ families. The American Cancer Society: encourages prevention, provides support, helps people to gain access to care, and helps educate the community on preventive measures for cancer to decrease their risk. (American Cancer Society,2019) The Cancer Society helps families gain access to and find support groups and other ways to ensure all the people involved are receiving proper care and coping in a healthy and safe way. They also relieve caretakers and offer help in transporting patients to all the necessary appointments, plus they also help with finding lodging when clients have appointments at bigger hospitals away from home. The Cancer Society advocates for communities to pursue a healthier lifestyle and teaches healthy eating habits and promotes physical exercise.

The Community Coming Together

In the Permian Basin, we are all one big family, and when they see members struggling they are quick to come together and have fundraisers to bring light to the situation. It could be car washes to golf tournaments, or the Permian Basin coming together. In Midland Texas, “A coalition of anti-breast cancer groups announced Friday this year’s efforts to raise awareness and to encourage women in the Permian Basin to seek early detection of the disease through a series of 14 events in October.” (S. Panta,2016) Events are held to raise awareness and educate the community on topics that aren’t usually addressed until it happens to them. The events are aimed at raising awareness of breast cancer, which the American Cancer Society cited as being the most common form of cancer among American women. The organization also stated that one in eight, or 12 percent, of women in the United States, will develop invasive breast cancer during their lifetime. (S. Panta,2016)

How This Impacted Me

Writing this paper really made me realize how much one diagnosis can change someone’s life forever, I feel so much more aware of the feelings and struggles that families and clients face. When I interviewed my Grandma Judy, I had no idea she was embarrassed by her appearance and how it really affected her confidence including her outlook on life. This experience really made me realize how lucky I am to have a fighter for a Grandma. I was enlightened on how patient care is so important, and how our supporting them could change their whole perspective. When talking to Donald who is an oncology nurse in Las Vegas he really opened my heart to how we need to be nurses and care for patients, but in long term, they become like friends as well. Everyone needs someone, and this really made me realize I picked the right profession because taking care of people and making them feel valued is a priceless feeling.

Conclusion

Whether you are a caretaker, family member, or patient cancer will always be a hard thing to deal with. It can affect your perception of yourself or your outlook on life. It is important that we come together as a community or a friend. This can be a scary situation, but with resources such as the American Cancer Society and other organizations, we can be educated and take measures to make a difference. It is a scary fight, but it’s not a fight you have to fight alone.

References

1. Timby, B. K., Smith, N. E., & Smith, D. W. (2018). Introductory medical-surgical nursing. Philadelphia: Wolters Kluwer.
2. Kurzen, C. R. (2012). Contemporary practical/vocational nursing. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins Health.
3. What We Do. (n.d.). Retrieved from https://www.cancer.org/about-us/what-we-do.html
4. Sterkel, M., Panta, S., & Odessa American. (2016, October 01). Events to shed light on breast cancer. Retrieved from https://www.oaoa.com/people/health/article_867314f8-87e8-11e6-9550-83bab76af8fd.html

Introduction:

Cachexia, meaning bad condition in Greek (4), is defined as “ a loss of lean tissue mass, involving a weight loss greater than 5% of body weight in 12 months or less in the presence of chronic illness”, like cancer (2,4,15,17,18,19,20,22,30,31). To be classified in a state of cachexia, 3 of the 5 criteria must be met along with a 5% weight loss in 12 months. Those criteria include; fatigue, the decline in muscle strength, low serum albumin levels, low fat-free mass index, and changes in biochemistry markers (4,15). There are 3 stages of muscle wasting: pre-cachexia, cachexia, and refractory (2,6). Pre-cachexia is defined as weight loss ≤ 5% with anorexia and metabolic changes (17). Refractory is known as the “terminal stage of cachexia… it is also the stage at which interventions are the least likely to be effective, and it predicts limited survival” (10). While there are stages in this process, not every patient experiences cachexia from pre-cachexic to refractory. Although cachexia may not be able to be fully reversed, a multidimensional treatment approach should be implemented early to help prevent or delay the progression of cachexia (6). Death in these patients is dependent on the amount of weight loss which is why maintaining weight and muscle mass is an important factor (8,29). Prevention will not only help the survival rate but can also help the tolerance of anti-cancer treatments and help increase the patient’s quality of life (5). Research suggests that a combination of nutritional, pharmacological methods and physical activity show the greatest benefit to a patient with cachexia (2).

Significance of Problem:

With any health condition, there are risks that could cause the problem to worsen. Addressing the issue early and being proactive, could help prevent many negative outcomes that stem from health problems like cachexia (6). If preventative measures are not taken early on, the severity and rate of progression could be more severe than they would be if the correct precautions were taken (6). Without prevention, the patient is at higher risk to develop many other health conditions that are associated with the illness (6). Chemotherapy toxicity, shorter time to tumor progression, increased risk of poor surgical outcome, physical impairment, and shorter survival rate are just a few outcomes that could stem from cachexia (6). If cachexia is taken control of early on after diagnosis, a patient’s quality of life could enhance, and this is also important in treatment and recovery (6). Poor quality of life can cause a patient to become depressed and this can create a barrier to a patient’s outlook on life and see the condition as only negative (31).

According to Alves et. Al. (2015), eighty percent of people with advanced cancer will develop cachexia (1). Muscle wasting is more prone to happen in individuals who suffer from pancreatic, stomach, or esophageal cancers (24). Of the eighty percent who develop it, twenty to thirty percent of cancer-related deaths are due to a large amount of body weight loss (1,5,16). While aging humans are predominately disposed to a five to ten percent muscle mass loss every decade after the age of fifty, cachexia can show a five percent loss in less than 12 months (27). While muscle mass loss from aging is normal, certain health conditions that arise later in life such as cancer can exacerbate the decline of muscle mass, but treatments can help reduce these negative effects (8, 29).

Review Of Literature:

Exercise in General

Exercise is defined as a “planned structured and repetitive bodily movement done to maintain or improve one or more components of physical fitness” (17). Exercise, in general, has many positive outcomes for both healthy individuals and also someone who is suffering from cancer cachexia. Exercise can increase the quality of life in more ways than one, such as muscle hypertrophy, lessening fatigue related to cancer, helping inflammatory response, and increasing muscle strength just to name a few. (6, 29). Due to these responses, it was found that physical exercise needs to be implemented in a patient’s daily regime as a complementary treatment to other cancer treatments (22, 25).

One factor that comes from cancer itself is cancer-related fatigue, which could pose a problem in completing an exercise plan. Cancer-related fatigue has effects on many aspects, including quality of life. A study by Dimeo showed that exercise, when tailored to the individual, can increase energy in the patient, as well as, physical and mental benefits, increased functional capacity, improved quality of life, and decreased depression and anxiety (9,14). While trying to find a treatment for fatigue that stems from cancer, the theory of planned behavior by Icek Ajzen in 1985, was introduced into helping cancer patients. The theory of planned behavior was introduced to a group of patients that were experiencing cancer-related fatigue (31). The theory proposed that the motivation or lack thereof, would come from the potential of enjoyment, expected benefit or harm the task could bring, anticipated difficulty, and also a sense of support from others around them (31). It was found in a study by Brown, Huedo-Medina, Pescatello, Pesctello, Ferrer, and Johnson, that patients with cancer who were active, experienced multiple mental and physical benefits, increased quality of life, improved functional capacity, and a decrease in depression and anxiety (9).

Fatigue is different in every individual (9). These levels differ in each individual, it was proposed that each plan needs to be individualized so that each patient feels comfortable with the task before them (9). As stated, every patient is on different levels of the condition, whether it be disease progression or level of energy, which is why physicians are to individualize each exercise plan so they are tailored to the individual needs and energy levels (9). Fatigue, after diagnosis of the disease, is one of the first symptoms that need to be treated to help the patient progress through their treatment plan. This can also help increase the survival rate, as well as, delay the progression of cancer cachexia (14).

There are many negative symptoms and outcomes with cancer cachexia. It causes weight loss, a decrease in exercise capacity, as well a decreased survival rate (17). These are major factors that need to be dealt with to ensure survival and recovery. The loss of skeletal muscle that stems from cancer cachexia causes a decrease in protein synthesis and also an increase in protein degradation (7,15). Protein synthesis is a mechanism in the body that is used to build protein molecules while protein degradation is the process by which cells see the damaged and faulty cells and deteriorate them (7, 15). When these two body mechanisms are working against each other, there are no new proteins being made so muscle starts to waste away (7, 15). In Cancer Cachexia Prevention via Physical Exercise, it was shown that exercise is “shown to be effective at counteracting the muscle catabolism by increasing protein synthesis and reducing protein degradation, thus successfully improving muscle strength, physical function, and quality of life” (16). Physical exercise has the potential to balance protein turnover to anabolic versus catabolic reactions, making it so that there is no loss of protein synthesis and protein degradation only (8).

Cancer cachexia puts the body in a starvation-like state. It produces changes in weight, muscle, and adipose tissue wasting, anorexia, anemia, change in energy balance, and changes in the way the body metabolizes carbohydrates, lipids, and protein (8). In all health conditions, weight loss and starvation halt the recovery process (8). Exercise in any form can promote a disturbance in this cycle (8). It was found in the investigation by Battaglini, Hackney, and Goodwin that these disruptions can help with the promotion of muscle mass retention and hypertrophy of the muscle tissues (8). These disruptions can help lead to improved bodily functions and quality of life in a patient suffering from cachexic conditions (22).

When trying to increase skeletal muscle and muscle mass that has been lost, physical exercise is the only therapeutic method that has been shown to do just that (16). Skeletal muscle makes up to fifty percent of someone’s total body weight and consists of over six hundred separate muscles. When there is an imbalance of catabolism and anabolism, patients start to see a decline in muscle mass and muscle strength (19). Exercise is known to have an anabolic effect, especially when resistance methods are used (5). While there are no concrete ways to help prevent and lessen the effects of cancer cachexia it has been researched that endurance and resistance both show positive benefits. It’s been found that “exercise training can induce an increase in protein synthesis” (28). “In addition, increased protein synthesis depends on exercise intensity. Therefore, high-intensity exercise training is effective in increasing protein synthesis. However, it is difficult for cancer patients with cachexia to perform high-intensity endurance exercises. Low-intensity endurance exercises is an effective countermeasure” (28). In a study by Strasser, Steindorf, Wiskemann, and Ulrich, it was shown that patients’ muscle strength was improved after low volume, moderate volume, and high volume exercises (28). It is known that these all make positive impacts on strength. When dealing with patients suffering from cancer, especially cancer cachexia, it needs to be known that these patients are not as capable to perform at the same levels as healthy individuals (11). Individuals suffering from cancer cachexia can’t always perform at the same level as healthy individuals, individualized plans are the most successful way to reduce the effects of cancer cachexia (9). An example of an exercise prescription recommendation for someone battling cancer would be: after treatment exercise would be 3-5 days per week along with resistance training 2-3 days per week (11). Those that are still going through treatment can increase exercise over the span of a month (11). It was found in a recent study that when the low-load high volume was compared to high-load low volume, both being completed to failure, low-load high volume was more effective when increasing muscle protein synthesis (28). With this information, it may be thought that when trying to increase protein synthesis, the intensity is not the dependent factor as it is the volume of the exercise.

Exercise in general has a positive effect on the inflammatory response. Inflammation is a defensive shield for many different things, such as injuries, diseases, and cancers (13). Chronic inflammation can cause an increase in a person’s risk for cancer (13). Cancer and some of its associated treatments can promote the negative cytokines that promote the pro-inflammatory factor that is not wanted in cancer patients (8). Exercise, in the correct quantities, without pushing the patients over their limit is known to promote upregulation of anti-inflammatory cytokines and can also down-regulate the detrimental cytokines that induce pro-inflammation (8). The anti-inflammatory cytokines help to counteract the catabolism of muscles, it also increases protein synthesis, and helps to reduce the degradation of proteins, this will also help to improve muscle strength, physical function, and his/her quality of life (16). “Acute exercise is known to induce immune response with greatly enhanced production of both cytokines involved in the acute-phase inflammatory response and those that limit the inflammatory response”(16). This anti-inflammatory response has the ability to reduce systemic inflammation that is stemmed from cancer because of this process, it was found that it can relieve the wasting process brought on by cachexia (16). Repeated exercise would be more beneficial to produce anti-inflammatory cytokines if implemented in the early stages of cancer (17). Systemic inflammation that is brought on by cancer cachexia is associated with reduced weight, reduced exercise capacity, and reduced survival (17). “Incorporation of exercise training as a part of the therapeutic regime of cancer treatment can result in these anti-inflammatory cytokines mitigating some of the effects of the pro-inflammatory and through increased androgenic hormonal actions skeletal muscle protein synthesis is enhanced leading to a reduction in the muscle tissue and net protein losses within the cancer patient” (8).

Exercise is beneficial in so many ways. It can increase muscle mass, help a person’s overall mental and personal health, and muscle strength, reduce fatigue, increase metabolic changes, decrease inflammation, and many more. The processes within a body keep it going and help fight against infections and diseases. Even if a person did not exercise before they developed cancer cachexia, it is shown to help during the treatment process (28). The effects of exercise outweigh many treatment methods, but it is best to be used with other treatments for optimal success.

Resistance

Most people know a resistance exercise is a form of exercise that gains muscle and strength. Knowing that this is the outcome of resistance training and knowing that cancer cachexia produces a loss of muscle as well as weakens an individual. This regime of exercise seems a perfect combination to help combat the negative outcomes of cachexia.

Resistance training provides an increase in muscle fibers, which in turn helps gain muscle mass and also helps increase body weight, which is needed in patients suffering from cancer cachexia (18). Cancer cachexia treatments need to be a multifaceted approach. When individuals are on a positive basis on certain aspects such as nutritional status, pharmacological method, and exercise training, there is potential for a positive outcome to be reached (2,18). Resistance exercise can go farther past just building muscle and building strength. It can also help build confidence in the patient to ease their mind (21). Wondering if they can withstand having to do daily tasks of living can put a toll on individuals’ quality of life (21). Resistance can also improve the daily function of patients (21). With all the benefits of resistance training, it seems to be a viable option when treating to prevent, delay or reverse muscle wasting (21).

In a study by Little and Phillips, it was found that in a 12-week program of resistance training that both type I and type II muscle fiber size increased and also improved muscle strength by 25-30% in patients with renal disease muscle wasting (16,21). In an 8-week study, conducted on patients with HIV muscle wasting, a resistance program increased muscle strength by 60% and lean body mass by 5% was found (16,21). In another study, it was found that using high-intensity resistance training in patients during chemotherapy that there was an average increase in muscle strength of 41.3%, as well as, a 1% increase in body weight (16). With cancer cachexia wasting muscle at 5% or more in 12 months, this form of treatment can help delay the onset of cachexia or even refractory by slowing down the process because of the increase in muscle strength percentage (4). Resistance training showed in a study by Strasser, Steindorf, and Wiskemann that there were significant improvements in muscle mass and muscle strength in both upper and lower limbs (27). The 14.6kg increase in lower limb muscle and a 6.9 kg in the upper limbs in a period of 12 weeks to one year (27). These large gains produced in the study can have the potential to increase the individual’s life expectancy (27). In the same study, it was predicted that if an individual with cancer cachexia performed resistance training two times a week there is potential to have an increase in muscle mass by 1-2kg per 6 months (27). Increases of that magnitude can help prevent disease muscle wasting as well as age-associated muscle wasting losses. If this type of treatment could prevent patients suffering from pre-cachexia from progressing into cachexia or cachexia into refractory, the chances of survival would increase in the individual.

The musculoskeletal part of a human is so vital to daily living that it will have the potential to slow the progression of cachexia. “An improved musculoskeletal fitness is linked to bettered health status and greater quality of life (29). As described before, quality of life is important in the recovery process. Exercise has been found to improve a person’s mental health which is needed in all deterring health conditions Resistance training generates force in the muscle, before any other improvement the first is increasing the size of the muscle (29). This hypertrophy is due to the increased protein synthesis in actin and myosin, which is reduced during cancer cachexia (29). This increase in protein synthesis in the muscles is the element that increases muscle mass and muscle strength (29). When trying to produce improvement in muscle mass or muscle strength, whether in a healthy individual or someone suffering from cachexia, it is necessary to keep increasing the exercise prescription (29). Modifications are different for each individual as they begin to become more familiar with the training or when they start to see plateaus in the improvements (27). “Resistance training is well established non-pharmacological anabolic strategy promoting skeletal muscle hypertrophy and improving muscle function” (23).

Aerobic training

Aerobic training has been found to increase multiple different aspects that are, produced through cancer cachexia. Cancer cachexia pathogenesis has the following linked; anorexia, metabolic issues, inflammation, and enhanced muscle proteolysis (7). Aerobic exercise provides improvements in quality of life, exercise capacity, flexibility, body composition, fatigue, muscle endurance, pain, nausea, diarrhea, sense of control, depression, self-esteem, and life satisfaction were all found in cancer patients during or following aerobic training cancer treatments (29). With these improvements, it is likely that providing aerobic treatment during or after cancer treatments provide positive effects on the individual in quality of life and physical functioning (29).

This form of training stimulates oxidative metabolism (5). Oxidative metabolism is directly correlated with the prevention of hyperlipidemia, a condition where there are high levels of fat deposits in the blood, this can restrict blood flow through the vessels, increasing the risk of heart attack or strokes (5,11). It can also improve insulin resistance; aerobic training can help decrease the amount of insulin that is found in the blood and decrease the chance of developing type 2 diabetes (1). The inflammatory response is also reduced when aerobic training is implemented (5). “Regular exercise appears to significantly lower circulate C-reactive protein and inflammatory cytokines concentrations” (26). C-reactive protein is a protein made by the liver and is sent to the bloodstream as a response to inflammation, high levels of C-reactive proteins mean there is a serious infection in the body (12). It also lowers the concentrations of plasma that has interleukin, and tumor necrosis factor-alpha (26). In the study by Laura Stewart et al., there was found to be a 58% reduction in the C-Reactive protein due to aerobic exercise (26). When healthy active individuals are compared to inactive individuals, the active individuals show a response of a lower concentration in the blood with C-reactive proteins (26). In a study done in animal models, it was found that chronic endurance exercise prevents/ reduces the symptoms of cancers, so it was proposed that endurance exercises can be a low-cost, and safe option for patients with inflammations (20).

Aerobic exercise is “associated with the prevention of a large spectrum of disorders” (1). This exercise regime causes many adaptions to happen in the body; inducing protein turnover, mitochondrial biogenesis, and anti-oxidant capacity (1). While cancer cachexia is an imbalance of protein synthesis and degradation, protein turnover can help balance out these two battling forces, helping to stabilize the wasting process (15). Helping to stabilize this process can also lead to more mitochondria being stored and made in the body (1). With this, you can also expect an increase in mitochondrial biogenesis because the more muscle, the better the ability of the muscle to make and store these mitochondria (1). Mitochondrial biogenesis is the process in which there is an increase in the number of mitochondria being made in the body, which in hand also helps the muscle uptake more glucose, helping the insulin sensitivity that cancer cachexia presents (1). Aerobic exercise training “also included an increase in aerobic consumption of substrates and prevention of glucose and glutamine metabolism impairment in immune cells” helping to increase insulin sensitivity(1). There are many benefits that aerobic exercise is associated with, including, increased survival, decreased tumor growth, prevention of body mass losses, and a decrease in skeletal muscle degradation (1).

Ubiquitin-proteasome and autophagy are also increased through aerobic exercise training (1). Both of these processes maintain the cellular mechanisms and recycle damaged organelles that happen because of cancer (1). Autophagy will improve muscle energy balance because it rids the body of damaged and aged mitochondria (6). Aerobic training has benefits such as autophagy, positive metabolic changes, and oxidative metabolism processes making it easy to see that this form of exercise needs to be implemented in someone who is suffering from these types of muscle wasting diseases. It is a low-cost, low-risk form of rehabilitation that could help prevent, delay and possibly reverse the disorder at hand.

Conclusion:

Your body is made up of over 600 different muscles and contributes up to 50% of a person’s total body weight (19). Cachexia starts to diminish these muscles and induce muscular wasting and a loss of overall body strength, along with the normal decline in muscle because of aging (27). A decrease in protein synthesis and an increase in protein degradation in combination produce the loss of skeletal muscle mass (7,15). This can account for twenty to thirty percent of cancer deaths (1,5,16). This high prevalence of deaths makes prevention a key factor in slowing down this process. Prevention won’t only affect the survival rate but can also help the tolerance to anti-cancer treatment, and quality of life (5,6,30). Exercise, in any form, whether aerobic or endurance seems to promote good outcomes for someone who is suffering from cancer cachexia. It has been shown to increase lean body mass, and functionality, increase strength, increase anti-inflammatory cytokines, decrease the pro-inflammatory cytokines, increase protein turnover, and increase oxidative metabolism, and insulin sensitivity (8). It can also help prevent and slow down these negative determents that cancer cachexia can put onto the body (5,6,30).

Exercise is a low-cost and low-risk form of treatment that when used and prescribed correctly can cause great outcomes (3, 20). To make the best impact on the individual, a multidimensional approach needs to be done, exercise, nutrition, and pharmacology (2,6). Physical exercise produces many great outcomes but these potential benefits can be abrogated if the patient does not have a good nutrient basis (5). The nutritional state needs to be looked at in the patients because if not enough nutrients in the body this could further the wasting process in the individual (5). Pharmacological can be added, such as steroids, and at the appropriate time, nutritional supplementation can promote large gains in muscle mass in cachexic patients (21).

It was very emphasized that cachexia needs to try to be prevented instead of trying to be reversed (6). This makes early recognition of cachexia a very important factor in survival, it can also help reduce its effect (6). When cachexia is diagnosed earlier in the progression, treatments can be used to slow down the process (30). Another early factor is the introduction of physical activity (3). It is important in health maintenance and cancer prevention, but now, research shows that it can be a vital part of cancer treatment (3). The exercise showed reductions and improvements in many cancer patients’ progression from cachexia. Reduce fatigue, improve strength, improvements in muscle mass, increase function capacity, increase quality of life, and many other cachexia-prone impairments (8,9,14,16,18,21,27,28,29,31). This makes it important to start implementing physical activity to help prevent or delay cachexia effects (6). Even if exercise was not a part of the patient’s life beforehand, exercise has still shown benefits during and after treatment, it helps improve quality of life and reduces fatigue brought on by cancer (6).

Exercise dosing is a very important individualized component. While exercise helps decrease the amount of fatigue, every patient needs to be evaluated differently than the last and differently than the next (5,6). Cancer and cancer-related cachexia affect each individual differently, as well as, each patient will have a different exercise capacity (5,6,9). A large part of a patient’s recovery will be dependent on the exercise program and their ability to perform the exercises at the given workload and intensity (5,6,9).” Exercise training needs to be compatible with the exercise capacity of the cancer patients” (5,6). Aerobic and resistance exercises both have shown numerous advantages when implemented in the recovery of cachexic patients. Aerobic and resistance exercises have also been “accepted extremely well and enjoyed when the programs are tailored specifically to the individual patient” (8).

Exercise alone or in conjunction with the other methods has a lack of negative effects and an abundance of positive benefits. With all this information on the positive effects of exercise, it has proven as a low-risk therapy that can improve activities of daily living and improve quality of life (3). Being proactive needs to have a greater emphasis placed on it, this will have the goal to maintain or delay the process of losing physical function (17). It has been proven to be a safe complementary treatment (22). Physical exercise needs to be recommended for everyone because of the role it has in preventing these negative effects of cancer (3). Physical exercise has been successful in increasing muscle mass, and muscle strength, reducing fatigue, enhancing the quality of life, and has had no negative effects reported, making this treatment option safe and beneficial to a person suffering from cancer cachexia (3,8,9,14,16,18,21,27,28,29,31).

Future Research:

There are multiple different research aspects that could provide beneficial information on delaying cachexia effects. While multiple studies showed the effects of exercise on patients after treatments, a study that showed the effects of muscle mass and strength on these patients during active treatment could show some positive results. While chemotherapy and radiation can have deleterious effects on muscle mass, and strength, it also shows fatigue (6). A study by Dimeo showed that exercise when tailored to the individual can increase energy in the patient, as well as physical and mental benefits, increased functional capacity, improved QOL, and decreased depression and anxiety (9,14) it is also known that resistance exercise can increase muscle mass and strength. A new study could be beneficial to see the kinds of results that could come from trying physical activity during active treatments to see if the results from the exercise can delay the negative effects or even help sustain the amount of muscle mass and strength that an individual has before the start of the disease. If the positive effect of exercise can equal or increase the body mass and strength of pre-disease then pushing for patients to include physical activity during treatment would be a new modality that could be added. So when these are used coincidently the exercise may be able to help reduce the number of negative effects put on the body.

Another spin-off to the above future research would be to see if patients who were physically active before their diagnosis helped to lessen the cachexia effects or slowed the down the progression. Having a larger percentage of muscle mass can contribute to a longer time frame of how severe the negative effects arise in the patients. This could provide more time to further slow down the progression, as well as, have a longer time frame to cure cancer. Being able to cure cancer before the severe effects show up could potentially raise the survival rate.

If being previously active was not in the patient’s lifestyle. Researching how the effects differ if exercise is added in at different stages. Timing can be key in many situations, including the diagnosis of cancer. While someone in the refractory stage has a very low survival rate, would introducing exercise provide enough time to make any difference in the patient’s final outcome, whether it be cachexia or cancer-related?

Role of Astrology in Diagnosis of Cancer

Abstract: Someone rightly pointed out ‘Health is Wealth.’ It is very important to take care of good health. There are various branches since the ancient era who started to study on disease and the causes to occur diseases’. In India, Ayurveda is the oldest branch that started studying medical science. Moreover, we can say that since the ancient era, India is having study and medicine in Ayurveda. Turmeric is the best antibiotic- is the invention of Ancient Indian Medical treatment i.e. Ayurveda. Now it has been accepted worldwide about the importance of Ayurveda in Health Science. The most and hardest non-curable disease which is problematic to the universal level is cancer. Indian Health Science works at two different levels e.g. Ayurveda and Astrology. The combination of Astrology which specifies Cancer and though the disease of cancer may be indicated astrologically through several amalgamations yet Varna Yoga has been observed in many cases. Diseases can also be known through a detailed analysis of a birth chart and studies on the basis of the birth chart is called as Astrological Identification of diseases. A good astrologist is in a situation to see the risks of diseases that a native could suffer from his life. However, there are a unit numerous aspects that one ought to take into thought whereas creating such predictions. The readers shouldn’t get influenced with a single fact of their horoscope. There is a unit numerous things that area unit analyzed so as to see the precise results. In Janmkundli which is the part of Astrology prepared on the basis of Birth date, time, and place during the birth mentions various houses and their impact on the life as for this sickness, the homes ought to even be afflicted beside the malevolent planets. Apart from this, the dasha-antardasha are analyzed, besides the position of transiting planets. Taking into consideration of the disease i.e. Cancer –it is expounded to Hindu deity, afflicted Moon, afflicted Jupiter or Saturn and conjointly forms relations with signs as well as, Aries, Taurus, Cancer, Libra, and Capricorn. The native encompasses a risk of full of cancer once the Moon is afflicted being the lord of the sixth house or eighth house in its unfavorable dasha. This all indicates the importance of Astrology in identifying the health issue of a human being. Hence current research work focuses on the role of astrology in the diagnosis of cancer for individuals.

Key Words: Disease, Astrology, cancer, medical science, diagnosis, Ayurveda, etc.

Introduction: In this technical and global era, there is a drastic change in human life. Technological growth created human life very easy and comfortable. The death ratio is decreased because of the continuous invention in Medical science. Yet, cancer is called as the ‘Death’ due to non-curable disease. Indian Ayurveda and Astrology are now accepted worldwide as a unique work of India. Due to the recourse of proper preventive, remedial, and corrective spiritual measures many cases got success in curing cancer- proved by researchers also. In Indian Astrology, the maximum study depends on the Janma kundali- A Janam Kundali is a basic tool for creating pseudoscience predictions. A Kundli or horoscope is an associate degree pseudoscience chart or a diagram showing the positions of planets, moon, and the sun is formed on the idea of the date of birth, place, and time. Janam Kundali is chart in Hindu pseudoscience. The causes of various diseases are mentioned in Kundali. There may me clarification for Cancer sickness in Janam Kundli. Cancer may be a future disease. It can be identified with the study of houses and their relation with each other in Janm-kundalini. Thus, it’s imperative that the sixth and therefore the eighth house form a relationship with one another. One is likely to suffer from cancer if Mars and Mercury if afflicted, are in or own the 6th, 8th, or 12th house. This is often as a result of the sixth house determining the sickness and therefore the eighth house signifies the diseases that last long.

According to Indian Vedic Astrology, there are a total of twelve houses in the Janma Kundli of Individuals. According to the time, place, and date of birth, these planet houses are changed and the impact of their places shows on the life of individuals. About health issues and how they affect a few examples of houses and their effect on health has been mentioned.

Following are a few examples of planets and their impact on Health:

• Sr no.
• Name of planet
• House no. in Kundali
• Impact on Health

1. 01

• Rahu
• Sixth
• It is the house of disease, enemy, and Debt

1. 02

• Rahu
• Eighth
• This house is ruled by Mars so effect on Health issues.

1. 03

• Sun
• First
• If the sun is with Saturn and Mars then has blood impurities, aggressive nature, eye diseases, fevers, and itching problems.

1. 04

• Jupiter
• First
• If it is in the first house, the Blood impurities make him/her sick

1. 05

• Sun
• Sixth
• Suffers from heart disease

1. 06

• Moon
• Sixth
• Suffers from stone in urinal tract

1. 07

• Rahu
• Fifth, eighth, and Twelve
• Mental Illness

Table of Kundli houses and Diseases

As shown in the above table, the house number placed in the Kundli shows its effect on an individual’s life and health such as Sun and Moon area unit accountable planets for an accident. After that, Rahu, Mars, and Shani create an accident. However, Jupiter, Venus, Mercury, and Moon shield North American countries from accidents.

Rahu is taken into account to the karaka of cancer. However, Mars and Saturn can even provide these diseases. Jupiter is taken into account to be the lord of growth and cancer takes place with the expansion of dead cells within the body. The chance of cancer is additionally high once the lord of the eighth or sixth home is afflicted. Following are a few planets and the chances of occurring disease it:

• a. Sun – Diseases of the top and eye, bile, T.B., epilepsy, cardiovascular disease, disease of the skin.
• b. Moon – Diseases of the eyes, stomach, intestines, face, and mind, mental illness, phlegm, cold, asthma, T.B.
• c. Mars – Sores, ulcers, leprosy, epilepsy, diseases of blood and neck.
• d. Mercury – Diseases of the liver, navel, and encompassing space, brain disorders, skin diseases, coma, and nervous disorders.
• e. Jupiter – Diseases of ear, neck, nose, and heart, asthma, phlegm, T.B., venereal illness.
• f. Venus – Diseases of the eye, venereal illness, piles, illness of privates.
• g. Saturn – an illness of leg, lameness, loss of consciousness, giddiness, insanity, rheumatism.
• h. Rahu and Ketu – Diseases because of intake of poison, those caused by worms, bacterium, and bacilli of the abdomen, insanity, cancer, epilepsy, and heart diseases.

These explainains the importance of Indian Astrology in Science and medical science.

Indian Astrology and Science:

The prophetic Indian Astrology star divination relies upon some logic given by the sage Parashara. It is not a Science however there lies some strength or truth in. it is still gifted during this world. The success of the associate Indian sacred text predictor depends upon what quantity correct he/she will tell regarding the associate unknown person or his life or what number of his shoppers get happy by his predictions. No predictor is 100 percent correct. The prediction of a Jyotishi on some consumers goes wrong as a result of the consumer don’t offer correct birth details. Many consumers offer calculable birth details with heaps of confidence. Some shoppers don’t need to hassle to enquire their correct birth details from their elders as they are doing not notice the importance of correct birth details in Jyotisha.

An Indian Astrology will himself suffer in life thanks to his own unhealthy luck or a nasty section of your time.

The job of an associate Indian Astrologer is not to bluff his/her profession by claiming that he/she will modification their destiny by prescribing the result and so known as remedies like stones, mantras, tantras, yantras, yagna, havan, poojas, homams, or deans. However, solely to inform one thing regarding their destiny & to alert them of some returning smart or unhealthy section of your time in their lives in order that their profession will prepare themselves prior to (if the state of affairs appears to be avertable or if its unhealthy impact may be minimized) consistent with the sensible ways in which as urged by the Indian Astrology predictor. Although neutering one’s destiny is not that simple & it cannot be through without the bounty of God.

The Pseudoscience Cause: If Planet Saturn, Rahu, and Mars area unit placed unfavorably and Sun and Moon are weak within the horoscope, skin infections seem when unhealthy planets (Saturn, Mars, Rahu, Kethu, Uranus, Neptune, Pluto) area unit posited in unhealthy homes like the sixth house which is known as the place of disease, eighth house which is called as the place of longevity and twelfth house which is known as a place of death. Once unhealthy planets occupy the signs: Virgo, Scorpio, and Pisces in star divination, that planet is liable for weight gain. Your weight gain is especially caused by the planets Jupiter, Moon, and Saturn. Jupiter is liable for managing fats, sterol, and liver health. Once your Jupiter is inactive then there would be a management of lipids. Mercury governs brain nerves, Moon controls the mind, heart, and abdomen. Jupiter rules over the liver and lungs. The components of the body that causes sleep disorders area unit the brain nerves, lungs, and liver. Thus these 3 planets’ area units are liable for providing sound sleep.

Astrology and Diagnosis of Cancer: it has been already mentioned in the above table as an example that Astrology will predict health issues or injuries before their actual look within the chassis. However, all the planets, sensible or unhealthy, and the sun and also the moon will have a nasty impact on an individual’s health.

CANCER as everybody has a fear for it because it is known as another name of death. On the idea of our expertise of the last forty-five years, we tend to maintain that cancer will be cured through correct identification. In an exceeding variety of cases, we tend to were ready to determine the chance of cancer with success, a lot of prior to its incidence. The natives were affected by cancer, were fully cured when taking recourse to correct preventive, remedial, and corrective religious measures. Hindu deity and Ketu purpose to cancer. One is probably going to suffer from cancer if Mars and Mercury if afflicted, area unit in or own the sixth, eighth or twelfth house.

Generally, it’s believed that cancer may be a death tantalizing sickness. However, pseudoscience identification if created a lot of before its incidence makes the cure virtually sure. Medical and remedial measures build this potential.

Planet House and Cancer:-

The affliction of the sixth, eighth, and/or twelfth house, their lords, and occupants affect on the health issue and especially cancer and they show their effect in the following manners:

1. Planet of Cancer: The main significates of cancer area unit Hindu deity, Mars, Mercury, and Saturn. Hindu deity is the poison of cancer, whereas Mars offers rise to neoplasm, cyst, boils, wounds, cuts, operations, etc. Mercury multiplies the cells of cancer if afflicted and Saturn makes it chronic and incurable.
2. Lord Dusstanas: Rahu, Mars, Mercury, and Saturn area unit connected with the Dustanas and watery signs or side one another or the lords of Dustanas.
3. Gemini and Virgo: These two planets also have a lot of to try and do with cancer, if these signs area unit occupied by Mars, Mercury, or Saturn identical with Dustanas.
4. Jupiter conjointly plays an important role in inflicting additionally as preventing cancer. If Jupiter is unaffiliated and is placed in an angle or triplet and isn’t accepted by malevolent Mars or Mercury or if Jupiter aspects the Ascendant, the ninth or fifth house it’ll defend the native if he’s affected by cancer. However, if Jupiter is within the sixth or the eighth or aspects of these homes below affliction by Mars, Saturn, Hindu deity, or Mercury, one is probably going to suffer from incurable cancer.
5. Mercury: This planet is the lord of Skin and this can be the key indicator for a disease of the skin. Since Venus is karaka for cosmetics, in many cases it has been seen that Venus’s affliction at the side of mercury causes skin allergies as a result of cosmetics. Additionally Moon afflicted by Rahu denotes disease of the skin to the native. In star divination, the world mercury and also the sixth house within the horoscope is liable for skin-connected disorders.

Various Types of Cancer and its diagnosis by Astrology:

• Stomach – Sometimes in conjunction with a harmful or receives evil facet from a malign the lord of the sixth house shows this effect on health. Alternately, the sixth lord himself would be malign within the twelfth, and also the sixth is occupied by a harmful effect. The Sun is the planet principally afflicted. The Rahu-Ketu axis is sometimes seen across within the Ascendant and also the seventh house.
• Colon and Body Part – The Sun, Mars, and Ketu ought to be studied rigorously. The sixth and also the eighth houses in the Kundli and correspondingly Virgo and Scorpio signs would feature in these kinds of cancers.
• Bladder – This type of cancer happens three times more in men than in females. Cancer of the bladder makes itself legendary initially by a modification in bladder habits. Long before blood seems in wee-wee, planetary positions within the horoscopes would provide a sign of the upcoming danger. The Sun, Mercury, and Mars sign Virgo, the sixth house and its lord ought to be examined rigorously. Sometimes the Sun and Mercury alongside the lord of the sixth house were severely afflicted by Mars and Saturn and as was common in the Rahu-Ketu axis.
• Uterus – The sixth house and sign of Virgo are connected with Mars and Venus.
• Breast – Astrologically, the fourth and also the tenth house of the Kundli govern the breasts, the fourth right facet and also the tenth, the left. The Moon typically governs the breast whereas glands come back beneath the jurisdiction of Mars. It implies that if the fourth and also the tenth house of Kundli, their lords, the Moon, and Mars are all afflicted and to feature there to if the Rahu-Ketu axis is additionally afflicting these planetary positions then there is the probability of carcinoma.
• Blood – Blood cancer may be a cancer of the blood-forming organs, mainly bone marrow. The Moon and Mars, the 4th, the 8th, and 12th houses of the Kundli, and their, lords are to be studied closely. If these all are afflicted then cancer may be a certainty. As declared earlier, the Sun is the planet for all unrestrained growths and ought to have prime thought in cancer cases.
• Liver -The fifth house and lord of that house ought to be afflicted by this kind of cancer.
• Lungs – Jupiter and also the Moon are the planets that need the most careful thought. Besides the fourth and also the sixth houses in the Kundli and their lords, signs Gemini, Cancer, Virgo, and Sagittarius are closely concerned with lungs and they are thought as the most afflictions in these signs. It’s usually seen that in cases of carcinoma common signs are within the sixth house and or watery signs within the eighth house.

Vulnerable Degrees:

There is still sizable scope to additional slim down the sphere of study and at last trace the precise degree of the Zodiac that activates the cancerous growth in sure components of the chassis. Carter is of the opinion that the twenty-fifth degree of Virgo-Pisces which is called as Sayana system may be a common space of affliction as that degree looks to be connected with swollen conditions typically, tumors, and its growths.

The star Labarum is placed at 25° 35′ comparable to Narayana at 2° 5′. This can be a star of the fourth grade and partakes qualities of Venus and Mercury. It’s already seen that these planets are usually afflicted during this illness. There’s another star close to regarding the on top of degree. It of Zavijaya and is found at 26° 2′ with corresponding to Narayana at 2° 32′. This faint Xanthus star is preponderant of Mars-Mercury characteristics and is alleged to be concerned in cases of polygenic disorder, particularly if planets are afflicted around this degree and are within the sixth from the Moon.

Conclusion: Thus, the above-all discussion explains the importance of Vedic Astrology in the diagnosis of diseases, especially of Cancer. Since the early 19th century, the concept of Hindu astrology has been in use as the English equivalent of Jyotiṣa. But the Vedic astrology is a relatively modern concept, entering common usage in the 1970s with self-help publications on Āyurveda or yoga. Vedanga Jyotishya is one of the earliest texts about astronomy within the Vedas. Though Cancer can be defined as any uncontrolled growth of body cells, which has destroyed the adjutant tissues; Zodiac Sign will help to find out your long journey disease and even can work as remedies to cure ourselves from it. If not treated cancer and it may also spread to other parts of the body through blood and lymph. Cancer may affect people at all ages, but the risk increases with age.

At the global level, Cancer amounts to about 13% of all human deaths. Up till a few years ago, it was incurable. On the premise of scholarly expertise of the last forty-five years, astrology tends to maintain that cancer may be cured through correct designation. In a very range of cases, astrology tends to were able to establish the chance of cancer with success, a lot of sooner than its incidence. The natives, World Health Organization were affected by cancer, were utterly cured once taking recourse to correct preventive, remedial, and corrective nonsecular measures. Rahu and Ketu’s purpose to cancer. One is probably going to suffer from cancer if Mars and Mercury if afflicted, are in or own the sixth, eighth or twelfth house.

Generally, it’s believed that cancer could be a death invitatory wellness. However, pseudoscience designation if created a lot of before its incidence makes cure virtually bound. Medical and remedial measures create this attainable. In 2000, once many planets happened to be getting ready to each other, astrologers foretold that there would be catastrophes, volcanic eruptions, and periodic event waves. This caused a complete seaside village within the Indian state of Gujarat to panic and abandon their homes. The anticipated events failed to occur and also the vacant homes were burgled. There are numerous studies that started to work on Indian Vedic Shastra, Astrology, and so on concern with it. Using remedies suggested by astrologers will help to see the positive impact of medical science and medical treatment on individuals suffering from cancer. Because of it many research scholars of word-wide started to study on Indian Vedic Astrology. Hence, we can’t deny the importance of Vedic Astrology in Science and even in medical science to diagnose disease, the root causes of disease, and other affecting factors of an Individual’s success and failure of life.

References:

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The thought of the word cancer brings up so many emotions due to its negative connotation. Cancer has created this poor reputation by taking the lives of so many innocent people each day. Cancer is irregular cell growth which is due to the alterations of DNA caused by many lifestyle factors. During 1981 it was estimated that diet was responsible for 35% of cancers in the United States. This essay focuses on the impacts that a vegetarian diet can have on decreasing cancer risk.

The phytochemicals included in a plant-based diet has shown to develop a healthy body. An Oxford controlled study performed in 2007 using 3,277 people of same ethnicity, similar age and lifestyle habits showed that vegetarians had reduced risks for certain disease such as cancer by having an overall greater health than omnivores. The study results showed that vegetarians had 10% lower cholesterol, 25% lower chance of having IHD, and a lower BMI mean of 22 whereas omnivores had a mean of 25. Another cohort study done by EPIC-Oxford shows that these factors are highly correlated to cancer. A vegetarian diet is full of phytochemicals that protect the body from changes in the DNA.

Studies have proven that the consumption of animal protein such as dairy products, and meat are associated with the risks of several types of diseases such as cancer. In a randomized study done with 93 patients with prostate cancer showed that the patient’s health significantly improved with zero intake of meat and high intake of low-fat diet including lots of fruits, vegetables and whole grain foods. This study was followed up for a year and results showed that patients saturated fat content decreased from 20 to 5g/day, cholesterol content from 200 to 10mg/day, as well as fiber content from 31 to 59g/day. These protective nutrients are very important dietary factors that can contribute to decreasing the risks for many diseases. It can be said that vegetarians have built increased immunity that protects them from developing disease that can open pathways to other disease.

There is evidence supporting that meat produces carcinogenic chemicals. The general public consumes processed meat such as smoked, barbecued, fried or grilled meat. Processing meat with high temperatures produces PAH and NOC, which are carcinogens that can cause changes in DNA with long term exposure. In 1995, the National Institutes of Health conducted a cohort study to observe the relationship between processed meat and cancer. This was done by self-administered questionnaires about lifestyle habits of people aging from 50-7 followed up for 6 years. The results showed that the increase of 100g/day in the consumption of processed meat increases risk for lung cancer by 16% and colorectal cancer by 20%.

Meat is needed for optimal health which is required for a healthy body. A study performed by the UK National Diet and Nutrition Survey collected data for 2 years of adults aging from 19-64 with omnivorous diets that suggest that meat is responsible for providing 15% magnesium, 36% zinc and 21% iron in their diet. Red meat carries out important roles in the human body such as providing beneficial fatty acids, amino acids and micronutrients that are important from an infant’s development during pregnancy up to adult life. For example, by making proteins in the body that carry out important functions that improve the health of individuals.

There is no direct causation of cancer from meat. In a cohort study between people who were vegetarians and omnivores done to report the incidences of colorectal cancer present between the two groups, found that the rate of having cancer was higher in vegetarians than omnivores. This study had 61 566 participants, included both men and women, and was controlled of confounding effects such as smoking, physical activity, and age.

Meat can help prevent disease that can lead to cancer. Meat provides high levels of protein than vegetables or fruits. Proteins are more satiating compared to other nutrients such as carbohydrates, and study proves that satiety leads to weight loss. Obesity can open so many paths for so many diseases like cancer. The MONICA study done in Europe showed that a percentage of cancers could be avoided by maintaining a low BMI, 40% of endometrial cancer, 10% of breast cancer and 25% of kidney cancers. Proteins increase satiety and that is why eating lean red meat reduces or maintains weight, because less is eaten while the body is full. Red meat is a healthy source of protein and contains important nutrients that benefit health and even weight loss because of its a satiating advantage due to high levels of proteins.

A vegetarian diet is full of phytochemical that protect the body from changes in the DNA. These phytochemicals stop the have been observed to reduce cholesterol and lower BMI. While, meat plays an important role throughout an individual’s life by providing significant amounts of zinc, magnesium and iron. Red meat carries out important roles in the human body that carry out important functions. The consumption of animal protein is associated with the risks of several types of diseases such as cancer. Meat produces carcinogenic chemicals such as PAH and NOC, which are carcinogens that can cause changes in DNA with long term exposure increasing risk for lung cancer and colorectal cancer. A vegetarian diet can provide protective nutrients that can result in lower risks for cancer. Vegetarians can build immunity to developing pathogens because it has been shown that they have decreased saturated fats, cholesterol and increased fiber in their body. On the contrary, some research suggest that meat can help prevent disease that can lead to cancer by providing higher levels of protein than a vegetarian diet. Proteins increase satiety and that is why eating lean red meat reduces or maintains weight, because less is eaten while the body is full. This satiating affect can prevent obesity which can prevent many diseases like cancer.

There have been many studies done providing evidence that being a vegetarian decreases chances of having cancer and provides better overall health. Many research show that meat is beneficial for overall health, but they suggest that it may increase risks of having cancer. When looking at the big picture, the arguments on the No side are less convincing.

After doing research we can see that there are benefits and disadvantages to both diets. However, there’s more research and evidence that show that the debate is leaning towards the yes side. Meat provides healthy nutritional benefits and plays an important role in the body, but the same thing can also be said with plants. Plants provide antioxidants and phytochemicals that can be essential for protecting the body. There’s lots of studies that provide evidence for prevention of disease that leads to cancer by both diets. Some studies show that proteins from meat are associated with cancer, being a vegetarian creates a strong immune system and metabolism that prevents such disease. On the other hand, meat has known to provide high levels of protein that have satiating effects which can help with maintaining body weight. This can prevent obesity and diabetes which potentially come with greater consequences such as cancer. The outcomes of many studies suggest that there is a strong correlation between meat and cancer. Preparing meat requires high temperatures which burn off carcinogenic chemicals. A study provides evidence that as meat consumption is increased, the more likely individuals are to be at risk of cancer. Although, there’s lots of supporting evidence that consuming meat is related to cancer, there is no direct causation. A study done by Oxford shows that vegetarians were more victims of cancer than omnivores, but it is important to note that their sample size was small for such a study. No more evidence was found that also had the same outcomes.

Considering all the arguments, we can conclude that there is a greater amount of evidence on the yes side and that vegetarians could have a decreased chance of cancer.

References

1. American Cancer Society. Facts & Figures 2019: US Cancer Death Rate Has Dropped 27% in 25 Years. American Cancer Society (2019). doi:10.3322/caac.21551.
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Cancer is an acute condition where abnormal cells consecutively divide and spread into neighboring tissues, which endangers cell life if the spread is not controlled. According to World Health Organization (WHO) (2010), cancer causes 13% of all deaths in 2004, accounting for 7.4 million deaths which mostly spread across lung (1.3 million deaths/year), stomach (803,000 deaths), colorectal (639,000 deaths), liver (610,000 deaths) and breast (519,000 deaths) cancer. Moreover, WHO predicts deaths caused by cancer will increase up to 11.5 million deaths in 2030. Through 200 years of cancer research, the first significant increase in overall relative survival rate was in 2005 when the number reached 68% and despite the predecessor existence of herbal medicine, the first tool to combat against cancer was surgery; hence the question of herbal medication legitimacy to combat against cancer arise.

History of Herbal Medication

Herbal medicine, as per its definition by World Health Organization (WHO) is “naturally occurring, plant-derived substances with minimal or no industrial processing that have been used to treat illness within local or regional healing practices”. Contrast to conventional medicine, herbal medicine originally takes the form of crude drugs such as tinctures, teas, poultices, powders, and other herbal formulations. Throughout history, our ancestors seek for food and medicine from plants. Petrovska (2012) outlined a basic timestamp regarding herbal medicine throughout the centuries:

Up Until 200 BCE

The oldest written evidence of medicinal plants usage is a Sumerian clay slab from Napgur, dating back 5000 years ago. Circa 2500 BC, Pen T’Sao, written by Emperor Shen Nung informs of 365 drugs based off roots and grasses, many of which are still used today. The Ebers Papyrus (circa 1550 BC) presents a collection of 800 proscriptions in reference to 700 plant species. Homer’s epics (circa 800 BC), 63 plants were referred. Herodotus (500 BC) used natural ingredients for treatment, and similarly the works of Hippocrates (459-370 BC) contain 300 medicinal plants categorized by physiological action. ‘De Causis Plantarum’ and ‘De Historia Plantarum’, written by Theophrastus (371-287 BC) generated a classification of more than 500 medicinal plants known at the time.

Throughout the Common Era

‘De re medica’, written by Celcus (25 BC-50 AD) referred 250 medicinal plants. Dioscorides (circa 77 AD) dubbed as the ‘father of pharmacognosy’ wrote ‘De Materia Medica’, describing 657 plant-based drugs. Following his work, Pliny the Elder (23 AD-79) wrote approximately 1000 medicinal plants in ‘Historia Naturalis’. Galen (131 AD-200) introduced new plant drugs not mentioned previously. Following these discoveries, monasteries upheld and developed the knowledge well into the Middle Ages; Arabs and Indians trade pharmacotherapy, introducing the knowledge to Europe through trading. Though in the late 19th and early 20th century, the credibility of herbal medicine was questioned which inquires further of this essay’s main problem: is it reliable?

Medicine Development and Cancer

The first arguably credible and successful tool of treating cancer is through surgery and chemotherapy, seeing a 68% rise of survival rate in 2005 (DeVita & Rosenberg, 2012). Referring back to DeVita & Rosenberg (2012), surgery was also the first tool available. In 1809, Ephraim McDowell removed an ovarian tumor without the use of anesthesia and provided evidence that tumor masses could be cured by surgery. The next milestone in cancer treatment began in 1895 through the introduction of radiation, which was further encouraged by the discovery of radium by Pierre and Marie Curie. Following after was Paul Ehrlich by the 20th century, who first made a concerted effort to develop chemicals to cure cancer, where he coined the word chemotherapy. In 1975, Köhler and Milstein developed methods for producing antibodies by fusing cultured myeloma cells with normal B cells from immunized mice, which led to the successful development of therapeutic antibodies for cancer.

Presently, cancer treatment is based on surgery, radiation therapy, chemotherapy and immunotherapy which are based on synthetic drugs.

Herbal Medication and Cancer

Argumentatively, herbal medicine does do its bidding in treating sickness, even one such as cancer. Nie et al. (2015) argues that as cancer is now widely recognized as a systemic humoral disease, the function of herbal medicine is to modulate the whole body in a more holistic way. Cancer is also the development which involves multiple genes and proteins and the complex biology of cancer development requires relatively complex approaches, which is why Hu et al. (2016) believes that the inclusion of herbal medicine is advantageous to combat cancer.

Furthermore, there are numerous studies that exhibits herbal medicine as workable treatments for cancer as source of vitamin and compounds that are able to suppress effect on carcinogenesis and cancer metastasis, increase anti-cancer activities as well as induce apoptosis in cancer treatment. In addition, herbal medicine can also treat cancer alongside chemotherapy while acting as adjuvant treatment as well, and can even help treat cancer chemotherapy-induced side effects. Though one study finds that such accompaniment of herbal-drug medicine combinations results in almost no impression and can even become hazardous.

The Rising Trend of Herbal Medication

Wachtel-Galor & Benxie (2011) mentioned that over the past 100 years, the development and mass production of chemically synthesized drugs have revolutionized health care in most parts of the world. However, large sections of the population in developing countries still rely on traditional practitioners and herbal medicines for their primary care. Wachtel-Galor & Benxie (2011) further explains that 90% of the population in Africa and 70% in India still depend on traditional medicine. Furthermore, in China, traditional medicine accounts for around 40% of all health care delivered and more than 90% of general hospitals in China have units for traditional medicine. Wachtel-Galor & Benxie (2011) further highlights the trend in other countries. For example, in the United States in 2007, about 38% of adults and 12% of children were using some form of traditional medicine. A survey conducted in Hong Kong in 2003 reported that 40% of the subjects surveyed showed marked faith in TCM compared with Western medicine. Herbal medicine, more specifically as complementary medicine (CM) is widely used among cancer patients throughout the world. In Europe, CM was used by between 15% and 73% of cancer patients. In Switzerland, two studies showed a prevalence of CM use of 26.5% and 39%. Swiss mandatory basic health insurance has covered four CM methods since 2012 (traditional Chinese medicine, homeopathy, herbal medicines, and anthroposophical medicine).

Herbal Medication Vs Conventional Medication

Despite such elucidation in support of herbal medicine, the use of herbal medicine by patients with cancer may result in potentially negative effects that can impact the efficacy and safety of conventional anticancer treatment (Ben-Arye et al., 2015). Within the study Ben-Arye et al. (2015) also informs safety-related concerns that are posed by herb-drug interactions: “CYP induction can cause a reduction in bioavailability and subsequently the effectiveness of anti-cancer agents, whereas enzyme inhibition can increase the risk of toxicity, such as that observed with etoposide, paclitaxel, vinblastine and vincristine”. Also previously referred, Alsanad et al. (2016) states that there is a possibility of herbal medication becoming hazardous.

Zhang et al. (2015) discussed in ‘The Complexity of Safety of Herbal Medicine: From Prejudice to Evidence’ a number of causes of adverse events to herbal medicines. Zhang et al. (2015) stated that there are both direct and indirect reason. The former refers to the intrinsic toxicity at normal therapeutic dosage or in overdose whereas the latter refers to the adverse effects associated with herbal medicines, which may result from contamination of products with toxic metals, adulteration, misidentification, or substitution of herbal ingredients, or improperly processed of prepared products. Zhang et al. (2015) also mentions about wrong indication of herbal medicine which leads to inappropriate use of herbal medicines as well as the danger of herb-drug interaction which are associated with nearly 60% of the risk of adverse outcomes — users of medicinal herbs are usually suffering from chronic conditions for which they are likely to take prescribed concomitantly. This, in turn, further increases the potential of herb-drug interaction.

Conclusion

Regarding herbal medicine, there are still many variables unknown and much to discover. Previous research has shown us great advancements in understanding the ability of herbal medicine in treating cancer, how beneficial the natural ingredients are towards the human immunity system. Though said, there are yet to be any cumulative statistical data of the success rate regarding herbal medicine in treating cancer. Herbal medicine may be the answer to alternative and healthier cancer treatment method, but we still need to observe its abilities and reliability in this field.

Why was this study ⁇

As cancer, one of the leading cause of death worldwide, with lung cancer being the second most significant diagnosed cancer in both men and women in US [ref] and the dismal five year survival rate of 16% is in part due to lack of symptoms during early stages and lack of effective screening test until recently [ref]. Hence detection at the earliest may decrease the mortality rate. Tumor staging based on coarse and discrete stratification will determine the patient’s prognosis in lung cancer. Chest X-rays and sputum cytology, the potential screening tests for lung cancer conclusively proven to be of no value. Subsequently number of studies compared computed tomography (CT) with the chest X-ray helps in identifying lung cancer at the earliest. Radiographic medical images, as shows how patient’s inside looks, offers specific information about the changes caused and growth of tumor, helps radiologists in evaluation of prognosis in lung cancer. Later trials have focused on low-dose CT (LDCT) as screening tool. Even-though role of LDCT has established, issues of high false positive rates, radiation risk and cost effectiveness still need to be addressed.

However it seems that the incidence of lung cancer and resulting mortality, fortunately decreasing in both men and women. In the United States in 2011, 115060 and 106070 new cases of lung cancer were seen in men and women respectively. The number of deaths in 2011 was estimated 156940: 84600 in men and 71340 in women. However in men, this represents a continuing decline in incidence and mortality. It occurs by 1980s. Decline is primarily due to the decrease in cigarette consumption, as 80% of lung cancer deaths are caused due to smoking. After long 10 decades by 1990s it seems to be decline in women also. Small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) with the latter category comprising several histological subtypes, including squamous cell cancer, adenocarcinoma and large cell cancer are the major cell types of cancer [ref].

In 1968, Wilson and Junger established the principles of screening for the World Health Organization (WHO) [ref]. The ideal screening test should pose little risk to the patient, should be sensitive for detecting disease at its earliest with least false positives, but acceptable to the patient, and relatively inexpensive to the health system [ref]. The search for the lung cancer screening test were started in 1960s. Early results were promising, but all the tests used had inherent biases. Recent advances in radiomics through applications of artificial intelligence, computer vision and deep learning techniques allows the extraction of numerous quantitative features with minimum pre-processing from radiographic images and solves most the issues and work as ideal screening tool.

The most significant of these biases were lead time, length time, and over diagnosis bias [ref]. The time between early diagnosis with screening and the time in which diagnosis have made without screening is called lead time. The intention of screening is to diagnose a disease at the earliest even before diagnosing the disease without screening. But the early diagnosis cannot guarantee the prolonged life of a person but can affect interpretation of the five-year survival rate [ref]. Length time bias helps in the apparent improvement of survival rate when that improvement is actually due to selective detection of cancers with a less progressive course while missing cancers that have the most rapidly progressive course. It gives the impression that detecting cancers by screening reduces the dangerous effect of cancer, thus reduces the mortality rate [ref]. Over diagnosis is the diagnosis of disease that will never shows any symptoms or death during ordinarily expected lifetime of patient [ref]. It is actually a side effect of screening for early forms of disease. Even-though screening saves lives, sometimes there is a chance to cause harm to the patient’s life due to unnecessary treatments.

Why Computed tomography screening⁇

The interest in CT as a screening tool developed when CT technology evolved and made it possible to get good images in single breath hold time with less radiation exposure [ref]. CT scan images are combination of a series of X-ray images taken from different angles around the body and uses computer processing to create cross-sectional images of bones, blood vessels and soft tissues inside the body. Hence provide more detailed information than plain X-rays do. It is able to detect very small nodules present in the lungs. Conventional CT was not ideal for screening as radiation exposure was 7 milliSieverts (mSv) and scan time was long [21]. Low-dose CT (LDCT) reduced the radiation exposure to 1.6 mSv in the NLST trial [22]. Low-dose CT delivered images with excellent resolution to detect nodules of 0.5 cm to 1 cm size. Low-dose CT is comparable in sensitivity and specificity of lung nodule detection with the conventional CT mode. The first report was from Kaneko et al., who screened 1369 high-risk participants with both LDCT and chest radiography [23]. CT detected 15 cases of peripheral lung cancer, while 11 of these were missed on chest radiography. Of the non-small cell carcinomas identified, 93% were stage I [23]. Sone et al. authored the second report in the literature with 3958 participants screened with both LDCT and X-ray [24]. Only 4 lung cancers were detected by X-ray, whereas 19 were seen on CT, of which 84% were stage I at resection [24].

More malignant and benign nodules were detected with the LDCT scan when compared with X-ray. LDCT detected about 4 times more lung cancers than X-rays do. CT screening for lung cancer detects more cancers and early disease.

Why deep learning technique⁇

Artificial intelligence (AI) with its recent development in digitized data acquisition, machine learning and computing infrastructure gradually changes the medical practice. Applications of AI is expanding to the areas of human expert’s province. The latest advancements in AI is overwhelming, but it leads to two popular concepts machine learning and deep learning. Due to the supremacy both in terms of accuracy and feature extraction, when trained with huge amount of data deep learning technique gains more popularity.

As a way of making machines intelligent, in every sector machine learning has become necessary. Machine learning is a set of algorithms which parse data and learn from the parsed data, then make decisions from the learned data. Deep learning, subset of machine learning, due to its hierarchical nature achieves great power and flexibility in learning data. It is able to represent the world as nested hierarchy of concepts, with each concepts defined it will relate it to more simpler concepts and helps in more abstract representations in terms of less abstract ones [ref].

Using hidden layer architecture, deep learning technique learn categories incrementally, low level features at the lower layer and high level features at higher layer. Deep learning technique requires high-end machines as compared with traditional machine algorithms. GPU has become an integral part to execute deep learning algorithm.

In order to reduce the complexity of the data and convert the data to a form suitable to accept by the algorithms, machine learning techniques needs to identify the domain expert. But the deep learning algorithm eliminates the need of domain expertise and hard core feature extraction, as it learns high-level features from the data in an incremental manner. Deep learning techniques has the ability to solve the problem end to end, but in the case of machine learning, needs the problem statements to break to different parts to solve and their results should combine at the final stage.

Deep learning algorithm takes long time to train due to large number of parameters, but traditional machine learning requires only few seconds to few hours to train, but the scenario gets reversed in the case of testing phase. At test time deep learning algorithm takes very less time to run, but the test time increases on increasing the size of the data. Even-though deep learning algorithm takes time, the accuracy is ideal and it works similar to human brain with excellent performance. Hence deep learning technique have permeated the entire field of medical image analysis.

Dataset

To conduct the study and to develop the system to classify the images as nodule (cancerous) and non-nodule (non-cancerous) images, sample set of nodule and non-nodule images are required. Sample images of nodules and non-nodules are from the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) image collection. This database consists of diagnostic and lung cancer screening thoracic CT scans with annotated lesions [ref]. It contains scan of high risk patients with independent annotations of four experienced radiologists, where a final annotation is made when three of four radiologists independently agree on that lesion. In LIDC-IDRI database lesions are classified in to three categories: nodules > 3mm, nodules = 3mm. In this study nodules > 3mm and non-nodules >= 3mm are considered to classify the image as cancerous or non-cancerous. The total dataset contains scans of 1012 patients, from which 1200 scan images are used for this study. The LIDC-IDRI dataset is publicly available which makes the study reproducible.

All the CT scans available in the LIDC-IDRI dataset are in the MetaImage (mhd/raw) format, which is a test-based tagged file format for medical images [ref]. Each .mhd file is stored with separate .raw file that contains all the voxel data. Each CT scan consists of a cross-sectional slices of the chest. Every cross-sectional slice is a two dimension image of 512 by 512 pixels and are called x and y dimensions respectively. Every slice on the pixel contains Hounsefield Unit (HU) value [ref]. HU values are a measure of radio density and are commonly used in CT scans to express the values in standardized and convenient form. The HU value ranges from [-3024; 3071]. Different substances in a human body produces different HU values, hence helps in the classification of images.

Every CT scan images in the LIDC-IDRI dataset are inspected in a two-phase annotation process by the experienced radiologists [ref]. In the initial phase each radiologists independently marks the lesions to one of the following three categories: nodules > 3mm, nodules = 3mm. in the second phase each radiologists compares their own marks with the anonymized marks of other radiologists. A final annotation is marked when three of the four radiologists agree on a lesion [ref].

Study Analysis

In-order to reduce the mortality rate and increase the survival rate among infected patients of lung cancer, detection of nodules at the earliest is very important. In this study a framework using convolutional neural network (CNN) is developed for the classification of images as cancerous or non-cancerous. The main objective of this study is to reduce the burden on the radiologists in the earlier detection of lung cancer and reduce the processing time of using CNN in the classification of images without the reducing the high accuracy.

Convolutional neural network, class of deep neural network used for the analysis of visual imagery [ref]. As compared with other algorithms of image classification CNNs requires less pre-processing stages. This independence from prior knowledge and human effort in feature design is the major advantage of the use of CNNs. CNNs are commonly used for image classification, the learning process was surprisingly fast and highly accurate [ref]. They are good enough in classifying objects in to fine-grained categories, similar to the human behavior.

CNN, deep learning algorithm, which takes in an input image, then assign importance such as learnable weights and biases to various objects in the image, hence able to differentiate each object in the image one from the other. In CNN architecture the connectivity patterns of neurons is similar to that of the connectivity patterns in human brain [ref]. But the response of individual neurons is restricted only to certain region of the visual field, called as receptive field. Collection of such fields overlap to cover the entire visual area [ref]. The main aim of CNN is to convert image to a form suitable to process, without losing features critical for obtaining good prediction.

The kernels used in the convolutional layers plays very important role in feature extraction and the classification of images. In order to identify the role of kernels in determining the accuracy, pre-trained networks VGG16 and Alexnet is compared. VGG16 with five group of convolutional layers, where each convolutional layer has 3-by-3 kernels is able to give an accuracy of 95.25%, in a processing time of 1703 minutes. It is because of the deep 41 layers of VGG16.

For the analysis 1200 random samples of images from LIDC-IDRI dataset is used, where 695 images were used for training the network and 505 images for validation. Figure 1 shows the accuracy and loss plot of VGG16 network. The confusion matrix shows the true positive, true negative, false positive and false negative which is easy for the analysis. From the analysis true positive gives the correctly classified lung cancer images and false positive gives the misclassification of images, means that the lung cancer is wrongly predicted as non-cancerous image. From the four categories we are able to calculate the sensitivity and specificity of the networks. Confusion matrix is used to identify how many images are depicted correctly and incorrectly as nodule image or non-nodule image i.e. for easy analysis. Figure 2 shows the confusion matrix of VGG16 network.

From the 268 nodule image set VGG16 classifies 250 images as nodule image itself and 18 images as non-nodule images and out of 237 non-nodule image it classifies 234 as non-nodule and 3 as nodule image.

Alexnet with three group of convolutional layers, where each convolutional layer has different sized kernels, 11-by-11 for the first, 5-by-5 for the second and 3-by-3 for the third group of convolution is able to give an accuracy of 86.93%, in a processing time of 44 minutes. It is because of the lesser 25 layers of Alexnet. Figure 3 shows the accuracy and loss plot of Alexnet.

Figure 4 shows the confusion matrix of Alexnet, where from the 268 nodule image set Alexnet classifies 228 images as nodule image itself and 40 images as non-nodule images and out of 237 non-nodule image it classifies 211 as non-nodule and 26 as nodule image.

Based on the inference obtained new image-net is designed with 5 group of convolutions in each group 5-by-5 sized Laplacian of Gaussian (LoG) kernels are used for convolutions. Usually 3-by-3 or 5-by-5 sized kernels are used for convolution as its performance is more accurate than large sized kernels, where there exist chance of losing features. LoG kernels are used, as laplace operator may detect edges as well as noise, desirable to smooth the image first itself by convolution and then suppresses the noise before detecting the edges. Hence as compared with other kernels helps in accurate feature extraction and classification of images. Designed image-net is a simple serial network with minimal processing time than Alexnet and accurate as VGG16. Figure 5 shows the newly designed network architecture.

Convolutional Layer, Pooling Layer, and fully connected layers are used to build the new CNN. Also ReLU layer is used for the activation functions. Raw input image (CT images) is used as the input to the CNN and the hidden CONV layer computes the output of neurons that are connected to the local regions in the input. ReLU layer applies an element wise activation function and leaves the size of volume unchanged. Pooling layers perform max-pooling function to convert different sized features to unique sized features for easy performance. The class scores will be computed by fully connected layers. Hence as shown in figure 6, designed network is able to give an accuracy of 96.44%, in a processing time of 23 minutes.

Figure 7 shows the confusion metrics of designed network, from the 268 nodule image set the designed network classifies 257 images as nodule image itself and 11 images as non-nodule images and out of 237 non-nodule image it classifies 230 as non-nodule and 7 as nodule image.

Table 1 describes the comparison of three networks, Alexnet, VGG16 and the designed network based on their performance in terms of Accuracy, Sensitivity, Specificity and Processing Time. The designed networks performs well compared with pre-trained CNN models both in terms of accuracy and processing time for the classification of image as nodule and non-nodule images.

Conclusion and Future Work

A convolutional neural network based system for the classification of images as cancerous or non-cancerous image using lung CT image is developed. Lung image with different shape and size of cancerous tissue has fed at the input for training the system. The proposed system was able to detect the presence and absence of cancerous cells with 96.44% accuracy, 95.89% sensitivity and 97.04% specificity in 23 minutes in single CPU workstation.

In near future, the system will be trained with large dataset of multi-resolution images to diagnose the type of cancer with its size and shape. The overall accuracy of the system can be improved using false positive reduction and improving the number of hidden neurons with deep network.

To promote their fast multiplication and expansion across the body, cancer cells change their metabolism. Cancer cells prefer to utilize aldohexose for aerobic metabolism rather than delivering it through the organic process glycolysis pathway. Glycolysis produces ATP and pyruvate from glucose. The ribose 5-phosphate and NADPH are then generated in the mitochondria or incorporated into the tricarboxylic acid cycle through the pentose phosphate pathway.

The Warburg Effect

The Warburg effect is a hallmark of cancer that refers to the preference of cancer cells to metabolize glucose anaerobically rather than aerobically, even under normoxia, which contributes to chemoresistance. As a result of this, cancer cells prefer aerobic glycolysis to glucose intake. Even in the presence of oxygen and completely functional mitochondria, cancer cells’ glucose absorption and lactate generation were drastically increased. This well-known metabolic shift supplies cancer cells with the required substrates for proliferation and division, both of which are required for the growth of cancer cells and metastasis.

Otto Warburg, a Nobel laureate and a biochemist, wrote the most significant book ever written on mitochondrial malfunction and its role in cancer. Otto Warburg unwaveringly hypothesized that neoplastic transformation originated because of irreversible damage to mitochondrial respiration based on his series of experiments on cancer cell respiration and metabolism, as well as his in-depth analysis of reported works from other investigators at the time, using an approach similar to what Watson and Crick used in deciphering the DNA double-helical structure. As a result, cancer cells must depend on an inefficient glycolytic ATP production (2 ATPs glucose) rather than respiration, which produces far more ATP glucose (approximately 36 ATPs glucose). When oxygen tension is normal, Warburg claims, normal cells generate the bulk of their energy through mitochondrial respiration. The cytosol provided more than half of the energy to cancer cells, with the mitochondrial respiratory chain providing the rest. The bio energetically inefficient glycolytic dependence on which cancer cells rely for most of their energy generation is not due to a lack of oxygen; it functions even when there is enough. To meet their energy demands, cancer cells must convert to a greater glucose import mode since glycolysis is bio energetically inferior.

Glucose Metabolism and Cancer Cells

Since cancer cells thrive on expansion, aerobic glycolysis permits them to satisfy their ATP and biosynthetic precursor requirements. The purpose of aerobic glycolysis, rather than creating lactate and ATP, is to maintain a high number of glycolytic intermediates in the cells to facilitate anabolic activity. Therefore, it might explain why cancer cells consume more glucose when they are developing.

Environmental growth constraints have little effect on cancer cells. This is done by obtaining the ability to proliferate in the absence of growth signals due to mutations in receptor-associated signaling molecules, as well as being resistant to antigrowth stimuli, such as those mediated through cell-to-cell contacts. Cancer cells are pushed away from blood arteries and, as a result, from oxygen and nourishment supply in the early stages of carcinogenesis by uncontrolled cell growth. The only way for oxygen and glucose to reach the core cells of a non-vascularized tumor is by diffusion through the basement membrane and the peripheral tumor-cell layers.

CSCs (cancer-stem cells) and non-CSCs make up most cancer cells. CSCs are capable of self-renewing and causing cancer. The metabolic plasticity to which CSCs adapt is influenced by the tumor microenvironment. Instead of depending on mitochondrial infrastructure and function, CSCs choose glycolysis and the PPP. The energy produced by glycolytic metabolism allows CSCs to meet their fundamental demands. With the rising energy demands of specialized progenitor cells, the metabolic network grows.

Application of Glucose Metabolism in Treating Cancer Cells

It aids in the treatment of drug resistance and improves the efficacy of current combo therapies. Targeting glucose metabolism in therapy appears insignificant as compared to diagnosis owing to effectiveness or safety concerns. Several medications have proven effectiveness and several targets, as well as some older non-chemotherapeutic treatments with novel elements of tumor glucose metabolism.

Modulating specific targets with altered glycolytic metabolism would lessen therapeutic toxicity when compared to standard cytotoxic treatments. Several investigations have found that combining therapy with vitamin C reduces ATP and NADPH generation by interfering with glycolysis and the TCA cycle. It can destroy cancer cells by raising oxidative stress and inhibiting cancer cell survival and invasion further.

Conclusion

Cancer cells in hypoxic environments have been identified as important and promising targets for cancer therapy because they influence tumor malignancy and resistance to traditional therapies, and they are only found in malignant tumors, not normal tissues. Despite the substantial research on cancer metabolism that has yielded exciting discoveries over the previous few decades, uncertainties remain. Nonetheless, technological advancements are likely to unearth a slew of new features of glucose metabolism in cancer that may be used to improve patient treatment.

Abstract:

Lung cancer is one of the most serious cancers in the world, with the smallest survival rate after the diagnosis. In CT scans, lung nodules appear as dense masses of various shapes and sizes. They may be isolated from or attached to other structures such as blood vessels or the pleura. In this paper a detection of Candidate Nodules (solitary or juxtapleural in a 2D CT slice is achieved using two schemes of segmentation and enhancement algorithms. Convolutional Neural Network (CNN) for deep learning classification is used as a revolutionary image recognition method to distinguish between two types of nodules according to its location (juxtapleural and solitary lung nodules). Our CAD system achieves accuracies of first scheme of segmentation for detecting solitary nodules and juxtapleural nodules by using CNN are ……and ……. respectively. Also achieves accuracies of second scheme of segmentation for detecting solitary nodules and juxtapleural nodules by using CNN are ……and ……. respectively.

Keywords: CT scans, Convolutional Neural Network (CNN), Deep Learning, Computer Aided Detection (CAD).

I. Introduction

Lung cancer is a disease that consists of uncontrollable growth of cell and tissues of the lung which may lead to metastasis that is the infestation of adjacent and nearby tissue and infiltration beyond the lungs. From epithelial cells Carcinomas are derived which are the vast majority of primary lung cancers. Lung cancer, the most usual cause of cancer-imputed death in men and women. An estimated new lung cancer cases 14% for males and 12% females in US in 2017 [1].

The early detection of lung cancer can increase overall 5-year survival rates by extracting the lung nodules. Hence, this diagnosis can improve the effectiveness of treatment. Traditional x-ray and computed tomography (CT scan) are attempted to diagnose lung nodules. Treatment of lung nodules depends on the histological

type of cancer, the stage, and the patient’s status, but overall only 14% of people diagnosed with lung cancer survive five years after the diagnosis[2].

Because of small size of nodule in the lung, it is difficult to distinguish between it and another mass in a 2D slice.

Actually, the search for micro-nodules does not always make sense on single slices: the nodule shape, size and gray tone are very similar to vessels sections. therefore, a segmentation step is very important to distinguish between the small nodules and blood vessels. Hence, the type of nodules according to its location (solitary or juxtapleural) will be easily classified.

In this paper, we propose a two schemes of segmentation and enhancement for nodule emphasis which extracts a 2D candidate lung nodules. A convolutional neural network is used as a deep learning tool for the classification of juxtapleural and solitary lung nodules.

II. Related Work:

To date, many types of research about nodule detection by using CAD system have been developed. It begins with preprocessing and segmentation followed by the classification step.

For instance, Diego et al [3] used a method composed of four processes for lung nodule detection. The first step employed image acquisition and pre-processing. The second stage involved a 2D algorithm to affect every layer of a scan eliminating non-informative structures inside the lungs, and a 3D blob algorithm associated with a connectivity algorithm to select possible nodule shape candidates. The final step utilized a support vector machine for classifying the possible candidates into nodules and non-nodules depending on their features. QingZeng et al [4] proposed to employ, respectively, the convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks. Yang et al [5] described a 3D pulmonary nodule detection scheme utilizing MSCT images. This method segmented the initial nodule candidates first and extracted voxels features based on analysis eigenvalues of Hessian matrix. Then support vector machine (SVM) and decision rule are applied to categorize them into two sorts to remove FPs. Sarah et al [6] used Gaussian smoothing kernel for filtration which helps to reduce noise effects. Next, features such as sphericity, mean and variance of the gray level, elongation and border variation of potential nodules are extracted to classify detected nodules to malignant and benign groups. Fuzzy KNN is employed to classify potential nodules as non-nodule or nodule with different degree of malignancy.

Serhat et al. [7] used Genetic Cellular Neural Networks (G-CNN) for segmentation. Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones.

Finally, fuzzy rule based thresholding was applied and the ROI’s were found. The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm.

Jin et al [8] proposed convolution neural network as a classifier to detect the lung nodules. The system achieved 84.6% of accuracy, 86.7% of specificity and 82.5% of sensitivity.

Thomas et al [9] developed CAD system to detect and localize 60.1% of all the nodules with an average number of 2.1 (1.5%) false positives per slice. In addition, three different types of neural network structures for this CAD system are tested and compared. With 95% confidence we can conclude that deeper neural networks decrease the false positives significantly

Wu et al [10] combined a several common image processing techniques with complex segmentation step, such as connectivity labeling, binarization, mathematical operation and hole-filling.

Ming et al [11] applied the watershed algorithm to estimate the segmentation and then used a region growing method. Xujiong et al [12] proposed a two-step segmentation method for lung extraction. Firstly, a 3-D adaptive fuzzy thresholding technique and secondly applied a 2-D-based post refinement process on the lung contour chain code to obtain a complete lung mask. Michela et al [13] used a dynamic threshold for identification of three different groups corresponding, respectively, to the upper, middle and lower parts of the lung volume. The slices of the lung middle part and a threshold determined empirically for all other slices. In their tests, they applied thresholding with fixed threshold to the first 30 and the last 30 slices of a CT scan. Sasidhar et al [14] applied two steps of segmentation: firstly, extracted lung parenchyma by using a threshold of -420 Hounsfield Unit (HU) as:

Gray Level Value = 1024 + HU

Secondly, extract lung nodules using the threshold of -150 HU.

Qingxiang et al [15] proposed a method implements an active evolution and structure enhancement which can segment blood vessels and detect pulmonary nodules at a high accuracy.

Firstly, he introduced a vessel energy function (VEF) during active evolution to help distinguishing the nodules from vessels. VEF consists of three energy terms, which are gradient term, intensity term, and structure term.

= Fgradient ・ Fintensity・ Fstructure

Secondly, he utilized a radius-variable sphere model to refine the extracted contours. Candidate blood vessel centerline points, denoted as

V = {,,…..,}, are first selected.

Serhat et al [16] segmented the lung regions of the CTs by using Genetic Cellular Neural Networks

(G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image.

Qing et al [17] transformed a number of correlated variables into a smaller number of uncorrelated variables, which are called principal components depended on their cumulative variance proportion that is called principal component analysis (PCA).

III. Materials And Methods:

A. Dataset:

A 14 digital CT consisting of 2991 2D slices which contains 172 nodules (100 solitary nodule and 72 juxtapleural nodule) are collected with approval from Cornell University [18] each abnormal image contains a tumor with equivalent diameters of lung nodules ranging from 7.78 mm to 22.48 mm. The in-slice (x, y) resolution is 0.703×0.703 mm and the CT slice thickness is 1.25 mm in DICOM format and has 512×512 pixels.

B. Implementation:

• Software:
  • Windows 10 Pro 64-bit
  • Matlab R2018b toolboxes
• Hardware:
  • Processor: Intel(R) Core™ i7, 2.70Ghz
  • RAM: 8 GB
  • Display Adapter: NVIDIA NVS 4200M

C. Segmentation and Enhancement for Nodule emphasis:

Lung segmentation is a prerequisite step for developing an automated computer-aided diagnosis system for CT scans of thorax that can lead to the early diagnosis of lung cancer as it is a separating task of the lung region from other anatomical portion of the chest CT image.

Inhomogeneity in the lung region is a very challenging problem as there is similar densities such as veins, arteries, bronchioles. A wealth of known publications has addressed the segmentation of lung regions from CT images and chest radiographs.

Here this phase is meant to remove the unwanted parts and to enhance the visibility of extracted pulmonary nodule. The preprocessing component reduces noise and artifacts in the lung image slices. refers to tasks necessary for enhancing the quality of displayed image by rectifying distortions due to media decay or motion artifacts.

In this paper, a two schemes of segmentation algorithm are proposed as follows:

a. Scheme I (Thresholding + Morphology):

It consists of four main steps as shown in fig.1

Figure 1: Scheme I of segmentation

(Thresholding + Morphology)

· Thresholding:

Selects thresholds for the input DICOM image, by calculating the optimum threshold separating two classes (foreground as 1 and background as 0). It reduces grey level images into binary images [19] [20] [21], If g (x, y) could be a threshold version of f (x, y) at some global threshold T, it is often outlined as g (x, y) = 1 if f (x, y) ≥ T

= 0 otherwise

The thresholding equation is outlined as:

T = M [x, y, p (x, y), f (x, y)]

during this equation, T refers to the threshold; f (x, y) is the gray value of point (x, y) and p (x, y) denotes some native property of {the point | the purpose} such as the average gray value of the neighborhood centered on point (x, y) [22].

the resulted image of the Thresholding step shown in fig.1 (B)

· Clearing lung border:

“Imclearborder” function Suppresses structures that are lighter than their surroundings and that are connected to the image border [23] [14], as shown in fig.1 (C).

· Closure operation [21]:

Morphological operation (Erosion and Dilation) was applied for smoothing the outline of the segmented lung. The morphological close operation is a dilation followed by an erosion, using the same structuring element (5×5 kernel) for both operations. This operation enhances the lung borders and fills the gaps which can cause deficiencies in the phase of border detection.

Fig.1 (D) showed the resulted image of the closing operation step.

· Superimposing [14] [21] and 2D candidate

Representing the lung masses which are superimposed on the original image as shown in fig.1 (E).

(A)

(B)

(E)

(D)

Figure 2: Results of scheme I of segmentation (Thresholding + Morphology):

A. original CT image, B. Thresholding, C. Clearing lung border, D. Closure operation, E. Superimposing and 2D candidate nodule

b. Scheme II (Bounding box + Maximum intensity projection):

It consists of the following image processing methods:

· Bilateral filter [24] [25]:

Is a non-linear and edge preserving filter. It replaces the pixel values of the image with a weighted average of similar and nearby pixel values. It filters the image using range and domain filter. Bilateral filter is defined as

( (1)

(C) where the normalization term

(2) ensures that the filter preserves image energy and is the original input image to be filtered;

{displaystyle x} is the original input image to be filtered; are the coordinates of the current pixel to be filtered;

{displaystyle Omega }Ω is the window centered in ; {displaystyle x}

{displaystyle f_{r}} is the range kernel for smoothing differences in intensities;

{displaystyle g_{s}} is the spatial kernel for smoothing differences in coordinates (this function can be a Gaussian function). The resulted image of bilateral filter shown in fig. 3(A)

· Thresholding gray-level transformation function [26]:

Applied by using bit plane slicing method, it maps all gray levels of the image from 0 to 255, by converting the value of pixels that ranging from 0 to 127 to one level (e.g., 0) and maps all levels above 127 to another level (e.g., 1) that resulting in transformation of the gray image into binary image as shown in fig. 3 (B).

· Bounding box:

Measuring the image properties by initializing small rectangle (bounding box) which containing all the regions, specified as a 1 [27] [28]. Thresholding to isolate the two lungs easily is used by converting all zeros pixels to ones and the opposite. remove border artifact by clearing lung border, erosion and dilation are applied also [29]. Superimposing is done by multiplication of original image by the last image resulted from the dilation step.

· Maximum intensity projection (MIP) [30]:

evaluates the projecting of the voxel with the highest attenuation value on every slice throughout the volume onto each XY coordinate or a 2D image, only the pixel with the highest Hounsfield number along the Z-axis is projected so that in a single bidimensional image all dense structures (nodules) in a given volume as illustrated in next figure [31].

Figure 3: MIP principle

Also a morphological close operation is done after MIP using a structuring element (size 2 pixels), so that the detection of lung nodule become easily. Fig. 4 shows the segmentation results of scheme II.

D. Convolutional neural network for deep learning classification:

Convolutional neural networks (CNN) are a successful tool for deep learning classification [30] [32], and developed to suit image recognition as it is a multilayer neural network which consists of one or more convolution layers followed by one or more fully connected layers.

Our convolutional neural network architecture is consisting of convolution layer, max pooling layer, fully connected layer and softmax layer as shown in fig.4

In the input image layer, we specify the input image size as 512×512 and the channel size is 1. the filter size in the convolution layer is [5,5] and number of filters is 20. The max pooling layer returns the maximum values of rectangular regions of inputs, in our work the size of the rectangular region is [2,2]. The fully connected layer combines all of the features (local information) that learned by all previous layers across the image to classify these images. As the output parameter in this layer is equal to the number of classes in the target data, the output size will be 2 classes (solitary and juxtapleural nodules).

Solitary nodule

Juxta pleural nodule

Figure 5: The convolutional neural network CNN architecture.

(A)

(B)

(F)

(E)

(I)

Figure 3: Results of scheme II of segmentation (MIP + Bounding box):

A. original CT image, B. bilateral filtering, C. Bit plane slicing, D. bounding box, E. thresholding, F. clearing lung border, G. Filling holes, H. Superimposing and I. candidate nodule with MIP

(D)

(C)

(H)

(G)

IV. Results And Discussion:

In this paper we propose a Computer Aided Detection (CADe) system to detect candidate nodules either solitary or juxtapleural nodules in a 2D CT slice regarding to its location. Two Segmentation and enhancement schemes (Thresholding + Morphology and bounding box + MIP) are achieved and their results is shown in figure 2 and figure 3 respectively. Convolutional Neural Network (CNN) is achieved for deep learning classification as a revolutionary image recognition method to classify between juxtapleural and solitary lung nodules. The results of sensitivity, specificity and accuracy of CNN for deep learning classification distinguishing between solitary and juxtapleural nodules for the two schemes of segmentation is shown in table 1 and table 2. Our CAD system achieves ….. , ….. and ….. as a sensitivity, specificity and accuracy of CNN respectively to classify the solitary nodules and ……, ……. And …… as sensitivity, specificity and accuracy of CNN respectively to classify the juxtapleural nodules when using scheme 1 of segmentation. It achieves also ….. , ….. and ….. as sensitivity, specificity and accuracy of CNN respectively to classify the solitary nodules and ……, ……. And …… as sensitivity, specificity and accuracy of CNN respectively to classify the juxtapleural nodules when using scheme 2 of segmentation. Results provided that the CNN accuracy of solitary nodules detection

Table1: Results of CNN for deep learning classification with scheme 1 of segmentation (Thresholding + Morphology) distinguishing between Solitary and juxtapleural nodules

Nodule type

Sensitivity%

Specificity%

Accuracy%

Solitary

97

Juxtapleural

96

Table2: Results of CNN for deep learning classification with (Bounding box + MIP) distinguishing between Solitary and juxtapleural nodules

Nodule type

Sensitivity%

Specificity%

Accuracy%

Solitary

95

Juxtapleural

93.3

Based on literature research shown in table 3 a sensitivity, accuracy and database are observed. However, systems showed promising results, for example, regarding to the parameters of sensitivity, accuracy and database, stood out the systems of Qaisar et al. [37], Mizuho et al. [38] and Patrice et al. [39]. The first tested his method with 250 different nodules and had an accuracy of 88%. The second validated his system with 665 nodules and obtained accuracy of 68%. The third tested his method with 2635 nodules achieved an accuracy of 88.28% and Sensitivity 83.82%. However, validation of the systems was not tested with a

broad range of nodules types; it shows promising results. Comparing to our system, Patrice et al. [39] achieved 88.28% and Sensitivity 83.82% when using Deep Convolutional Neural Network (DCNN) for nodule detection knowing that the nodule type not informed and our system showed best result when using scheme 1 of segmentation achieving 97% accuracy for detection of solitary nodules when using CNN for deep learning classification with sensitivity …………… and 97% for detection of juxtapleural nodules when using CNN for deep learning classification with sensitivity ……………

Study

Year

Database

Nodule type

Accuracy%

Qaisar et al. [37]

2017

250

Ground glass opacity

88%

Mizuho et al. [38]

2018

665

NI

68%

Patrice et al. [39]

2018

2635

NI

Accuracy 88.28%

Sensitivity 83.82%

Table3: Comparison with related work of CNN for deep learning classification

(NI: Not Informed)

V. Conclusion

A Computer-Aided Detection (CADe) system to detect candidate nodules either solitary or juxtapleural nodules in a 2D CT slice regarding to its location is proposed by applying Two segmentation and enhancement schemes (Thresholding + Morphology and bounding box + MIP) on a database consisting of 14 digital CT consisting of 2991 2D slices which contain 172 nodules (100 solitary nodule and 72 juxtapleural nodule). Convolutional Neural Network (CNN) for deep learning classification is applied to classify between juxtapleural and solitary lung nodules with equivalent diameters of lung nodules (solitary and juxtapleural) ranging from 7.78 mm to 22.48 mm.

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Abstract—

Image processing techniques are widely used in several medical problems for image enhancement in the detection phase to support early medical treatment. In this research, we aim to improve quality and accuracy of early detection of lung cancer through a combination of image processing techniques and machine learning. The Cancer Imaging Archive (TCIA) dataset has been used for training and testing purpose where DICOM is the primary format used for image storage. Classification is done using SVM (Support Vector Machine) classifier which identifies whether the CT image is cancerous and non-cancerous. Before training the classifier, we are performing image processing techniques on CT images such as converting the image into HU (Hounsfield Unit) scale to get the binary image of lungs followed by nodule segmentation where nodules are detected within the lungs. Further, features are extracted from CT images using GLCM (Gray Level Co-Occurrence Matrix) .The enhanced image is then given to the classifier to provide accurate results. As an enhancement, we have also performed Early Stage Detection for reducing the growing cancer burden. MATLAB image processing toolbox based has been used for the implementation on the CT scan images.

Keywords—Nodules Detection, Image Processing, Classification, Machine Learning

Introduction

Cancer is one of the most dangerous diseases that causes death. Lung cancer has become one of the most common causes in both men and women. A large number of people die every year due to lung cancer. The disease has different stages whereby it starts from the small tissue tissues and spreads throughout the different parts of the body. According to the American Cancer Society [9] the cases of lung cancer increases very rapidly and almost 14% newly diagnosed cancers are lung cancer and also the main cause of cancer death worldwide. The previous study of diagnosis showed that most of the lung cancer patients belong to the age of 60 years. The process of early detection of cancer plays an important role to prevent cancer cells from multiplying and spreading. Although CT scan imaging is best imaging technique that are reliable for lung cancer diagnosis because it can disclose every suspected and unsuspected lung cancer nodule in the medical field. However, the variance of intensity in CT images and anatomical structure misjudgments by doctors and radiologists might cause difficulty to interpret and identify cancer from CT scan images. Therefore computer-aided diagnosis can be helpful to assist radiologists and doctors to identify cancerous cells accurately.

Previous research have been conducted for analyzing lung cancer, where the detection of lung cancer with image processing techniques provides good results and accuracy. In this study, we proposed different methods in analyzing lung cancer using image processing techniques along with machine learning algorithm for the classification of the CT scan images whether it is cancerous or non-cancerous. The main aim of this research is to provide accurate results by the means of the machine learning algorithm.

Literature Review

Several researchers have proposed and implemented the detection of lung cancer using different approaches of image processing and machine learning. According to our survey, Aggarwal, Furquan and Kalra [2] proposed a model that provides classification between nodules and normal lung anatomy structure. The method extracts geometrical, statistical and gray level characteristics. LDA is used as a classifier and optimal thresholding for segmentation. The system has 84% accuracy, 97.14% sensitivity and 53.33%specificty. Although the system detects the cancer nodule, its accuracy is still unacceptable. No any machine learning techniques has been used to classify and simple segmentation techniques is used. Ignatius and joseph [6] developed a system using watershed segmentation. In preprocessing, it uses a Gabor filter to enhance the image quality. The accuracy of the proposed is 90% which is comparatively higher than the model with segmentation using neural fuzzy model and region growing method. As a limitation, it does not classify cancer as benign and malignant. Gonzalez and Ponomaryvo [7] proposed a system that classifies lung cancer as benign or malignant. The system uses the priori information and HU to calculate the Region of Interest. Shape features like area, eccentricity, fractal dimension and textual features are extracted to train and classify the support vector machine to identify whether the nodule is cancerous our non-cancerous. Gonzalez and Ponomaryvo is the current best model. However, it does not make any effort for early detection of lung cancer which will improve the overall chances of survival rate for the patient and thus leads to one of the limitations of this study. Also no such filtering techniques are used which would remove the noise and improve the accuracy of the system.

Proposed System

Figure 1 shows a general description of lung cancer detection system that contains basic stages:

0. Collecting Dataset

Image Processing(Median Filter)

Converting Image to HU scale

Segmentation(Lungs and Nodules)

Feature Extraction

Classification

Stage Detection

CT scan lung images

Image Denoising using Median Filter

Converting Image to HU Scale

Segmentation (Lungs and Nodules)

SVM Classification

Benign or Malignant

Fig-1:Flowchart of Proposed System

A. Collecting Dataset

The Lung Cancer Image Dataset [8] has been chosen from TCIA(The Cancer Imaging Archive) which is an online CT scan image dataset publicly available for researchers in the field of digital image processing. Images in the dataset are in the DICOM format with a size 512*512 pixel. In TCIA dataset, we have used 20% data for testing and the remaining 80% of the dataset is used for training the SVM classifier.

B. Image Processing

Smoothing is an image processing technique used in order to reduce noise in an image to produce clearer image. Most of the techniques are based on low pass linear filters. It is mostly based on the averaging technique of the input image. To perform a smoothing operation we will apply a filter to our image. The most common type of filter is the median filter which is used in our proposed system.

C. Converting image to HU Scale

Hounsfield Unit is the unit of measurement used in CT scan images which is a measure of radiodensity. CT scanners are carefully calibrated to accurately measure this.HU scale is a linear transformation of the original linear attenuation coefficient measurement into one in which the radiodensity of distilled water at standard pressure and temperature (STP) is defined as zero HU, while the radiodensity of air at STP is defined as -1000 HU. The HU value is therefore given by

Fig 2. Hounsfield Formula

TABLE 1

Hounsfield Unit

Substance

HU

Air

-1000

Lung

-500

Fat

-100 to -50

Water

CSF

15

Kidney

30

Blood

+30 to +45

Muscle

+10 to +40

Grey matter

+37 to +45

White matter

+20 to +30

Liver

+40 to +60

Soft Tissue, Contrast

+100 to +200

Bone

+700(cancerous bone) to +3000(cortical bone)

By default however, the returned values are not in this unit. But this can be fixed. Some scanners have cylindrical scanning bounds, but the output image is square. The pixels that fall outside of these bounds get the fixed value -2000. The first step is setting these values to 0, which currently corresponds to air. Next we convert our image back to HU units, by multiplying with the rescale slope and adding the intercept.

CT_IMAGE_HU=(CT_IMAGE*RESCALE_SLOPE)+INTERCEPT

D. Segmentation

Segmentation is a process that splits the image into its constituent regions or objects. Segmentation is usually used to trace objects and borders such as lines, curves, etc. in images. The main objective of segmentation is to simplify and change the representation of the image into something that is more significant and easier to examine.

In our proposed system, Segmentation involves two parts which are Lung Segmentation and Nodules Segmentation.

Lung Segmentation is implemented through HU Scale where once the image is converted into HU Scale, we get segmented binary image of lungs. As a precautionary measure, we have also used active contour method to trace the lungs part properly so as to improve the accuracy. Further in Nodules Segmentation, nodules are detected within the binary image by using Morphological Operations such as dilation and erosion. Dilation and Erosion are often used in combination for specific image preprocessing applications such as filing holes or removing small objects. As a result of which, we get a segmented nodule image given to feature extraction to perform textual analysis on the image.

Fig 3. Original Image

Fig 4. Lungs Segmented Image

[image: D:FLung Cancer Detection with stagesModule 2DICOMSTraining SetMask2.bmp]

Fig 5. Nodule Segmented Image

E. Feature Extraction

Feature Extraction stage is a crucial stage for the Computer-Aided Diagnosis (CAD) system. It uses different methods and algorithms for feature extraction from the segmented image. The extracted image can be classified as either cancerous or non-cancerous using texture properties. We have used HARALICK GLCM (Gray Level Co-Occurrence Matrix) for texture feature extraction from CT scan images. The GCLM is a tabulation of how often different combinations of pixel brightness values (gray levels) occur in an image. Firstly we create a gray-level co-occurrence matrix from images in MATLAB.

Some of the features are extracted using this method are:

• Contrast
• Entropy
• Energy
• Homogeneity
• Correlation

Then these features are used for tumor classification.

F. Classification

This stage classifies the detected nodule as malignant of benign. Support Vector Machine (SVM) is used as a classifier. SVM is supervised machine learning algorithm which defines the function that classifies data into two classes. In our proposed system, we have defined two classes as cancerous or non-cancerous. SVM is a binary classification method that takes as input labeled data from two classes and outputs a model file for classifying unknown or known data into one of two classes.

Fig 5. SVM Classifier

Training an SVM involves feeding known data to the SVM along with previously known decision values, thus forming a training set. It is from the training set that an SVM gets its intelligence to classify unknown data.

TABLE 2

TUMOR CLASSIFICATION USING

SVM

Image

Output Value

Classification

Image_1

Non-Cancerous

Image_2

1

Cancerous

Image_3

Non-Cancerous

Image_4

1

Cancerous

Image_5

1

Cancerous

Image_6

1

Cancerous

Image_7

1

Cancerous

Image_8

1

Cancerous

Image_9

Non-Cancerous

Image_10

1

Cancerous

G. Stage Detection

Early Detection represents one of the most promising approaches for reducing the growing cancer burden. The purpose of early detection is that it will identify cancer while still localized and curable, preventing not only mortality but also reducing costs.

Future work

In the future scope, the accuracy of the system can be improved if training is performed by using a very large image database. Similarly, we have planned to use MRI scan images to detect different types of lung cancer or other cancers using the same approach.

V. Conclusion

The current best system does not classify into different stages of cancer such as stage I, II, III or IV which results into non-satisfactory results. Therefore new system is proposed. The proposed system is successfully able to classify both benign and malignant tumors more accurately. It is also possible for early-stage detection of lung cancer by using this system, thereby overcoming the limitations of the existing system. SVM can handle better complex classifications as compared to KNN classifiers.

1. References

1. Xiuhua,G., Tao, S., & Zhigang, L.(2011) “Prediction Models for Malignant Pulmonary Nodules Based-on Texture Features of CT Image.” In Theory and Applications of CT Imaging and Analysis. DOI: 10.5772/14766. https://www.intechopen.com/download/pdf/14768
2. Aggarwal, T., Furqan, A., & Kalra, K. (2015) “Feature extraction and LDA based classification of lung nodules in chest CT scan images.” 2015 International Conference On Advances In Computing, Communications And Informatics (ICACCI), DOI: 10.1109/ICACCI.2015.7275773. https://ieeexplore.ieee.org/abstract/document/7275773.
3. Jin, X., Zhang, Y., & Jin, Q. (2016) “Pulmonary Nodule Detection Based on CT Images Using Convolution Neural Network.” 2016 9Th International Symposium On Computational Intelligence And Design (ISCID). DOI:10.1109/ISCID.2016.1053. https://ieeexplore.ieee.org/abstract/document/7830327.
4. Sangamithraa, P., & Govindaraju, S. (2016) “Lung tumour detection and classification using EK-Mean clustering.” 2016 International Conference On Wireless Communications, Signal Processing And Networking (Wispnet). DOI:10.1109/WiSPNET.2016.7566533. https://ieeexplore.ieee.org/abstract/document/7566533/authors#authors
5. Roy, T., Sirohi, N., & Patle, A. (2015) “Classification of lung image and nodule detection using fuzzy inference system.” International Conference On Computing, Communication & Automation. DOI: 10.1109/CCAA.2015.7148560. https://www.sciencedirect.com/science/article/pii/S1877050917327801
6. Ignatious, S., & Joseph, R. (2015) “Computer-aided lung cancer detection system.” 2015 Global Conference On Communication Technologies (GCCT), DOI: 10.1109/GCCT.2015.7342723. https://ieeexplore.ieee.org/abstract/document/7342723
7. Rendon-Gonzalez, E., & Ponomaryov, V. (2016) “Automatic Lung nodule segmentation and classification in CT images based on SVM.” 2016 9Th International Kharkiv Symposium On Physics And Engineering Of Microwaves, Millimeter And Submillimeter Waves (MSMW). DOI: 10.1109/MSMW.2016.7537995. https://ieeexplore.ieee.org/document/7537995/authors#authors
8. The Cancer Imaging Archive(TCIA) Dataset https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics.
9. American Cancer Society. Costs of Cancer, 2002 [online], (cited 25 Jan 2003), http://www.cancer.org/docroot/MIT/content/MIT_3_2X_Costs_of_Cancer.asp
10. Sruthi Ignatious, Robin Joseph, Jisha John Dept. of Computer Science and Engineering, Mar Baselios college of Engineering and Technology Trivandrum, Dr. Anil Prahladan Dept. of Imageology, Regional Cancer Centre Trivandrum, ” Computer-Aided Lung Cancer Detection and Tumor Staging in CT image using Image Processing” India International Journal of Computer Applications (0975 – 8887) Volume 128 – No.7, October 2015 https://pdfs.semanticscholar.org/e8bf/3d6b4d897fd3c9e13feed03636d3ee0f1845.pdf

Many diseases that affect a person are life-threatening. Cancer is one of those fatal diseases. Cancer is basically a general name that is given to a whole group of diseases that have one thing in common – abnormal cell growth. The causes of this disease cannot be traced to a single factor because many factors contribute to its birth. Its effects are explained by the rapid spread of abnormal cells and the way they pile up upon each other. This paper attempts to discuss the causes and effects of cancer.

Firstly, some things have been given the name of carcinogens. Carcinogens refer to cancer-causing substances. Tobacco is an obvious example in this regard. Smoking is one of the most immediate causes of lung cancer. Statistics indicate that lung cancer has been the cause of the greatest number of deaths due to cancer in the USA for both men and women. Research has also found that it is one of the most avoidable forms of cancer death also.

The second important cause of cancer is the genetic makeup. If faulty genes are carried by a person from his birth, the chances of getting inflicted by cancer are raised to a high level. Breast cancer is usually caused due to inherited gene mutation. Colorectal cancer is also usually inherited. Therefore, genetic counseling is usually suggested for those who have a very strong history of cancer in their family. They should seek knowledge about their genetic makeup.

Another important cause of cancer is the everyday environment that a person is exposed to in routine. Different types of radiation, tobacco smoke, ultraviolet light, and some cosmetics products, all contribute towards the cause of cancer. Substances like radon, lead, and arsenic are usually found in homes and can lead to cancer. Furthermore, asbestos is also a contributing factor. Living near cellular phone towers and being exposed to the radio frequency waves used by cell phones all the time are equally dangerous.

One of the effects of cancer is that due to the local overgrowth of abnormal cells a swollen mass is produced. This swollen mass, also known as a tumor, can then become a source of extreme pain. Moreover, metastatic tumors can invade new parts of the body and spread there. For example, cancer spreads from the breast to the bones and then causes fractures. Hence, its fast-spreading ability and the ability to leave the original site and infect some other part of the body is the most alarming aspect.

Another common consequence of cancer is weight loss. Statistics indicate that around eighty percent of people with cancer suffer weight loss. Lastly, cancerous cells also weaken the immune system, and hence the body becomes vulnerable to several kinds of viruses and infections that the immune system would have otherwise been able to recognize and destroy. This explains why pneumonia usually bedevils patients in the last stages of cancer.

Summing up, cancer is a multifactorial disease. Ranging from general exposure to radio frequency waves to genetic makeup, this disease is caused by a variety of factors. Its effects are extremely painful and eventually life-threatening.