Category: Breast Cancer

Meg, age 50, currently on post-menopausal, was diagnosed with Invasive Ductal Carcinoma (IDC), also known as one of the most common forms of breast cancer (“Invasive Ductal Carcinoma,” n.d.). The first sign of cancer was detected through a mammogram, followed by a physical examination and later confirmed by an ultrasound and biopsy. Three tumours/ lymph nodes were discovered. After surgery, Meg underwent treatments of chemotherapy followed by Taxotere, radiation therapy and hormone therapy.

Side effects of treatments

Chemotherapy & Radiation therapy

The side effects that Meg encountered after chemotherapy and radiation therapy was cognitive changes and fatigue, where she described her problems with concentration; having difficulties retaining information when reading a book, and driving; where she could not maintain proper attention and finding words.

Fatigue, in this case, is not fatigue that most people would experience in their everyday functioning. Cancer-related fatigue (CRF) is a more extreme form of fatigue where it is not relieved by sleep or rest, where up to 80% and 90% of patients treated with chemotherapy and radiation, respectively, reported experiencing fatigue. Moreover, as Meg has stated her difficulties with driving and concentration, a study has revealed the impact of CRF on patients who have gone through chemotherapy where 88% of patients reported it to be interfering with their daily routines, and 91% felt that it hindered them from having a normal life. In addition, approximately 50% and 60% reported to find concentrating on things and social activities, respectively, somewhat more difficult (Hofman, Ryan, Figueroa-Moseley, Jean-Pierre & Morrow, 2007). Hence, these outcomes of CRF that Meg is experiencing are not uncommon for individuals who have received treatments of chemotherapy and radiation.

Closely related to fatigue, the side effect of Cancer Related Cognitive Changes (CRCC) from standard to high dose of chemotherapy is also a very common change for 48%- 95% of breast cancer patients (Ahles & Saykin, 2002). Changes such as the ability to concentrate, maintain focus, fatigue and organising problems, as Meg has also experienced with her reading, driving, and finding words. As these changes require more effort to perform everyday functions, it will result in fatigue and vice versa.

Hormone therapy

After a year of hormone therapy instead of the suggested ten years, being on Famara, Meg experienced joint pain and weight gain. These are the side effects of menopause although Meg was post-menopausal.

Impact of distressful symptoms on life roles

Lymphoedema

Symptom distress may generate physical dysfunctional, cognitive and emotional disturbances, particularly typical symptoms with upper-arm problems such as lymphoedema. Lymphoedema is a continual swelling where there are build-ups of protein-rich fluids and has an impact on most women’s lives as well as Meg’s. Impacts such as discomfort in mobility, where the swelling in Meg’s right upper limb has limited the movement in her right shoulder. Other effects include the incapability to carry heavy items such as groceries, exercises that may lead to further symptoms, troubles of appearance in the fit of clothing, and unable to manage occupational responsibilities as returning to work was also a concern for Meg (Armer, 2005).

Body image disturbance

Body image disturbance is another distressful symptom that has also impacted Meg’s life, from trying to cope with the loss of the breast to significant weight gain. As the breast connotes a woman’s sense of femininity and identity, deformities in its appearance will generate negative impacts on a woman’s Quality of Life (QoL) including their physical, cognitive, emotional and socio-economic conditions. Results from a study of 577 women studied by Al-Ghazal, Fallowfield, and Blamey in 2000, revealed the effect of a woman’s psychological well-being and satisfaction are from invasive surgery to the breast, which ultimately may result in the removal of breast (Al-Ghazal et al, as cited in Helms, O’Hea, & Corso, 2008). This will heighten Meg’s emotional stress about her body image, which may lead to anxiety symptoms that will further increase her concern about the recurrence of cancer.

Weight gain

In addition, the range of 50% – 96% of women have reported to gain weight after the diagnosis of breast cancer, with 20% of the range noted to gain over 10 kg (Goodwin et al., as cited in Helms et al., 2008). Like body image disturbance, weight gain can also negatively impact Meg’s self-esteem. Although not specified in the case study of the amount that Meg has gained besides it being “substantial”, weight gain may be another factor besides painful joints and feet, that contributes to her decrease in physical activities.

As a result, all of these can also impact Meg’s QoL and well-being as a whole, such as life satisfaction, family situations, physical health, and economic status. Although Meg has some social support from friends, she may not be satisfied with her life, dealing with the sense of loneliness as she lives alone without any familial support, her father being in a nursing home that is four hours away. Furthermore, despite having support from her employer, Meg’s cognitive and fatigue-related issues combined with painful joints in the neck, back, knees and feet, additionally with the pressure to contribute to her job as a lawyer, all may decrease QoL due to distressed.

Suggestions / Interventions

Exercise

As Meg has reduced physical activities because finding walking difficult due to painful feet, and experiences breathlessness on exertion, I would recommend her to slowly exercise more, starting with the easiest, for example, yoga. As potential effective interventions for fatigue and cognitive may include Non-Pharmacological Interventions such as exercise. Studies have shown patients receiving chemotherapy or radiation have participated in exercise programs such as aerobics, and revealed to have reduced levels of fatigue (MacVicar & Winningham; Mock et al., as cited in Pinto & Maruyama, 1999). Other interventions include rest, sleep, and psychosocial interventions that involve support groups, individual counselling, coping strategies, and stress management (Mock, 2004). Therefore, I would recommend Meg to take on these interventions if she finds it difficult to express her concerns to her loved ones.

Dietary change

Ligibel, J.A., Basen-Engquist, K., & Bea, J.W. (2019) compiled together a list of studies on different interventions and how breast cancer survivors can manage their activities after the diagnosis. Studies were associated with dietary change, where diets included consumption of Mediterranean and macrobiotic diet, increase intake of vegetables, fruits, fiber and decrease in fat intake. Most studies have shown effective results in declining the risk of the cancer recurring. Hence, I would suggest Meg to take on a healthier diet and maintain a healthy weight at home as it would lower her anxiety levels for the recurrence of cancer.

Home-based multidimensional programmes

Furthermore, other effective recommendations are home-based multidimensional survivorship programmes as aims to emphasise in increasing women’s QoL and the transition to what is considered a ‘normal’ life. The programme is most effective when three factors work in conjunction with one another. Factors include Education, such as advice and information on self-management. Physical comprises of exercise and resistance training, and Psychological where cognitive therapies and counselling are involved, as mentioned above. However, these programs have only shown short-term benefits in reducing anxiety and fatigue at home (Cheng, Lim, Koh, & Tam, 2017).

Overall case reflection

Overall, a majority of cancer patients and survivors, including Meg, experiences a diverse range of challenges from diagnosis, throughout treatment and post-treatment. As a result, these treatments have led to physical changes, where Meg had issues regarding her appearance with the loss of the breast, psychological distress, depression and financial challenges. As Meg still received income protection insurance payments upon all of her sick leave with the support from her employer, financial matters were not as big of a concern for her, nor did she experienced any depression. However, with no emotional and social support except some coming from her friends, the issue of loneliness may be a risk of developing depression.

Moreover, as early detection may save lives, women ages 40-74 who have completed screening every one to two years have reduced their mortality by 40% (Seely & Alhassan, 2018). Although it is not Meg’s fault that she had her first mammogram when she turned 50, but if she were to be screened at an earlier age, it may possibly help reduce the risk of breast cancer or its effects, along with a healthy diet.

To conclude, breast cancer is still one of the most prevalent cancer in women worldwide. Issues such as fatigue and cognitive changes are issues that need to be appropriately managed by health professionals, and should not be avoided.

Breast cancer is a form of cancer most common in women, but can also affect men, were cells in the breast tissue become mutated and multiply. This most commonly occurs in cells around the milk ducts and glands. In the first stages there are usually no symptoms but, as cancer develops the size or shape may change and a lump could form. Some forms of this cancer are seen as hereditary due to mutated genes being passed on from the parents being linked to a risk of this type of cancer (1). But the risk can also increase due to diet, alcohol intake and age (2).

Breast Cancer can be hereditary

Breast cancer is the most common type of malignant tumour in women across the world. Around 5%-10% of these case is due to mutations in autosomal dominant genes (these are genes that are unrelated to sex chromosomes and so can be passed from either parent, only one parent is needed to pass on the mutated gene for the child to be affected.) BRCA1 and BRCA2 genes have links to cancer, as well as TP53 which results in triple negative breast cancer, which is more aggressive and challenging to treat. There is also a higher chance of it to recur or spread causing more complication in other organs. These genes have a high rate of expression but most breast cancer cases are related to a gene of low expression such as CHEK2, CDH1, NBS1, RAD50, BRIP1 and PALB2 as these are often mutated in a gene pool, therefore, causing a greater effect on the population as a whole even though there is a lower risk to an individual (3). A mutation is a change in DNA bases, three of these bases code for amino acids the building blocks of proteins (there are 20 types) there for a small change in bases of DNA can cause a change in amino acid sequence causing a protein to be ineffective. If these proteins are involved in cell proliferation control, then uncontrollable mitosis can occur.

ENVIRONMENTAL FACTORS

There have also been studies that show lifestyle factors have an effect on hereditary breast cancer including BMI and alcohol intake. These studies are mainly done in western countries, so studies were needed in eastern countries to compare populations as, annually the incidence rate of breast cancer in Eastern Asia is 25.3/100,000, which is lower than that in Western Europe 89.9/100,000 and North America 76.7/100,000. This could be due to different gene pools, lifestyle or diet. One explanation they decided to investigate was the high soy diet in eastern culture compared to other countries, as studies had been done showing that increased soy intake lowers risk of breast cancer. But studies had not yet investigated the risk when comparing hereditary breast cancer of the BRCA gene to non-carrier of this gene with the same lifestyle in eastern countries. From the study, they found many comparisons in lifestyle between carries and non-carriers that are affected (4). They also saw that in non-carriers, a higher meat consummation still caused an increased risk of breast cancer, than non-meat eaters that were not the carrier of the BRCA gene. In carriers the same positive correlation between meat-eating and breast cancer was shown.

CAN ALL CANCER CELLS FORM A TUMOUR?

A study was also done where breast cancer cells were grown in immunocompromised mice, as the immune system can recognise cancer cells as abnormal and attack, preventing further growth in some cases. This study found that few cancer cells were able to form a new tumour. These cells can be identified by surface markers, then those identifiers were isolated. There were as little 100 cells that made these markers, but there are thousands of cells with other markers. As we find these markers we can more clearly understand the pathways that regulate growth and survival of these tumour cells and find treatments to stop them (5).

Treatments for Breast cancer on the NHS

There are many treatments for breast cancer depending on the type of cancer, the NHS website shows 2. Firstly there is breast-conserving surgery where only the tumour is removed or a mastectomy where the whole breast is removed this is common when cancer covers or has spread throughout the breast. After surgery some patients, with certain types of cancers, may be treated with radiotherapy this is used to kill the remaining cancer cells this usually starts a month after recovery. It can also be used after chemotherapy which is an anticancer mediation that can be used before or after surgery when the tumour is too large. This treatment is less invasive (given through a drip and sometimes oral medication is also given) meaning patients could feel more comfortable and not have to stay at a hospital. Though it also has an effect on healthy cells causing hair loss, vomiting, tiredness and loss of appetite. But on stage 2 breast cancer when cancer has spread to other parts of the body chemotherapy cannot cure cancer but may shrink it and help the symptoms of it (2).

Charities like breast cancer now fund research projects to help in the diagnoses and treatment of breast cancer such as research into antibody therapy and biomarker discovery. The aim of this group is to find a new treatment for a certain type of breast cancer called triple negative breast cancer. Which at this point has no effective treatment and the ability to identify why this type of cancer is so aggressive compared to other types. The treatment they are investigating uses antibody’s which are a type of protein used in immune response, to target specific markers on triple negative breast cancer. (6) AstraZeneca a UK based biopharmaceutical company is also doing research into Immuno-Oncology and cancer cells as the immune system finds foreign cells and destroys them. Cells that become cancerous are able to bypass this mechanism. Immunotherapy strengthens the immune system to allow recognition of these cancer cells by ‘targeted inhibition of immune checkpoints, and through the scientifically driven combination of multiple immune system-stimulating agents, including chemotherapies, small molecules, and other immunotherapies.’ (7) This research needs lab technicians and researchers to allow the procedures to be done and gain results. They may also need a nurse to administer the drug to a patient and doctors to test the effectiveness. As well as the testing of drugs then needs to be done in countries for safety approval.

There is also genetic clinics that looking to family history, work out a risk of cancer and do genetic testing. Firstly, you would meet a counsellor who can offer a genetic test if there is a strong family history. This means if a gene is found that is a risk, lifestyle choices can be taken or in some cases, medication can be prescribed (8). As well as patients being tested more regularly for cancer or from a younger age, as when caught earlier cancer has a lower mortality rate. This may require different diagnostic test and tools which need qualified people to operate. These genetic tests are done using a sample of blood, hair or skin these may need a nurse to be obtained. These are then sent to a laboratory where technicians and geneticist are needed to test the samples and give a diagnosis of risk. By look for specific changes in DNA, chromosomes and proteins, this procedure changes depending on the disorder or disease that is being looked for as it would not be effective to test for everything. This is also done for newborn babies and tests for carriers of genetic disease. (9). Labs such as Ambry Genetics, Color Genomics, GeneDX and Myriad genetics do these genetic test for breast cancer and other cancers. (10)

Novartis is now investigating artificial intelligence (AI) with start-up PathAl into the diagnostics of cancer to increase the accuracy and decrease the time. As pathologists currently diagnose from slides with thousands of cells which and are inaccurate 3-9% of the time. This requires pathologists to show the artificial intelligence samples with and without cancer cells so it can recognise patterns. As well as software developers and engineers to develop artificial intelligence so it works effectively. Data analysis must be used to see how accurate the AI is at recognising cancerous and non-cancerous sample and assess if it can be used in diagnostics. As well as nurses and medical practitioners to get cell samples (11).

Referencing

1. Genetics Reference 2019, Breast cancer, Genetics Home Reference, Available at: https://ghr.nlm.nih.gov/condition/breast-cancer [Accessed: 6 March 2019]
2. NHS (2016) Breast cancer in women, Available at: https://www.nhs.uk/conditions/breast-cancer/ (Accessed: 6 March 2019)
3. Sheikh A1, Hussain SA, Ghori Q, Naeem N, Fazil A, Giri S, Sathian B, Mainali P, Al Tamimi DM (2015) ‘The spectrum of genetic mutations in breast cancer’, Asian Pacific journal of cancer prevention, 16(6):2177-85. [Online]. Available at: https://www.ncbi.nlm.nih.gov/pubmed/25824734 (Accessed: 6 March 2019).
4. Ko, KP. Kim, SW. Ma, SH. Park, B. Ahn, Y. Lee, JW. Lee, MH. Kang, E. Kim, LS. Jung, Y. Cho, YU. Lee, B. Lin, B. Lin, JH. Park, SK. (2013) ‘Dietary intake and breast cancer among carriers and noncarriers of BRCA mutations in the Korean Hereditary Breast Cancer Study ‘, The American Journal of Clinical Nutrition, 98(6)1493-1501 [Online]. Available at: https://academic.oup.com/ajcn/article/98/6/1493/4577354 (Accessed: 6 March 2019).
5. Al-Hajj, M. Wicha, MS. Benito-Hernandez, A. Morrison, SJ. Clarke MF. (2003) ‘Prospective identification of tumorigenic breast cancer cells’, PNAS Proceedings of the National Academy of Sciences of the United States of America, 100(7)3983-3988 [Online]. Available at: https://www.pnas.org/content/100/7/3983 (Accessed: 6 March 2019).
6. Breast Cancer Now(?) ‘Antibody therapy and biomarker discovery team’ [Online]. Available at: https://breastcancernow.org/breast-cancer-research/our-research-projects/antibody-therapy-and-biomarker-discovery-team (Accessed 8 March 2019)
7. Astrazenica (2018)’immuno-oncology’ [Online] Available at: https://www.astrazeneca.com/our-focus-areas/oncology/immuno-oncology.html (Accessed 8 March 2019)
8. Cancer Research UK (2018)’Genetic testing for cancer risk’ [Online] Available at: https://www.cancerresearchuk.org/about-cancer/causes-of-cancer/inherited-cancer-genes-and-increased-cancer-risk/genetic-testing-for-cancer-risk (Accessed 8 March 2019)
9. Genetics Home Reference (2019)’How is genetic testing done?’ [Online] Available at: https://ghr.nlm.nih.gov/primer/testing/procedure (Accessed 8 March 2019)
10. Facing Our Risk of Cancer Empowered (FORCE)(2018)’Genetic testing for Hereditary cancer’[Online] Available at: https://www.facingourrisk.org/understanding-brca-and-hboc/information/hereditary-cancer/genetic-testing/basics/labs-offering-genetic-testing.php (Accessed 8 March 2019)
11. Elizabeth Dougherty (2018)’ Artificial intelligence decodes cancer pathology images’ [Online] Available at: https://www.novartis.com/stories/discovery/artificial-intelligence-decodes-cancer-pathology-images (Accessed 8 March 2019)

ABSTRACT

Rise in deaths due to prostate and breast cancer are expected to continue in future. These diseases are the most common types of cancer for men and women across the globe. Machine Learning can be used to drop the number of deaths by these diseases with early detection. One of them is the classification of data of prostate cancer and breast cancer. The Cancer data which has been used has a variety of features, but not all features are essential features. In this study, we use Support Vector Machine-Recursive Feature Elimination(SVM-RFE) as a feature selection method. In this method, it will get a ranked features list. The use of this method in the classification of prostate cancer and breast cancer data results in a high level of evaluation. This method can produce an accuracy rate of 96.50%, the precision of 96.56%, and recall of 96.50%.

Introduction

Cancer is a disease caused by abnormal cell growth. These cells exist because of the changes in gene expression, then they will be developed into a population of cell that can attack specific tissues[1]. This is very dangerous because it can cause death. Based on the Global Cancer (GLOBOCAN) statistics[2] part of the International Agency of Research on Cancer (IARC) in 2018, in the 18.1 million cases of cancer, the second most common cases experienced by men are prostate cancer cases, while the most common cancer cases experienced by women are breast cancer cases. Until now, there has not been found a way to treat cancer efficiently.

In prostate cancer, there is an uncontrolled growth of cancer cells formed in prostate tissue. It is the most common cancer in men, and the case will continue t increase in many countries. In breast cancer, there is an uncontrolled growth of cancer cells formed in breast tissue. The growth of cancer cells form lumps that can spread to other tissues within the body, which is also known as malignant tumor. Cancer data has many features that possess information about the cancer itself. However, not all features are relevant features. The benefit of feature selection in machine learning is reducing the amount of data needed to reach the learning stage, increasing the predictive accuracy, more easy-to-understand data, and reducing execution time.

In the field of health, many methods have been carried out to diagnose breast cancer and prostate cancer. But in this study, we used computational techniques by applying machine learning. The method that is proposed is Supporting Vector Machine-Recursive Features Elimination (SVM-RFE). It is expected that feature selection methods and classification methods would give significant contribution to the health sector, especially in diagnosing prostate cancer and breast cancer. Previous studies on the classification of prostate and breast cancer have been carried out with various methods such as Convolutional Neural Network, Logistic Regression and Decision Tree.

Methods

Support Vector Machines

The basic methodology of the SVM method is to form an optimal plane or hyperplane that separates data into each class. The optimal hyperplane is a field that separates data into its class and is located perpendicular to the closest pattern where patterns are dots that describe a dataset[3]-[4][5]. Suppose there is a dataset D, xi , yi where i = 1, …, D, the set of training data in the dataset D that has two classes consist of N input vectors x1, …,xn and yi with yi being the class label from the dataset (malignant cancer or benign cancer).

Support Vector Machines-Recursive Feature Elimination

It is a combination of Support Vector Machines and RFE. RFE is a method that works by selecting features recursively based on the smallest feature value. SVM-RFE works by removing irrelevant features in each iteration, namely the lowest weight feature. We can exclude more than one feature in each iteration for speed reasons.

Performance Evaluation of Model

A classification model will map data to prediction classes. There are four cases possible. If the data has a positive label and classified as positive, then it is true positive (TP); if classified negative, it is false negative (FN). If the data has a negative label and is classified as negative, then it is true negative (TN); if classified as positive, it is false positive (FP). From a classifier and a data set, a 2 × 2 confusion matrix can be formed.

Classification report is calculated which gives us the following measures: Precision is used to calculate how many of them are truly positive. Recall is used to calculate how many real positive are captured by the model and labeled Positive. F1 score is the harmonic mean of precision and recall of the model.

Experiments and Results

Data

The data used were data based on prostate cancer and breast cancer, which is obtained from the Kaggle website. 100 observations were recorded for prostate cancer data, in which 62% observations were malignant cancer and 38% observations were benign cancers. Meanwhile, the breast cancer data consisted of 569 observations, in which 212 cancers were malignant cancer, and 357 were benign cancer. Features for each data are mentioned here.

Results

The result and analysis of classification of Prostate and Breast Cancer with the help of SVM-RFE is covered in this section. The results of the ranking score that are obtained using Equation (7) for the feature selection of prostate cancer are listed below in increasing order of their weightage of features: [‘fractal_dimension’, ‘smoothness’, ‘compactness’, ‘symmetry’,’radius’, ‘texture’, ‘perimeter’, ‘area’]

The feature having highest weight is the area feature which has a weight of 23992022.23703918,while the lowest weight feature is fractal_dimension feature,which has a weight of only 1.5849710602904137. The results of the ranking score that are obtained using Equation (7) for the feature selection of breast cancer are listed below in increasing order of their weightage of features: [‘fractal dimension error’, ‘smoothness error’, ‘concave points error’, ‘mean fractal dimension’, ‘symmetry error’, ‘mean smoothness’, ‘compactness error’, ‘concavity error’, ‘worst fractal dimension’, ‘radius error’, ‘worst smoothness’, ‘mean symmetry’, ‘mean concave points’, ‘mean compactness’, ‘worst symmetry’, ‘worst concave points’, ‘mean concavity’, ‘texture error’, ‘worst compactness’, ‘worst concavity’, ‘perimeter error’, ‘mean radius’, ‘worst radius’, ‘mean texture’, ‘mean perimeter’, ‘worst texture’, ‘worst perimeter’, ‘area error’, ‘mean area’, ‘worst area’]

The first highest feature is the worst area feature which has a weight of 147512379.03601986,while the lowest feature is fractal dimension error feature, which has a weight of only 0.14560705396198254.

Conclusion

We implemented categorizing breast and prostate cancer by selecting features subsets based on Support Vector Machine-Recursive Features Elimination. In breast cancer data, feature selection was performed by selecting 8 features from 30 features that have the highest rating on SVM weights while in prostate cancer data, feature selection was performed by selecting 2 features from 8 features that have the highest rating on SVM weights. Based upon SVM-RFE experiment, the feature profile of worst area had the highest score for breast cancer while the feature profile of area had the highest score for prostate cancer. We were able to produce an accuracy rate of 96.50%, the precision of 96.56%, and recall of 96.50% with the model. In future work, SVM-RFE optimization is needed to provide a consistent process in feature selection.

References

1. NCBI. What is Cancer? https://www.cancer.gov/about-cancer/understanding/what-is-cancer.
2. IARC Global Cancer Observatory. 2018.
3. Jakkula, Vikramaditya. ‘Tutorial on support vector machine (svm).’ School of EECS, Washington State University 37 (2006).
4. Learning: Support Vector Machines https://www.youtube.com/watch?v=_PwhiWxHK8o&t=25s
5. Qifeng Zhou, Wencai Hong, Guifang Shao and Weiyou Cai, ‘A new SVM- RFE approach towards ranking problem,’ 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, Shanghai, 2009, pp. 270-273.
6. Bustamam, Alhadi & Bachtiar, Anas & Sarwinda, Devvi. (2019). “Selecting Features Subsets Based on Support Vector Machine-Recursive Features
7. Elimination and One Dimensional-Naïve Bayes Classifier using Support Vector Machines for Classification of Prostate and Breast Cancer”. Procedia Computer Science. 157. 450-458. 10.1016/j.procs.2019.08.238.
8. Guyon, I., Weston, J., Barnhill, S., Vapnik, V. (2002) “Gene selection for cancer classification using support vector machines.” Mach. Learn 46: 389– 422.
9. A. Adorada, R. Permatasari, P. W. Wirawan, A. Wibowo and A. Sujiwo, ‘Support Vector Machine – Recursive Feature Elimination (SVM – RFE) for Selection of MicroRNA Expression Features of Breast Cancer,’ 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, 2018, pp. 1-4.
10. P. A. Mundra and J. C. Rajapakse, ‘SVM-RFE With MRMR Filter for Gene Selection,’ in IEEE Transactions on NanoBioscience, vol. 9, no. 1, pp. 31-37, March 2010.
11. https://www.cancer.gov/about-cancer/understanding/what-is-cancer
12. https://www.youtube.com/watch?v=_PwhiWxHK8o&t=25s

Breast Cancer is a common cancer in Hong Kong. It can be divided into invasive and non-invasive (Akram, 2017). Breast cancer does not only exist in females, but also exist in males. According to the Centre for Health Protection, there are total 4108 females and 15 males who are diagnosed in breast cancer respectively in 2016. The morbidly rate in females is more serious than males. Among females, breast cancer has become the third leading cause in Hong Kong. When comparing the age-standardized incidence and death rates of breast cancer in female from 1981 to 2016, the data of death rate is relatively steady. However, the incidence rate keeps increasing. The median age at diagnosis was 56 years.

In order to improve breast cancer detection, there are several existing breast cancer screening health promotion programs launched in Hong Kong.

Mammography

The first screening program is to provide 2D/3D mammography for females who do not have any symptoms of breast cancer in the ages of 50 years old. They can book and make an appointment of screening service in the website of Hong Kong Breast Cancer Foundation. After the booking, clients can take the screening in Breast Cancer Support Centre.

Clinical breast examination

Moreover, there are clinical breast examination provided from the Hong Kong Breast Cancer Foundation too. Clients can receive a physical examination done by a professional doctor, nurse or other health care providers. Those health care providers will feel the breast of client gently to observe if there are any abnormalities such as breast lump and sudden changes on the skin texture of breast.

Ultrasonography

Apart from the Hong Kong Breast Cancer Foundation, Ting Wah Group of Hospital will also provide ultrasonography for females who are 40 years old or above. Compare with the mammography, ultrasonography is more efficient to depict small breast tumors that are invisible on mammography. By using an ultrasound, more densely breast tissue can be detected and provide a real image for the radiologist.

Breast self- examination

In the Department of Health, there are some breasts cancer screening health promotion resources written by the Cancer Expert Working Group on Cancer Prevention and Screening. Females can read the books or print out materials from online. For example, they will provide some guidance from steps to steps on the skills of breast self-examination. People can try to feel their breast while standing up, sitting and lying in the bed. It is an effective way to find out abnormalities in size and shape of breast through self-examination.

Effectiveness

The effectiveness of the existing breast cancer screening health promotion programs are quite successful and I will explain the reasons on two aspects. Firstly, those existing screening programs are target- oriented. Breast cancer has been the number one cancer to affect women in Hong Kong for two decades and the number of new cases diagnosed each year is increasing (Cheung, 2018, p.6). In Hong Kong, the age and gender of target group are mainly females who are 50 years old or above. To meet the needs, woman health service center will first prioritize their target group with the trend. Secondly, those programs can bring longer time effects on reducing the mortality of breast cancer. After the mammography screening, females who get a positive result on breast cancer can identify their problem on an earlier stage such as stage I cancer metastasis. Patients can also seek help from the doctors and make consultation with the health care providers. If there are no such breast cancer screening programs, people may lose the golden hour to treat their breast cancer.

Problems

There are some potential risks under mammography. Every 200 out of 2000 females may receive a false positive result after the screening. People may suffer from anxiety and carry out unnecessary treatments after the screening which is treated as a waste of time and money. Also, the mental health of clients will be greatly reduced as they feel afraid of the consequence after being diagnosed on breast cancer. Moreover, mammography may bring negative result to clients who have breast cancer truly. This kind of mistake may give a wrong message to clients, leading them not to be aware of their actual health. Finally, deterioration of breast cancer will be existed.

Need

It is essential to enhance the use of media in the promotion of breast cancer screening. The screening habits among these patients were poor, with over 60% never having undergone mammography screening before their cancer diagnosis. (Sitt, 2018, p.171). Government should put more emphasis on the media advocacy such as promoting the efficiency or available screening programs through television and internet. Regardless of geographical barrier, this practice can encourage our target audiences by increasing their participation and familiarize with the screening programs. They are not subjected to the time and location. When the help of media, people can have better ideas on the procedures, prices and sites of having breast cancer screening.

Challenges

It is challenging to provide free of charge breast cancer screening to all the females and males residents in Hong Kong. Due to the high cost of 2D/3D mammography, government may exist financial imbalance when they spend too much expenditure on medical to provide equity. It is known that the proportion of self-financed screening programs provided by private sectors are higher than the free one by . However, it is not affordable for some low-income groups and families. Because of the barrier of household income, they are hardly to receive the equal needs as middle- or high-income groups. It is a difficult task for government to strive for the balance so as to reduce inequalities in health prevention.

Abstract

Cancer is a frightful disease and represents one of the biggest health-care issues for the human race and demands a proactive strategy for cure. Exploration of natural product containing anticancer agent provide a promising line for research on cancer. Moringa plant (Moringa oleifera) is one of the medicinal plants used in traditional medicine for the treatment of cancer. Several studies have reported that water and alcoholic leaves extracts of M. oleifera have anticancer activity in some cancer cell line, including HepG2 liver cancer cells, A549 lung cancer cells, Caco-2 colon cancer cells and MDA-MB-231 breast cancer cells. This study was carried out to investigate the mechanism on selular and molecular basis of ethyl acetate fraction of M. oleifera leaves (EMO) against T47D breast cancer cell line by observing cell cycle progression. Cell cycle analysis was performed using flow cytometry and cyclin D1 expression was analyzed using immunocytochemistry method. Flow cytometry assay indicated that EMO induced cell cycle arrest on G0/G1 phases. Immunocytochemistry assay showed that the EMO decreased expression of cyclin D1.

Introduction

Breast cancer has been widely known as the most common malignancies among women. It will afflict an estimated 9.1 million women in poorer countries over the next decade. If the 5 million women expected to die from breast cancer in the next decade, 70 percent will live in lowand middle-income countries1. There is evidence for a genetic contribution to the risk of developing breast cancer, as well as an association with modern affluence (diet and alcohol consumption). In addition, the influence of reproductive factors supports a hormonal role in the etiology of the disease2.

Moringa oleifera is an edible plant native to Northern Indian subcontinents, but recently the plants are widely cultivated and become naturalized in many countries throughout Asia and Africa3,4. M. oleifera is also called with various names such as horseradish tree, drumstick tree and locally named ‘kelor’. It belongs to the family of Moringaceaeand has been used in traditional medicine for centuries. The traditional uses of M. oleifera including the treatment of bacterial, fungal, viral and parasitic issues, along with asthma, circulatory, digestive and inflammatory disorders, malaria, typhoid fever, arthritis, hypertension, and diabetes5,6. Almost every part of the M. oleifera plant from the leaves to the fruit, bark and seeds can be used to treat a diverse array of ailments, but the leaves are the most widely cultivated due to its phytochemical composition and their associated medicinal properties3. It contains a rich source of rhamnose, glucosinolates and isothiocyanates. A study conducted by Manguro and Lemmen7 into the phenolics of MOE had characterised five flavonol glycosides using spectroscopic methods. The anticancer property can be attributed to specific components of MOE such as 4-(α-L-rhamnopyranosyloxy) benzyl glucosinolate, 4-(α-L-rhamnopyranosyloxy) benzyl isothiocyanate, benzyl isothiocyanate and niazimicin. The leaves contain quercetin-3-O-glucoside and kaempferol-3-O-glucoside which plays a role in antioxidant defence as it scavengers for free radicals thus reducing oxidative stress8. In addition thiocarbamates such as niazimicin found in the leaves, can be used as a chemopreventive agent9,10. Studies have suggested that the anticancer and chemopreventive property of M. oleifera extract can be attributed to niazimicin11,12.

A number of M. oleifera leaves extract cancer studies have been published. In previous studies M. oleifera aqueous crude leaf extracts has expressed anticancer effects in both A549 lung cancer cells and SNO oesophageal cancer cells13 as well as KB tumour cells14 in a ROS-dependent manner. Apriani et al.15 have reported that ethyl acetate fraction of M. oleifera leaves had potent anticancer activity and apoptosis inducing against breast cancer cell line T47D. This study is further research of Apriani et al.15, aims to investigate the mechanism on selular and molecular basis of ethyl acetate fraction of M. oleifera leaves (EMO) against T47D breast cancer cell line through observing cell cycle progression. Expression of cyclin D1 protein level was also investigated.

Material and Methods

Plant material: M. oleifera L. leaves were collected from Mangunreja, Tasikmalaya, Indonesia on Februari 2018, during rainy season.

Extraction: M. oleifera leaves were dried with an oven at 30oC. Dry powder of M. oleifera leaves were extracted with ethanol 96% for 3×24 hours. Filtrate was concentrated using rotary evaporator at 50oC. M. oleifera ethanol extract was then partitioned with n-hexane and ethyl acetate by liquid-liquid extraction. Ethyl acetate fraction was evaporated by using rotary evaporator to get ethyl acetate fraction of M. oleifera leaves (EMO).

Cell culture: Human breast cancer T47D culture cells culture were a collection of Paracytology Laboratorium, Universitas Gadjah Mada, Yogyakarta. Cells were cultured in RPMI (Gibco, USA) supplemented with 10% Fetal Bovine Serum (Gibco, USA).

Data analysis: Cell viability resulted by EMO treatment was analyzed statistically by probit analysis using SPSS 24.

Cell cycle analysis: FACS analysis was carried out to investigate cell cycle distribution. T47D cells (106 cells/well) were grown in 6-well plate and treated with EMO. After 24 hours treatment, cells were trypsinized and centrifuged at 2000 rpm for 3 minutes. Trypsinized adherent cells were collected and detected by adding 25 µL propidium iodide, 2.5 µL RNAse, 0.5 µL Triton-X, then incubated at room temperature for 10 minutes. The cell suspension was transferred into a flow cytometer (BD FACS-Calibur, USA).

Immunocytochemistry: T47D cells were grown with the density of 5×104 cells/cover slip in 24-well plate and incubated for 24 hours. The medium in each well was then replaced by the fresh medium containing various concentrations of EMO and then placed in a humidified incubator at 37oC for 24 hours. The cells were then harvested and were washed with PBS and fixed with cold methanol for 10 minutes at 4°C. After that, the cells in coverslips were placed each on a respective slide. The cells were washed with PBS and distilled water, then were blocked in a hydrogen peroxide (Millipore Sigma, Burlington, USA) blocking solution for 10 minutes at room temperature. Then, washed again with PBS, and incubated with pre-diluted blocking serum for 10 minutes at room temperature. Next, the cells were stained with primary Cyclin D1 antibody (Biocare Medical, Cali-fornia, USA) for 1 hour at room temperature. After three time-washing with PBS, the secondary antibody was applied for 15-20 min, and then washed with PBS three times. The slides were incubated with streptavidin-biotin complex (Biocare Medical, Cali-fornia, USA) for 10 minutes, and then washed with PBS three times. The slides were incubated in DAB (3, 3 diamino benzidine) (Alfa Aesar, Ward Hill, USA) solution for 3-5 minutes and washed with distilled water. Cells were counterstained with Mayer-Haematoxilin reagent for 3-4 minutes. After incubation, the coverslips were washed with distilled water and then immersed in absolute ethanol and in xylol. The protein expression was assessed under a light microscope (Olympus Life Science, Shinjuku, Tokyo, Japan).

Results and Discussion

Cytotoxic Activity of EMO on T47D Cells: Cytotoxic activity was used to evaluate the potential of EMO cytotoxicity on T47D cells. Furthermore, IC50 value was acquired as a parameter of EMO concentration to inhibit 50% T47D cell’s growth. According to Apriani et al.15, EMO giving IC50 value of 135.321 µg/mL. Based on the IC50 value, EMO was considered medium active as an anticancer because according to Kamuhabwa et al.16, an extract is considered active if it has an IC50 value less than 100 μg/mL, but it can be still developed as an anticancer because an extract is considered inactive if the IC50 value more than 500 μg/mL17.

EMO Induce Cell Cycle Arrest: According to the cytotoxic activity mentioned above, cell cycle distribution was determined by flow cytometry using. Figure 1 shows the distribution of T47D cells treated with variation concentration of EMO for 24 h incubation. T47D cells treated with 150, 200, 250 and 300 μg/mL of EMO were accumulated in G0/G1 phase, that is from 47.64% in untreated cells to 70.24%, 58.27%, 48.36%, and 55.77%, respectively, in treated cells. In addition, doxorubicin have been reported to induce cell accumulation in G0/G1 phase in T47D cells, which is 50.64%18. This study have proven that treatment of T47D cells with EMO resulted in significant G0/G1 phase arrest of cell cycle progression which indicates that one of the mechanism by which EMO may act to inhibit the proliferation of cancer cell is inhibition of cell cycle progression.

Generally, proliferation of cells is regulated by a variety of extracellular growth factor that control the progression of cells through the restriction point in late G1. In the absence of growth factors, cells unable to pass the restriction point and become quiescent, frequently entering the resting state known as G0. They can reenter the cell cycle in response to growth factors implies that the extracellular signalling pathways stimulated downstream of growth factor receptors ultimately act to regulate components of the cell cycle19. In this study, cells accumulation in G0/G1 phase indicates that T47D cells did not get any stimulus from extracellular growth factor signalling, so that the cells could not synthesize DNA.

Expression of Cyclin D1 was caused by EMO on T47D Cell: The cell cycle progression in G0/G1 phase might be caused by some proteins that play a role in cell cycle checkpoints. Cyclin D1 is an important regulator protein of G1 to S phase progression. Therefore, the expression of cyclin D1 was also investigated using immunocytochemistry method. As shown in Figure 2, immunocytochemistry evaluation indicated that cyclin D1 level decreased significantly confirmed by an intensive brown color in cytoplasm after being treated with 150, 200, 250 and 300 µg/mL EMO.

Cyclin D1 is synthesized in a response to growth factor stimulation through Ras/Raf/ERK signalling pathway18. Together with its binding partners cyclin dependent kinase 4 and 6 (CDK4 and CDK6), cyclin D1 forms active complexes that promote cell cycle progression by phosphorylating and inactivating the retinoblastoma protein (RB)20 and then cells will be able to enter the restriction point on the cell cycle. On the other hand, if appropriate growth factors are not available in G1, cyclin D1 level will decrease and could not associated with CDK 4 and CDK 6. In this study, the cyclin D1 level was decreased, cells cannot pass the restriction point and then enter a quiscent stage of the cell cycle called G0 in which they can remain for long periods of time without proliferating.

Conclusion

In conclusion, EMO exhibits potential ability as an anticancer through cell cycle arrest on T47D cells with cyclin D1 stabilization. Observation on its selectivity as part of safety aspect is also needed. Further, EMO has a potential compound to be explored and developed as a chemo preventive agent for breast cancer.

References

1. Junk, D.J., Determining the role of p53 mutation in human breast cancer progression using recombinant mutant/wild-type p53 heterozygous human mammary epithelial cell culture models, University of Arizona (2008)
2. Coles, C., Condie, A., Chetty, U., Steel, M., Evans, H.J. and Prosser, J., p53 Mutation in Breast Cancer, Cancer Research. 52 (1992)
3. Mbikay, M., Therapeutic potential of Moringa oleifera Leaves in chronic hyperglycemia and dyslipidemia: A review, Front Pharmacol, 3 (2012)
4. Shunmuganm, L., Moringa oleifera crude aqueous leaf extract induces apoptosis in humas hepatocellular carcinoma cells via the upregulation of NF-kB and IL-6/STAT3 pathway, University of Kwazulu-Natal, Durban (2016)
5. Fahey, J., Moringa oleifera: A review of the medical evidencefor its nutritional, therapeutic, and prophylactic properties. part 1, Trees for Life Journal, 1(5) (2005)
6. Welch, R.H. and Tietje, A.H., Inverstigation of Moringa oleifera leaf extract and its cancer-selective antiproliferative properties, Journal of the South Carolina Academy of Science 15(2) (2017)
7. Manguro, L.O.A. and Lemmen, P., Phenolics of Moringa oleifera leaves. Nat. Prod. Res., 21, 56–68 (2007)
8. Goyal, B.R., Agrawal, B.B., Goyal, R.K., Mehta, A.A., Phyto-pharmacology of Moringa oleifera Lam. An overview, Nat. Prod. Rad., 6, 347–353 (2007)

Introduction

High treatment costs, strenuous treatment plans, and emotional stress on the patient and on her family are the burdens women with breast cancer have to handle. Breast cancer is the most prevalent cancer among women in the US with 245,299 new cases and 41,487 deaths as a result in 2016 with higher rates present in both cottonwood county and in Minnesota (USCS, n.d.). This paper will evaluate the prevalence of breast cancer in Minnesota and Cottonwood county. It will also examine the insurance coverage, the required healthcare, and the cost of treatment for this cancer.

Description

Even though developed countries are now seeing a higher prevalence of chronic diseases, cancer has been around since 1600 BC; it is defined as the “uncontrolled growth and spread of abnormal immortal cells,” with these cells localized in the breasts in breast cancer (Farhadihosseinabadi et. al., 2018). This cancer is generally classified into two categories: in situ carcinomas and invasive carcinomas. Both types originate in the milk ducts of the breasts with in situ carcinomas staying in the ducts while invasive carcinomas spread from its original location in the milk ducts to the surrounding breast tissue (Farhadihosseinabadi et. al., 2018). A common symptom of this cancer are abnormal lumps located on the breast.

Prevalence of Breast Cancer

Breast cancer is the second most common cause of cancer death among women in the USA, and the most common cancer in women overall (Jacobs M.D., 2011, “Preface” para. 1). Throughout a lifetime, women have approximately a 12.5% (1/8) chance of getting breast cancer (Warner M.D., 2011, p. 1025), and in 2009, 192,500 women were diagnosed with invasive breast cancer while 62,300 were diagnosed with in situ breast cancer (Jacobs MD, “Preface” para. 1). In the USA from 2012-2016, the average incidence rate per 100,000 people was 127.5 (Cancer of the Breast, n.d.), and the mortality rate per 100,000 people was 20.6 (Healthy People 2020, n.d.), and approximately 3,477,866 women had breast cancer in 2016 (Cancer of the Breast, n.d.). Of the women who get breast cancer, 89.9% have lived for a minimum of 5 more years (Cancer of the Breast, n.d.).

Risk Factors

Social determinants, as defined by Healthy People 2020, are “the environments in which people are born, live, learn, work, play, worship, and age.” Activities and behaviors associated with these determinants, or risk factors, can increase a person’s chance of developing breast cancer. Gender and age are the greatest risk factors, along with obesity, lack of exercise, and alcohol consumption (Jacobs & Yang, 2011). Jacobs & Yang also state that breast cancer has a higher prevalence in white women compared to much lower rates in African American, Asian, and Hispanic populations (2011).

Genetics also play a big role in the likelihood of developing breast cancer. Jacobs & Yang (2011) state that “50% of women diagnosed with breast cancer report a relative of any degree with breast cancer,” with less than 10% of these patients actually carrying the genes BRCA1 and BRCA2 that are believed to cause this cancer. More genetic research needs to be conducted in order to grasp a better understanding of the role genetics has in this cancer’s development.

Effects/Rates

Cottonwood county has a rather high rate of poverty. As of 2018, there were 1,386 Cottonwood county citizens living in poverty, or 12.5 percent (Encounter Now, n.d.). Having high rates of poverty may put those people at a greater probability of developing risk factors, making them slightly more susceptible. However, according to a study on the affects one’s socioeconomic status has on their breast cancer diagnosis stage, “poverty has a strong effect on the probability of being diagnosed at the later stages,” (Campbell et al., 2009). A reason for this may be due to people living in poverty and not having enough time in their day to go to the hospital for screening. Mammograms may also be too expensive for them. This is scary for cottonwood county citizens because later stages are harder to treat compared to earlier stages

In Minnesota (MN), breast cancer incidence rate per 100,000 between 2012-2016 was 130.6, and the mortality rate per 100,000 between 2012-2016 was 18.2 (USCS, n.d.). Compared to the national average, Minnesota had a higher rate of incidence by 5.6 per 100,000 (see Figure 1), but a lower mortality rate by 2.4 per 100,000 (see Figure 2). For Cottonwood county, located in the southwestern part of Minnesota, the incidence rate for 2012-2016 was 170.8 per 100,000 and the mortality rate for 2012-2016 was 22.0 per 100,000 (Breast Cancer, 2019). Compared to Minnesota, Cottonwood county has a higher incidence rate by 40.2 per 100,000 (see Figure 1), and a higher mortality rate by 3.8 per 100,000 (see Figure 2).

Economic Burden of Breast Cancer

A research paper written by the CDC’s Division of Cancer Control and Prevention and other organizations set out to determine how many the Years of Potential Life Lost (YPLL) and loss of productivity (in 2008 US dollars) due to breast cancer between 1970-2008 (Ekwueme et al. 2014 p. 1). This paper primarily focused on the premature deaths of young women (20-49 years old). It was determined that with 225,866 deaths from breast cancer an estimated 7.98 million potential years of life were lost (Ekwueme et al. 2014 p. 5), and a range between $4.23 billion and $8.81 billion were lost in productivity per year, and each life lost was estimated to be $1.10 million (Ekwueme et al. 2014 p. 5). The study however only accounts for losses after death, and no other considerations that impact both the economy and the individual such as cost of treatment, productivity loss before death, or productivity loss by family members who must take care of patients (Ekwueme et al. 2014 p. 7). The study also only looked at women from the ages of 20-49 years old, which only accounts for approximately 11% of breast cancer deaths (American Cancer Society 2017, Table 1).

Treatment Cost

Being diagnosed with breast cancer can be very intimidating and the most common question that the patients will have concerns with is the cost. When determining the estimated cost for breast cancer, there are many different factors that come into play. Depending on the stage the cancer is at, how many visits and other medical aspects, and insured or not will establish the cost one would pay. With stage 0 starting at the lowest cost around $60,000, it can rise up to around $135,000 in stage 4, and the average cost for breast cancer usually ranges close to $86,000 (Elder, 2017). The biggest factor for treatment cost depends on how early the cancer was detected and what the person has to do on the side to help them become cancer free. Everyone has different procedures and outcomes, so the treatment cost varies from person to person.

Insurance Coverage

Breast cancer is one of the most common cancers that women are diagnosed with. Not only is it very common in women, but males also have a chance to be diagnosed with it. Since breast cancer is one of the largest cancers diagnosed, insurance does contribute to the cost. Health insurance pays a huge amount and is very important when being diagnosed with breast cancer. With being insured, many bills will be covered, reducing the price of the total payment. If someone is not insured and is diagnosed, there are still many ways that the individual can reduce the price. According to Courtney Elder, some of the ways the cost can be reduced is by, “asking for generic versions of the medication, payment plans, payment assistance from the government, and using free coupons” (2017). Breast cancer can be up to $100,000 or higher for payments out of pocket. This is a huge amount of money, but with people being insured, they usually pay around $5,000.

Special Healthcare

Breast cancer survivors and women continuing with treatments display certain symptoms that allow for special care needs. Females fight through psychological and physical health issues during and after breast cancer treatments. With special care needs, the society that is being affected is gaining support and therapy to help reduce the symptoms. When working in the healthcare field there are a lot of signs that people need to pay close attention too. According to, Zhang, Q., Xiao, S., Yan, L., Sun, L., Wang, Y., and Huang, M., “healthcare workers need to pay special attention to explore the potential of psychological interventions to effectively reduce the posttraumatic stress response, encourage the adoption of positive coping strategies, and hasten disability acceptance and return to society” (2019). Special healthcare is needed for multiple situations, but for breast cancer it continues to help the patient out day by day. With providing interventions for the physical and psychological needs, patients are able to fight through the symptoms faster and have a change in their lifestyle.

Conclusion

Breast cancer is constantly affecting women around the world and is increasing on a day to day basis. With certain determinants, people can help prevent the chance of developing breast cancer while others cannot due to genetics. Early detections and screenings are the number one procedures people should complete to receive the best outcomes. Due to different stages of breast cancer and treatments, the cost varies differently depending on one’s financial aid. Providing interventions for screenings and early diagnosis can help cure an individual with less treatments and a reduced cost.

References

1. American Cancer Society. (2017). Breast Cancer Facts & Figures 2017-2018. Atlanta: American Cancer Society, Inc. Retrieved from https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/breast-cancer-facts-and-figures/breast-cancer-facts-and-figures-2017-2018.pdf.
2. Breast Cancer Infographic Data Tables. (2019). Retrieved from https://www.health.state.mn.us/data/mcrs/docs/bcafinfogtable.pdf
3. Cancer of the Breast (Female) – Cancer Stat Facts. (n.d.). Retrieved from https://seer.cancer.gov/statfacts/html/breast.html
4. Campbell, R. T., Li, X., Dolecek, T. A., Barrett, R. E., Weaver, K. E., & Warnecke, R. B. (2009). Economic, racial and ethnic disparities in breast cancer in the US: Towards a more comprehensive model. Health & Place, 15(3), 855–864. https://doi-org.ezproxy.usd.edu/10.1016/j.healthplace.2009.02.007
5. Farhadihosseinabadi, B., Hosseini, F., Larki, P., Bagheri, N., Abbaszadeh-Goudarzi, K., Sinehsepehr, K., Johari, B., & Abdollahpour-Alitappeh, M. (2018). Breast Cancer: Risk Factors, Diagnosis and Management. Medical Laboratory Journal, 12(5), 1–9. https://doi-org.ezproxy.usd.edu/10.29252/mlj.12.5.1
6. Elder, C. (2017, March 19). How much is breast cancer treatment in the U.S.? Retrieved from https://www.singlecare.com/blog/breast-cancer-treatment-cost-u-s/
7. Ekwueme, U. D. PhD, MS. et al. (January 2014). Health and economic impact of breast cancer mortality in young women, 1970–2008. AM J Prev Med, 46(1): 71–79. doi:10.1016/j.amepre.2013.08.016
8. Encounter Now. (n.d.). Retrieved from https://www.povertyusa.org/data/2018/MN/cottonwood-county
9. Jacobs, L., Finlayson, C. A., & Yang, S. C. (2011). Early diagnosis and treatment of cancer: Breast cancer. Philadelphia: Saunders.
10. USCS Data Visualizations – CDC. (n.d.). Retrieved from https://gis.cdc.gov/Cancer/USCS/DataViz.html
11. Search the Data: Healthy People 2020. (n.d.). Retrieved from https://www.healthypeople.gov/2020/data-search/Search-the-Data#objid=4069
12. Warner, E. (2011). Breast-cancer screening. The New England Journal of Medicine, 365(11), 1025-1032. doi:http://dx.doi.org/10.1056/NEJMcp1101540
13. ZHANG, Q., XIA0, S., YAN, L., SUN, L., WANG, Y., & HUANG, M. (2019). Psychosocial Predictors of Adjustments to Disability Among Patients with Breast Cancer: A Cross-Sectional Descriptive Study. Nursing Research, 27(2), e15. https://doi-org.ezproxy.usd.edu/10.1097/jnr.0000000000000283

Abstract— Breast Cancer is one of the most common disease that is responsible for a high number of deaths every year. Despite the fact that cancer is treatable and healable in earliest stages, A huge number of patients are examined with cancer very late. Data mining process and classification are efficient way to categorise the data particularly in medical fields, where those approaches are broadly used in diagnosis to make decision. Mining provides useful information from the huge volume of the data stored in repositories. The present study focusses on implementing some different algorithms using the data mining to find out the relations between different attributes and visualize them. The Dataset is taken from UCI machine learning repository. The Main thing that motivated us to do this is the capability of modern data mining algorithms that can derive meaning from the given data. The prediction is done based upon the accuracy rates of each and every algorithm i.e., the algorithm with the best accuracy will be taken for prediction.

I. Introduction

There are many hazardous diseases in the present world but nothing is more deadly and dangerous than cancer. Among the family of cancers, Breast cancer is a type that exclusively affects women in the world, this disease mostly due to abnormalities inherited from their parents and there are two types of breast cancers malignant and benign. Benign means that cancer is in early stage and can be cured, and an additional problem is that this disease can reoccur even after completion of the treatment, as the number of cases of this disease is on a rise in all of the world we also have increased data that we have about the patients that incur this disease, so we can use aid of technology (i.e) Data Mining Techniques. We can use these techniques to predict the occurrence or recurrence of breast cancer and there are numerous papers for recognition, prediction and clustering using different principles like Associative rule mining, Classification Rule mining with algorithms like C5.0, K-Nearest Neighbour, Support Vector Machine, Fuzzy C-Mean etc. The above mention techniques were performed on datasets like SEER etc. Now in this project, we are going to take a dataset from UCI Repository which contains a total of 570 records and 32 attributes like diagnosis which states if that patient is either in malignant indicated in the data set as ‘M’ or benign symbolised by ‘B’ and radius which is the mean of distances from centre to the points on the perimeter, texture which is standard deviation of grey-scale values, area, perimeter etc are some of the values which are calculated for each nucleus. Here in this experiment, we will use different algorithms like Support Vector Machine (SVM), Decision Tree, Naïve Bayes (NB) and k Nearest Neighbours(k-NN) to get the relationship between attributes. We use Boosting algorithms like Ada Boost, Gradient Boost, XG Boost (Extreme Boost) to increase the prediction speed when compared to a normal algorithm through which we can obtain results at much more speed. We have seen various algorithms and have decided to use Ada-Boost and Gradient Boost algorithms, However, we have already applied other primitive algorithms such as KNN,SVM etc on the dataset while experimenting with them.

II. Related Work

In this section, we first review a couple of related work on breast cancer disease findings using data mining techniques. We then examine some related work at breast cancer investigation

In the Research Paper of Dr.S.N.Singh, they used four classifiers: J4.8, Simple CART, NaiveBayes, Bayesian LogisticRegression. Which yielded a considerable accuracy but was lacking modern standards and their dataset was taken from WBCO [bc7 doc].This shows promise for many of those algorithms but we are taking it to boost algorithms to test if they can perform better practically

Dona Sara Jacob, Rakhi Viswan, V Manju, L PadmaSuresh, Shine Raj published another paper which theorizes that identification of the tumour in the first stage is the most critical strategy that can save many more lives and hence they took a different approach and a different dataset from WPBC and also used WEKA GUI to get comparable and statistical visuals of their approach[bc9 doc]. This inspired us to look in more than one direction through which our problem could be addressed and could potentially be solved in an efficient way.

Dr. K. Soma Sundaram’s research paper suggests that we could use blood sample data as a way to predict breast cancer[bc8].

Our view on this approach wasn’t much positive as it would take time to sample the blood and the results to be generated and formatted for the algorithm to work, This approach however produced a better accuracy compared to other methods we saw as it has a set of really strong attributes/features that can predict the result efficiently.

A research paper published by Chinese teachers Qi Fan, Chang – Jie Zhu and Liu Yin [be3doc] particularly caught our eyes because they were using Decision tree algorithms to draw the prediction specifically C 5.0, CHAID, C&RT, and QUEST with a SEER Dataset However their accuracy was considerable but was low (65-75%) and this caused us to reconsider the algorithms we wanted to initially use.

Researchers Umesh D R and Dr. B Ramachandra used a dataset obtained from Globocon which had huge number of entries and that potentially resulted in getting higher accuracy of 87% as the model had a lot of unbiased data to train on[bc6]

A group of scholars (D. Soria, J.M. Garibaldi, F. Ambrogi, P.J.G. Lisboa, P. Boracchi, E. Biganzoli) used Five different algorithms (i) Hierarchical (H), (ii) Fuzzy C-Means (FCM), (iii) K-means initialized with hierarchical clustering (method average), (iv) Partitioning Around Medoids (PAM), and (v) Adaptive Resonance Theory (ART). On a local dataset of a series of 1076 patients from the Nottingham Tenovus Primary Breast Carcinoma Series presenting with primary operable invasive breast cancer between 1986-98[bc5]. This resulted in a very varying result out of which PAM was outstanding and most accurate.

Using a Part of Data mining (PCA)-Principle Component Analysis Sharaf Hussain, Naveen Zehra Quazilbash, Samita Bai, and Shakeel Khoja from IBA presented a paper which yielded the most relevant attribute selection from a given set of vast features[be2]. This helped us consider the features in our dataset which were viable to be used as inputs and which weren’t further narrowing down our strategy and getting us one step closer to develop our algorithm.

III. Proposed Work

Based on what we have learnt so far from various sources, We have finalized on using two of the modern algorithms as addition to our list in which we have already tested the simpler algorithms such as KNN, SVM etc..

Those two algorithms will be Ada Boost and Gradient Boost algorithms. Both are decision tree boosting algorithms. We have previously seen that the works of some researchers had low accuracy with decision tree algorithms and hence we plan to approach this problem with boosting algorithms that require compute power but not a lot of GPU power to give us acceptable results.

We have chosen both of these algorithms for their features which are detailed below

AdaBoost

The general idea behind boosting methods is to train predictors sequentially, each trying to correct its predecessor. The two most commonly used boosting algorithms are AdaBoost and Gradient Boosting. In the proceeding article, we’ll cover AdaBoost. At a high level, AdaBoost is similar to Random Forest in that they both tally up the predictions made by each decision trees within the forest to decide on the final classification. There are however, some subtle differences. For instance, in AdaBoost, the decision trees have a depth of 1 (i.e. 2 leaves). In addition, the predictions made by each decision tree have varying impact on the final prediction made by the model.

In first step of AdaBoost each sample is associated with a weight that indicates how important it is with regards to the classification. Initially, all the samples have identical weights

Next, for each feature, we build a decision tree with a depth of 1. Then, we use every decision tree to classify the data. Afterwards, we compare the predictions made by each tree with the actual labels in the training set. The feature and corresponding tree that did the best job of classifying the training samples becomes the next tree in the forest. Once we have decided on a decision tree. We use the proceeding formula to calculate the amount of say the it has in the final classification.

Significance=1/2(log((1-totalerror)/totalerror)

Where the total error is the sum of the weights of the incorrectly classified samples.

We look at the samples that the current tree classified incorrectly and increase their associated weights using the following formula.

New sample weight=sample weight*e^significance

Then, we look at the samples that the tree classified correctly and decrease their associated weights using the following formula.

New sample weight=sample weight*e^-significance

We start by making a new and empty dataset that is the same size as the original. Then, imagine a roulette table where each pocket corresponds to a sample weight. We select numbers between 0 and 1 at random. The location where each number falls determines which sample we place in the new dataset.

Repeat steps 2 through 5 until the number of iterations equals the number specified by the hyperparameter (i.e. estimators) [image: ]

Now use the forest of decision trees to make predictions on data outside of the training set

The AdaBoost model makes predictions by having each tree in the forest classify the sample. Then, we split the trees into groups according to their decisions. For each group, we add up the significance of every tree inside the group. The final classification made by the forest as a whole is determined by the group with the largest sum.[link1]

Grdient Boost

Gradient Boosting is similar to AdaBoost in that they both use an ensemble of decision trees to predict a target label. However, unlike AdaBoost, the Gradient Boost trees have a depth larger than 1. In practice, you’ll typically see Gradient Boost being used with a maximum number of leaves of between 8 and 32.

When tackling regression problems, we start with a leaf that is the average value of the variable we want to predict. This leaf will be used as a baseline to approach the correct solution in the proceeding steps.

For every sample, we calculate the residual with the proceeding formula.

Residual=actual-predicted

Next, we build a tree with the goal of predicting the residuals. In other words, every leaf will contain a prediction as to the value of the residual (not the desired label).

Each sample passes through the decision nodes of the newly formed tree until it reaches a given lead. The residual in said leaf is used to predict the value.

It should be better used with a learning rate so that the algorithm doesn’t just memorize all the values and give you a really good accuracy at the first run itself

Hence a learning rate alpha must be introduced which lies between 0 and 1

Prediction=first guess (avg) + (alpha * residual)

[image: ]Again we keep computing the residuals and branching until we are getting perfect values or a given bound is reached.

The final prediction will be equal to the mean we computed in the first step, plus all of the residuals predicted by the trees that make up the forest multiplied by the learning rate.[link 2]

Other Algorithms

We have also used simpler algorithms like KNN, Apriori and SVM to make a predictive model but we mainly focused on these boosting algorithms as they are the latest and an unexplored piece of the data mining archipelago which are mostly used at the mid-higher level of the industry.

One of the main differences between AdaBoost and Gradient Boost is that one allows the creation of unequal stumps while the latter doesn’t which has a slight impact on performance depending on the kind of data given as input.

IV. Conclusion

In this study we have taken different attributes and by comparing different algorithms we came to the conclusion that the Gradient boosting algorithm is the best among algorithms

References

1. Dona Sara Jacob, Rakhi Viswan, V Manju, L PadmaSuresh, Shine Raj, “A Survey on Breast Cancer Prediction Using Data Mining Techniques”
2. D. Soria_, J.M. Garibaldi_, F. Ambrogiy, P.J.G. Lisboa], P. Boracchiy, E. Biganzoliy, “CLUSTERING BREAST CANCER DATA BY CONSENSUS OF DIFFERENT VALIDITY INDICES”
3. S. Muthuselvan Dr. K. Soma Sundaram Dr. Prabasheela, “Prediction of Breast Cancer UsingClassification Rule Mining Techniques in Blood Test Datasets”
4. Umesh D R Dr. B Ramachandra, “Association Rule Mining Based Predicting Breast Cancer Recurrence on SEER Breast Cancer Data”
5. Dr. S. N. Singh Shivani Thakral, “Using Data Mining Tools for Breast Cancer Prediction and Analysis”

In this essay, a patient’s journey from diagnosis to completion of treatment will be discussed. The topics dealt with will include causes, to life after treatment. The text below will suggest the best possible actions and care for the patient.

Etiology and Epidemiology

Intraductal Carcinoma of the breast is classified as an in situ tumor where the lining of the duct mutates and become cancerous but does not spread (Cancer Research UK, 2017). In 2015, this accounted for around 7,900 female and 30 male breast cancer cases (Cancer Research UK, n.d). Malignant cell development in breast cancer has been linked with the lifestyle of an individual. For example, the medication they take such as the contraceptive pill or hormone replacement therapy; a lack of exercise and obesity (Cancer Research UK, 2017). Family heritage and genetics can also be a factor in cancerous cell formation; where a group of cells are already partially faulty and need fewer mutations for malignant cells to be created (NHS, 2016). One group of people known for inheriting the faulty BRCA 1 or BRCA 2 gene are the Ashkenazi Jewish population (Harmer, 2016). A final cause to discuss is age. If a female patient is 50, they are most likely to be going through the menopause which increases the risk of cancer. NHS (2016) statistics state “8 out of 10 cases of breast cancer occur in women over 50”.

Presentation and GP

A patient’s journey begins at presentation. Two paths can lead an individual to specialists for diagnosis. An initial appointment with a General Practitioner (GP) is often where a cancer is discovered as symptoms are experienced. These could include lumps in the breast or axillary nodes, breast pain and nipple abnormalities (Koo et al., 2017). To expand, Sibbering & Courtney (2015) state that the nipple may be discharging blood and be inverted. Using these signs, a GP would follow the NICE guidelines (National Institute for Health and Care Excellence, 2015) and complete a checklist to see if the patient needs a referral to a specialist. If appropriate, an individual should be seen within two weeks of a referral (Cancer Research UK, 2017). Secondly, a female between 50 and 71 years old should be invited every 3 years to take part in the national breast screening program (Public Health England, 2015). During a visit, a mammogram is taken. Kinsey-Trotman & Fosh (2016) show that at least 20% of tumors are caught through the screening program. Similarly, Wexelman et al. (2017) support that many tumors are caught during screening, even precancerous or low stage cancers are captured, improving survival rates. A patient will be referred to a specialist for more testing if needed after screening.

Diagnosis and Staging

Further investigations take place following a mammogram, these may include other diagnostic imaging. Alongside this, a needle biopsy will be taken where a collection of core tumor cells sent for analysis. Also, biopsies could be gathered from the sentinel lymph node in the axillar to test for nodal involvement (NHS, 2016). A hormone test may be conducted to see if a tumor can be treated and contained through this method of treatment.

After receiving test results, TNM staging from images is used in to evaluate the size of a primary tumor (T) to recognize if there is nodal involvement (N) and to see if there are metastases (M). In this case, T1N0M0 means there is a primary tumor of less than 20mm in diameter (Cancer Research UK, 2017). The sample of cells is taken to histology for analysis by microscopic testing. In this instance, a grade 3 cancer is diagnosed: this means the tumor has very differentiated cells compared to normal cells. The carcinoma is aggressive and developing quickly (NHS, 2016), which could change the course of action for treatment.

Principles and Methods of Cancer Management

Following diagnosis, a case is passed through to a Multidisciplinary Team (MDT). A MDT includes surgeons, radiographers and specialist nurses to help plan the best course of treatment for a patient (Kesson et al., 2012). At this stage, the patient’s disease would be treated radically in the hope that the cancer does not return. A radical surgical option is a mastectomy. These management options are suggested in patients at high risk of recurrence, for example, those with the BRCA 1 or 2 gene (Senkus et al., 2015). But if a patient wants to have this procedure done for other reasons the MDT should take their preference into consideration. Polsky et al. (2002) shows the positive impact that patient choice has on an individual’s health and wellbeing. In this case, there is little information suggesting the patient has a high risk of recurrence therefore, the procedures above would not be recommended and a breast conserving surgery would be a more viable option (Senkus et al., 2015). However, Sibbering & Courtney (2016) propose that a mastectomy is the most effective treatment but agree it is the patient’s choice. Advancements in restructuring technologies, for example onco-plastic surgery mean an individual may favor a mastectomy. In addition, Whelan et al. (2015) reveals the majority of people with stage one breast tumors will have a cancer management plan which involves a combination of treatments. It is suggested that this patient has a lumpectomy with whole breast external beam radiotherapy (WBRT). Senkus et al. (2015) state that in most oncology departments it is compulsory to have radiotherapy after a breast-conserving surgery for the benefit of the individual as it reduces the potential return of the cancer. Furthermore, to reduce cardiac dose and avoid causing cardiovascular disease, in left-sided breast cancer patients, deep inspiration breath-hold can be used (Van Haaren et al., 2017). Czeremszynska et al. (2016) find that using this procedure results in a 20% reduction in the dosage to the heart which benefits the patient by preventing radiation-induced side effects. However, some patients are not able to hold their breath long enough for the treatment to be effective (Czeremszyńska et al., 2017).

This patient could present with a variety of hormone positives and negatives after testing. If the patient is estrogen receptor positive (ER +) survival rates increase by 4% (Predict Breast Cancer, n.d) as there are more treatment options. Similarly, there is an increase in survival rates if a patient is human epidermal receptor positive (HER2 +) and progesterone receptor positive (PR +). A hormone treatment appropriate for the patient is aromatase inhibitors instead of other treatments such as tamoxifen due to the individual’s menopausal status (NICE, 2018). Margolese et al. (2016) support the use of Arimidex and show the benefits for postmenopausal women. However, Forbes et al. (2016) state there is little difference between the two and an extensive study needs to be conducted to see the true effects. The NICE pathways (2018) show that a patient who is advised to take endocrine therapy for 5 years, is over 50 years old, has no nodal involvement and a tumor size less than 3cm, should be considered for partial breast radiotherapy (PBRT), instead of WBRT, to reduce the risk overtreatment (Cutuli, Bernier & Poortmans, 2014). However, Szumacher et al. (2016) found that patients preferred having WBRT to PBRT due to the different side effects that each procedure has. On the other hand, Livi et al. (2015) conducted a scientific study and found the results of each treatments recurrence rate similar but stated PBRT caused fewer and lower risk side effects. All of this shows the controversial mix of ideas, findings and opinions making choosing the best treatment difficult.

Another option is chemotherapy; this can be neoadjuvant or adjuvant to surgery and radiotherapy. Chemotherapy is a systemic treatment; effecting the whole body not just the tumor (NHS, 2017). Normally, chemotherapy is used to reduce the size of the tumor and treat metastases (Symonds & Walter, 2012). This patient has a grade 3 tumor, which is aggressive (NHS, 2016), so chemotherapy could be used. Whereas, if it were a grade 1 tumor, chemotherapy would not be the first option. Also, this treatment is usually used for those with a strong or triple negative hormone response (Derks et al. 2018). Therefore, chemotherapy is not as viable as it would cause many unnecessary side effects.

Patient Care

Many side effects are seen due to treatment, especially during radiotherapy. A radiographer everyday will communicate with the patient giving advice and support. Firstly, a common physical side effect is erythema which worsens with dose given (Badenchini et al., 2017). Radiographers could suggest many options; the Society of Radiographers (2015) recommend aqueous cream instead of soap for the irritation, sodium lauryl sulphate free moisturizer for comfort and loose cotton clothing to prevent friction. Not only are the physical impacts important but making sure that the patient has a comfortable and supportive atmosphere to walk into every day is essential. Communication is a key factor in patient care; it should be age, culturally and knowledge appropriate considering learning deficiencies, language barriers and disabilities so all have access to their treatment (NICE, 2016). Non-verbal communication is also picked up by a patient (Health Care Professions Council, 2013). If you use non-verbal communication effectively, for example a smile and arms uncrossed, an individual can reflect your attitude, increasing their mood and making the daily exposure more successful. Active listening and observation are also critical to a patient’s journey as if they feel the validated and supported, they will relax and treatment will be more beneficial (Simpson, Truant & Resnick, 2014). If a radiographer uses these skills a patient is more likely to open up about their issues and side effects meaning they can have a more holistic view of the patient’s situation and they are then able to refer the individual to others for more assistance.

Potential Psychosocial Issues

An individual could be signposted by a radiographer to a support team, for example the Macmillan Center, for the psychological issues which arise during treatment. This could be because of an influx of strong emotions including anxiety, depression, anger and shame for both patients and their families (Macmillan, 2017). If this patient has surgery, self-esteem and sexual function may affected (Jun et al., 2012). This could be due to the disfigurement which occurs as a result of their treatment which effects the perception of their body image (Park et al., 2015). Not only this, when patients receive a cancer diagnosis, they may fear death and the consequences for their families (Rocha-Cadman, 2014). Furthermore, during a patient’s journey, the financial burden can increase for every person involved. An individual will have to travel into the hospital and they may have to pay for painkillers or medication (Pisu, 2014). Finally, people may lose the hope they have in their faith and the meaning of their life (Yun et al., 2017). At this point, a patient may have been seen by many members of the MDT including a group psychologist and a chaplain, if they choose. All these psychosocial issues show the need for integrated centers within hospitals as patients need as much support as they can get (Weis, 2015). Grassi et al. (2015) illustrate that good psychological, emotional, financial and spiritual support leads to lower mortality rates during and after treatment.

Clinical Follow Up, Support and Life after Treatment

Following completion of treatment, regular physical examinations, mammograms and blood tests are recommended for patients to attend (Khatcheressian et al., 2013). These should take place annually for at least 5 years (Cancer Research UK, 2017). During the first check up a patient will receive a care pack containing information on what the tests involve, symptoms to be aware of in case of recurrence and telephone numbers for clinical departments if they have any worries (Cancer Research UK, 2017). Patients are also advised to join external support groups including Maggie Centres, as life after treatment is found to be challenging by patients. The fear of cancer returning will impact an individual and their family even after the completion of routine checks, many years later (Sharpe et al., 2018). Survivorship care plans are also put into place to get patients back to their normal routines. Palmer et al. (2015) demonstrate the positive effect of survivorship care programs have in getting cancer patients back on their feet and motivated again.

Consent:

The principle of consent is an important part of medical ethics and international human rights law.[20]

Is a written document that crystallizes the communication ongoing between surgeon and patient and that proves that the patient has made an informed decision evaluating pros and cons and possible alternative treatments and gives consent for that specific treatment.

I usually give a consent form to the patient during the second or third clinic appointment, sometimes even later if more complex surgery is required or if a prophylactic mastectomy is involved.

After having discussed in a way that is understandable for the patient: her diagnosis and potential prognosis, the likelihood of success, the potential complications, recommended alternatives including no treatment and their risks and potential follow-up.

I divide complications into generics linked to surgery and anesthesia (like pain, scar, bleeding, infection, recurrence, fat necrosis, lymphedema, seroma, anesthesia-related complications, and DVT). and specific to the surgery chosen by the patient, for instance, if this patient chooses an implant-based reconstruction there will be specific mention of capsular contracture, implant rotation, rippling, implant rupture, asymmetry needs for further surgery, implant loss, BIA-ALCL needs to be discussed with every patient considering implant-based reconstruction, asymmetry, loss of sensation.

If the patient chooses an autologous reconstruction, then I will discuss flap loss, flap necrosis, donor site morbidities, and delayed healing.

I will also discuss approximate operative timing, postoperative analgesia, recovery times, the need for the use of a drain, and the time to remove it.

All this information is not only on the consent form but also recorded in the patient’s notes and all communication with the patient and GP, including the names of those who were present at the consultation.

The consent form will then be re-signed on the morning of surgery giving the patient time to ask further questions if needed before I will do the preoperative checks.

The consent form I use includes consent for medical photography.

Surgical plan for this patient:

I have never had a patient with breast cancer after treatment for HL, but I have cared for three patients who had mantle radiation for HL and requested bilateral prophylactic mastectomies, all of them chose implant-based reconstruction and immediate implant-based reconstruction.

Building on this experience I believe that both implant-based reconstruction and autologous reconstruction are valid options, and it will be the patient preference to guide me.

If her choice would be an autologous flap I will present her to the reconstruction MDT and the joint clinic with plastic surgeons.

If her choice of implant-based reconstruction will be discussed at MDT, I would favor a two-stage reconstruction with very careful expansion.

Adjuvant treatment:

After surgery the histological results will be discussed at post-op MDT and an adjuvant treatment plan will be defined.

In consideration of her ER PR status, this patient will be prescribed an aromatase inhibitor for 5 years. Large, randomized studies have shown that the adjuvant use of Tamoxifen for 5 years decreases the risk of recurrence of breast cancer by 45–50% and the risk of death by 31%, and the benefit persists for years after therapy is discontinued [21].

Several trials have assessed the utility of AIs in post-menopausal women with ER or PR breast cancer. AIs are less likely than tamoxifen to increase the risk for thrombotic events or endometrial cancer; however, they are more likely to be associated with arthralgia and osteoporosis. [21].

Since she had previous radiotherapy, she will not be recommended further radiotherapy as part of her adjuvant treatment.

If the Nottingham Prognostic Index or Predict demonstrates an intermediate risk Oncotype DX will be requested to assess if she would benefit from chemotherapy. However, due to the unique molecular biology of ILC, treatment response to chemotherapy is often predictably poor.

Recommended surveillance and follow-up

I will review this patient 2 weeks postoperatively to check her wounds and see how she is progressing.

Will refer her to Oncology to discuss in detail her adjuvant treatment.

I will follow her with a clinical follow-up appointment in year one and year 2 and with an annual surveillance mammogram for 3 years. From a reconstruction point of view during the first year, they will have an appointment a 3-month,6 months, and 1 year in the reconstruction clinic after that if they have any problem, they can contact the named reconstruction nurse who if needed will provide them with rapid access to the clinic.

The BCN will contact her with a phone call a 6-week post-op, and then at 6 months, all patients are actively encouraged to contact the breast care nurses if they have any problems or questions.

An onco-psychologist is available i,f at any point of their journey, they feel they need any psychological help. Oncologists will see her every 6 months for the first year and then yearly for the first 5 years. If she had an autologous flap plastic surgeon will guide the surgical follow-up.

NICE guidelines 2018 [4] state that everyone who has had treatment for early breast cancer should have a copy of a written care plan. The care plan has information about follow-up and signs and symptoms to look out for. It will also include contact details for specialist staff, such as breast care nurses.

Relevant Lifestyle factors:

Certain breast cancer risk factors are related to personal lifestyle factors like diet, drinking alcohol, weight gain postmenopausal obesity, and taking postmenopausal long hormonal replacement. Some modifiable risk factors decrease the risk of breast cancer like exercise or breastfeeding (4,3% reduction in relative risk of breast cancer for every 12 months of breastfeeding) [22]

This patient had chest wall radiation 20 years before and this certainly increased her risk of breast cancer. She attended a breast screening

I don’t think there is anything more specific she could have done if not maintaining a healthy body weight and physical activity.

Patients Reported Outcome Measures PROMs

Proms. (Patient Reported Outcome Measures) are procedure-specific questionnaires, designed to measure outcomes that are important to the patient.

In breast surgery, PROMs enable patients to quantify their symptoms and QoL about their cancer and enable them to evaluate the impact and effectiveness of their treatment. [23]

The BREAST-Q™ and The European Organisation of Research and Treatment of Cancer’s Quality of Life Questionnaire (EORTC QLQ) are two validated PROMs that have been widely utilized in breast surgery outcome assessment. [24]

The BREAST-Q is PROM questionnaire has been validated for patients undergoing breast reconstruction to enable measurement of quality of life (QoL) and satisfaction in this specific population of patients [25]

The EORTC QLQ-C30 and BR-23 despite being the most widely used questionnaire in Europe for assessing breast patients, although validated PROMs are not specific for breast reconstruction.

QLQ-C30 is a cancer-specific health-related QoL questionnaire analyzing domains such as physical, emotional, cognitive, and social functioning and sequelae of disease such as pain, nausea, and fatigue, while QLQ-BR23 is a PROM validated for breast cancer patients, assessing factors such as body image, sexual functioning and enjoyment, cancer symptoms, and systemic therapy side effects.

A systematic review by Cordova “ PROMs following mastectomy with breast reconstruction or without reconstruction” did show that breast reconstruction following mastectomy has led to better patient-reported outcomes compared to mastectomy-alone in the great majority of trials But care must be taken when interpreting the data the lack of randomized controlled trial [26]

A systematic review by Phan “The Use of Patient Reported Outcome Measures in assessing patient outcomes when comparing autologous to alloplastic breast reconstruction” demonstrated that breast reconstruction after mastectomy has been associated with positive psychological outcomes. When comparing autologous and alloplastic-based reconstruction, it is apparent from the patient’s perspective that autologous reconstruction is no less favorable and has a higher satisfaction of breast rate than alloplastic reconstruction techniques. Physical and sexual well-being were equivocal between the two reconstructive groups. [27]

Medico-legal aspects:

In the past the majority of complaints against breast surgeons were due to delays in breast cancer treatment and diagnosis, but with the introduction of MDT meetings and tighter National guidelines, improved radiology techniques it has become unusual to have a significant delay in diagnosis once a patient is under the care of the breast team [28].

Litigation concerning cosmetic and reconstruction surgery outcomes has now become the most frequent cause of medico-legal problems. The appearance of the breast has become a critical component even in breast cancer treatment. Reconstruction surgery is not the equivalent of aesthetic surgery in terms of outcomes and judgments. It is and remains both an oncologic and a reconstructive procedure with all the oncologic limits in its background. [29] As the majority of the allegations arise from unrecognized or unmet expectations, all the limitations of reconstruction must be discussed with the patient and included in the informed consent to avoid errors of interpretation and communication between the surgeon and the patient (29). Clear, detailed documentation of clinical examination, diagnosis, MDT meetings, informed consent, and shared decision plus photographic documentation of pre and postoperative results, follow up and adjuvant treatment plan should be stored in the patient file.

Adherence to national guidelines local protocols and regular clinical governance would reduce the chance of medico-legal problems.

Introduction

Triple negative breast cancer is an aggressive subtype of breast cancer which do not have any receptors that are commonly found in other subtypes of breast cancer. It shows negative for the progesterone receptors (PR), estrogen receptors (ER) and human epidermal growth factor (HER2) protein. About 12 to 20 percent of all breast cancers are triple negative breast cancer and it is associated with poor survival rate [1]. Its recurrence and metastasis rates are higher compared to other breast cancer (BC) subtypes with the median overall survival only about 9 to 12 months [2]. TNBC has the least number of therapeutic options among the subsets of breast cancer because of lack of well-defined targets. This paper aims to discuss and compare different treatment techniques for triple negative breast cancer (TNBC).

Pembrolizumab and immunotherapy

Programmed cell death protein 1 (PD-1) is a protein on the surface of cells that protects the human body’s cells from the body’s immune system. PD-1 inhibitors are a class of drugs that block PD-1 to activate the immune system to attack cancer cells. The drug pembrolizumab is a monoclonal antibody which belongs to the class. In a study [3], pembrolizumab was given intravenously at 10 mg/kg every two weeks to heavily pre-treated patients with advanced TNBC. It gave preliminary evidence of the safety and efficacy of the drug. A decrease from the baseline in tumor burden was seen in 37.5% of the patients and the disease control rate was 25.9%.

An advantage of this method is that pembrolizumab is an established successful treatment for advanced melanoma. This guarantees the safety of the drug. But this study is limited because only 26 evaluable patients were tested. We cannot rely completely on the evidence. Another limitation is that 56.3% of patients experienced at least one treatment-related toxicity. The study also shows that patients with increased lactate dehydrogenase levels cannot profit from treatment.

Platinum-based chemotherapy

When mutations arise in BRCA1/2 gene, there is an increased risk of breast cancer. About 80% of breast cancers which occur due to a BRCA1 mutation are TNBC. Cisplatin and carboplatin are drugs with platinum. When they bind with DNA, they interfere with its repair mechanism leading to cell death. In a study [4], patients with metastatic or locally recurrent unresectable TNBC are given either cisplatin (75 mg/m2) or carboplatin (at area under the time concentration curve 6) every three weeks. Cisplatin therapy had a response rate of 32.6% while the carboplatin had response rate of 18.6%. Progress was seen in 33% of the patients.

Cisplatin therapy shows a high response rate when compared to other trials treating TNBC patients. Both cisplatin and carboplatin are only mildly toxic. One limitation is that the response rate varies when compared with other similar trials [5] but the study does shed some light on this treatment not working on people with low homologous recombination deficiency (HRD) scores. The testing population of 86 patients was small in this study.

Precision combination therapy

In a study [6], amphiphilic co-polymer P (MEO2MA-co-OEGMA-co-DMAEMA)-b-PLGA is used as a building block for a core structured nanoparticle. Doxorubicin and paclitaxel are loaded into the hydrophilic core and hydrophobic layer of the nanoparticles while the small interfering RNA is absorbed onto the surface of the nanoparticle. This structure is the modified with PDA at the outer layer and named NP-DTS-PDA. This drug is injected intravenously into mouse affected with tumor. When NP-DTS-PDA treatment was followed by laser irradiation, significant levels of karyolysis and apoptosis were observed. Eighty percent of the tumor cells died when this method which combines chemotherapy, gene therapy and photothermal therapy was used. NP-DTS-PDA had sensitized the cancer cells to chemotherapy.

This method shows significant results in reducing tumor size and almost no major side effects. The drugs are not toxic. All the results have proper evidence and data backing it up. One limitation is that this has only been tested in mouse. We do not yet know its affects and efficacy on humans.

Sacituzumab govitecan-hziy

Trop-2 is a human gene which plays a role in the proliferation of cells. This gene is overexpressed in tumor cells and helps in its proliferation. Sacituzumab govitecan-hziy is a drug that delivers high concentrations of the antibody SN-38 to tumor cells. Sn-38 is an antibody that targets Trop-2 and kills the cancer cells. In this study [7], 108 pretreated patients were given Sacituzumab govitecan-hziy (10 mg/kg). The response rate was 33.3% and the clinical benefit rate was 45.4%.

This method is the first FDA approved antibody-drug conjugate for TNBC treatment. It shows a good response rate over a diverse population and it was deemed safe and effective. There are some side effects including nausea, anemia, fatigue, and diarrhea. The median overall survival was 13 months which is a good number for this disease.

Combined CDK4/6 and PI3Kα Inhibition

CDKs are cyclin dependent kinase which helps regulating cell cycles. PI3Kα or Phosphoinositide 3-kinases are enzymes which helps in proliferation and growth of cells. Inhibiting both will increase apoptosis in cancer cells. In a study [8], the drugs BYL719, LEE011 and PD991 were given to mouse in different dosages to find out the immune response, safety and efficacy. The combination of BYL719 and LEE011 showed an increase in DNA damage and apoptosis of cancer cells.

The tumor volume decreased significantly with the drug combination. No side affects were mentioned in the study. As these tests were run in a mouse model, we do not know how humans will be affected from it. There is not enough evidence to back up the results.

Discussion

Immunotherapy showed a lot of adverse side effects while the results varied in the studies involving platinum-based chemotherapy. Even though these two techniques showed good disease control and response rate, these methods would not be advised due to its limitation. The combined drug inhibitors did not have much evidence backing it up too. The better treatments that we have for treating TNBC would be precision combination therapy and Sacituzumab govitecan-hziy. As the later is approved by the FDA and has good response rates, that will the preferred method now. Precision combination therapy has shown good results and proof. It has a lot of potential to be the best therapeutic option in few years. Overall, there is not much research on TNBC which is a very important field. Development in this area should be encouraged.

Conclusion

The advantages and limitations of five therapeutic techniques of triple negative breast cancer were reviewed. Sacituzumab govitecan-hziy is the best current technique among the five while precision combination therapy has promise to be the best in near future.

References

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