scholarly journals Application of Database and Data Science Techniques in the Malaysian Breast Cancer Survivorship Cohort STUDY

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 110s-110s
Author(s):  
M.D. Ganggayah ◽  
N.A. Taib ◽  
T. Islam ◽  
S.K. Dhillon
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Cancer Survivorship ◽  
Data Science ◽  
Psychosocial Support ◽  
Data Entry ◽  
Database Management System ◽  
Breast Cancer Patients ◽  
Breast Cancer Survivorship ◽  
Using Data

Background: Breast cancer is one of the leading cause of mortality among women worldwide. The Breast Cancer Resource Centre (BCRC) of University Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia, started the Malaysian Breast Cancer Survivorship Cohort (MyBCC) study in 2012. Aim: As a further enhancement of the research, the MyBCC database has been developed to conduct the survey in a convenient way, which aims to predict the factors influencing different survival rate among patients from multiethnic origin using data science techniques. Methods: The database comprised of life style related data of the patients including demographic factors, information on other illness, clinical factors, quality of life, psychosocial support, physical activity, work related questions, depression score, family background, type of medication consumed and financial status of the patients. This paper presents an approach to build an automated workflow using the MySQL database management system and PHP, integrated with R and HTML for web display. Results: A relational database comprising 816 breast cancer patients' data were developed for the MyBCC cohort study. This database serves as the backend for the MyBCC application where researchers can register new patients' records, update and view the information of recruited patients by using the system in a more commodious environment than before. Besides, the MyBCC database has been integrated with R programming tool by deploying the RMySQL package to perform audits. A few important analysis using plotly package, leveraging the integration of R with database are presented. Conclusion: In this paper, the development of the MyBCC database is presented, with the aim to automate the manual process of data entry, storage and analysis for performing audits for the breast cancer cohort study. The integration of database with R for automated analysis of data are also shown using examples of predictions that can be generated using functions in R. This fully automated workflow reduces the workload and time taken in performing manual predictions using data sources stored in flat files.

The menopause specialist and breast cancer survivorship

2016 ◽  
Vol 22 (4) ◽  
pp. 147-154
Author(s):  
Jo Marsden
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Survivorship ◽  
Survival Rates ◽  
Estrogen Deficiency ◽  
Treatment Result ◽  
Breast Cancer Patients ◽  
Breast Cancer Survivorship ◽  
Lifestyle Risk ◽  
Improvement In Survival

Due to improvement in survival rates, breast cancer is the most prevalent female malignancy in Europe and hence the management of breast cancer survivorship is garnering significant attention. Most of the health issues associated with treatment result from iatrogenic estrogen deficiency and recognition of this in the recent National Institute for Health and Care Excellence (NICE) menopause guidance has resulted in the recommendation for referral of breast cancer patients to menopause specialists for appropriate counselling about and management of early menopause, estrogen deficiency symptoms and lifestyle risk modification. The latter has significant implications for both all-cause and breast cancer-specific mortality. Extending the role of health professionals with an interest in menopause to provide such service for breast cancer patients is necessary as this is not within the remit or expertise of specialist breast cancer teams; however it will in turn, require menopause specialists to expand and regularly update their knowledge of breast cancer and its treatment.

The impact of arm lymphedema on healthcare utilization during long-term breast cancer survivorship: a population-based cohort study

2020 ◽  
Vol 14 (3) ◽  
pp. 347-355 ◽  
Author(s):  
Andrea Cheville ◽  
Minji Lee ◽  
Timothy Moynihan ◽  
Kathryn H. Schmitz ◽  
Mary Lynch ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Healthcare Utilization ◽  
Cancer Survivorship ◽  
Population Based ◽  
Breast Cancer Survivorship ◽  
Arm Lymphedema ◽  
The Impact

scholarly journals Clinical benefit of treatment after trastuzumab emtansine for HER2-positive metastatic breast cancer: a real-world multi-centre cohort study in Japan (WJOG12519B)

Breast Cancer ◽  
2021 ◽  
Author(s):  
Takamichi Yokoe ◽  
Sasagu Kurozumi ◽  
Kazuki Nozawa ◽  
Yukinori Ozaki ◽  
Tetsuyo Maeda ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Metastatic Breast Cancer ◽  
Cancer Patients ◽  
Real World ◽  
Treatment Strategies ◽  
Metastatic Breast ◽  
Trastuzumab Emtansine ◽  
Breast Cancer Patients ◽  
Her2 Positive

Abstract Background Trastuzumab emtansine (T-DM1) treatment for human epidermal growth factor receptor-2 (HER2)-positive metastatic breast cancer after taxane with trastuzumab and pertuzumab is standard therapy. However, treatment strategies beyond T-DM1 are still in development with insufficient evidence of their effectiveness. Here, we aimed to evaluate real-world treatment choice and efficacy of treatments after T-DM1 for HER2-positive metastatic breast cancer. Methods In this multi-centre retrospective cohort study involving 17 hospitals, 325 female HER2-positive metastatic breast cancer patients whose post-T-DM1 treatment began between April 15, 2014 and December 31, 2018 were enrolled. The primary end point was the objective response rate (ORR) of post-T-DM1 treatments. Secondary end points included disease control rate (DCR), progression-free survival (PFS), time to treatment failure (TTF), and overall survival (OS). Results The median number of prior treatments of post-T-DM1 treatment was four. The types of post-T-DM1 treatments included (1) chemotherapy in combination with trastuzumab and pertuzumab (n = 102; 31.4%), (2) chemotherapy concomitant with trastuzumab (n = 78; 24.0%), (3), lapatinib with capecitabine (n = 63; 19.4%), and (4) others (n = 82; 25.2%). ORR was 22.8% [95% confidence interval (CI): 18.1–28.0], DCR = 66.6% (95% CI 60.8–72.0), median PFS = 6.1 months (95% CI 5.3–6.7), median TTF = 5.1 months (95% CI 4.4–5.6), and median OS = 23.7 months (95% CI 20.7–27.4). Conclusion The benefits of treatments after T-DM1 are limited. Further investigation of new treatment strategies beyond T-DM1 is awaited for HER2-positive metastatic breast cancer patients.

scholarly journals Decision theory for precision therapy of breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael Kenn ◽  
Dan Cacsire Castillo-Tong ◽  
Christian F. Singer ◽  
Rudolf Karch ◽  
Michael Cibena ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Decision Theory ◽  
Data Science ◽  
Gene Expression Omnibus ◽  
Hormone Receptor Status ◽  
Breast Cancer Patients ◽  
Receptor Status ◽  
Kaplan Meier ◽  
Precision Therapy

AbstractCorrectly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

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