Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach

BMC Cancer

Research Article

Christina Teng¹  Adrian Pokorny¹  Richard J. Epstein²  Frank P. Y. Lin²  Rachel Dear³ 

[1] Department of Oncology, St Vincent’s Hospital, The Kinghorn Cancer Centre, 370 Victoria St, Darlinghurst, Sydney, Australia;Department of Oncology, St Vincent’s Hospital, The Kinghorn Cancer Centre, 370 Victoria St, Darlinghurst, Sydney, Australia;Garvan Institute of Medical Research, Sydney, Australia;The University of New South Wales, Sydney, NSW, Australia;Department of Oncology, St Vincent’s Hospital, The Kinghorn Cancer Centre, 370 Victoria St, Darlinghurst, Sydney, Australia;The University of Sydney, Sydney, NSW, Australia;

关键词: Breast cancer;    Cytotoxic drug therapy;    Decision analysis;    Machine learning;    Clinical decision support system;   

DOI  :  10.1186/s12885-016-2972-z

 received in 2016-02-02, accepted in 2016-11-24,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundMultidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments.MethodsWe analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines.ResultsMachine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p < 0.01, representing 43% and 53% variations from ESMO/NCCN guidelines, respectively). Using ten-fold cross-validation, the best classifiers achieved areas under the receiver operating characteristic curve (AUC) of 0.940 for chemotherapy (95% C.I., 0.922—0.958), 0.899 for the endocrine therapy (95% C.I., 0.880—0.918), and 0.977 for trastuzumab therapy (95% C.I., 0.955—0.999) respectively. Overall, bootstrap aggregated classifiers performed better among all evaluated machine learning models.ConclusionsA machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.

【 授权许可】

CC BY   
© The Author(s). 2016