A multi-criteria ranking algorithm (MCRA) for determining breast cancer therapy

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Hasan, Mostafa
Buyuktahtakin, Esra
Elamin, Elshami
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Breast cancer , Medical decision making , Patient-tailored treatment strategies , Risk factors , Inclusion and exclusion of treatment criteria , National comprehensive cancer network (NCCN) guidelines , Multi-criteria treatment ranking algorithm (MCRA) , Analytical hierarchy process (AHP) , Multi-criteria decision making , Decision support tools
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Hasan, Mostafa; Buyuktahtakin, Esra; Elamin, Elshami. 2019. A multi-criteria ranking algorithm (MCRA) for determining breast cancer therapy. Omega, vol. 82:pp 83-101

Breast cancer is the leading cause of cancer deaths among women. The selection of an effective, patient specific treatment plan for breast cancer has been a challenge for physicians because the decision process involves a vast number of treatment alternatives as well as treatment decision criteria, such as the stage of the cancer (e.g., in situ, invasive, metastasis), tumor characteristics, biomarker-related risks, and patient-related risks. Furthermore, every patient's case is unique, requiring a patient-specific treatment plan, while there is no standard procedure even for a particular stage of the breast cancer. In this paper, we first determine a comprehensive set of criteria for selecting the best breast cancer therapy by interviewing medical oncologists and reviewing the literature. We then present two analytical hierarchy process (AHP) models for quantifying the weights of criteria for breast cancer treatment in two sequential steps: primary and secondary treatment therapy. Using the weights of criteria from the AHP model, we propose a new multi-criteria ranking algorithm (MCRA), which evaluates a large variety of patient scenarios and provides the best patient-tailored breast cancer treatment alternatives based on the input of nine medical oncologists. We then validate the predictions of the multi-criteria ranking algorithm by comparing treatment ranks of the algorithm with ranks of five different oncologists, and show that algorithm rankings match or are statistically significantly correlated with the overall expert ranking in most cases. Our multi-criteria ranking algorithm could be used as an accessible decision-support tool to aid oncologists and educate patients for determining appropriate and effective treatment alternatives for breast cancer. Our approach is also general in the sense that it could be adapted to solve other complex decision-making problems in medicine, healthcare, as well as other service and manufacturing industries.

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