Determining an effective treatment plan for breast cancer: A multi-criteria decision model and algorithm
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Breast cancer is the second leading cause of cancer deaths in U.S. women. According to the American Cancer Society (2016a), an estimated 246,660 women would be diagnosed with invasive breast cancer, and more than 40,450 is estimated to die from this disease in 2016 alone. 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 critical factors such as the stage of the cancer (e.g., in situ, invasive, metastasis), risk factors related to breast cancer, biomarker related risks, and patient-related risks. In this thesis, a comprehensive set of criteria for selecting the best breast cancer therapy plan is determined by reviewing the literature and interviewing medical oncologists. Also, this work provides discussion of two analytical hierarchy process (AHP) models for quantifying the weights of criteria and subcriteria for breast cancer treatment in two sequential steps: primary and secondary treatment therapy. Using the weights of criteria and subcriteria from the AHP model, this work proposes a new multi-criteria treatment ranking algorithm, which evaluates every possible scenario and provides the best patient-tailored breast cancer treatment alternatives. This work also validates 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 output ranking matches or is statistically significantly correlated with the weighted overall expert ranking in most cases. The ease of calculations in the ranking algorithm with Microsoft Excel provides a significant computational benefit for the practitioners. Thus, our multi-criteria ranking algorithm could be used as an accessible decisionsupport tool to aid oncologists and educate patients for determining appropriate and effective treatment alternatives for breast cancer. Our multi-criteria ranking 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 industries.
Thesis (M.S)-- Wichita State University, College of Engineering, Department of Industrial and Manufacturing Engineering