Determining an effective treatment plan for breast cancer: A multi-criteria decision model and algorithm
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Issue Date
2016-12
Embargo End Date
Authors
Hasan, Mostafa
Advisor
Buyuktahtakin, Esra
Citation
Abstract
Table of Content
Description
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
Thesis (M.S)-- Wichita State University, College of Engineering, Department of Industrial and Manufacturing Engineering