TY - JOUR
T1 - A multi-criteria ranking algorithm (MCRA) for determining breast cancer therapy
AU - Hasan, Mostafa
AU - Büyüktahtakın, Esra
AU - Elamin, Elshami
N1 - Funding Information:
We gratefully acknowledge the support of the National Science Foundation CAREER Award under Grant # CBET-1554018 and Flossie E. West Memorial Foundation Award. We thank medical oncologists at Kansas Cancer Care Centers including Drs. Mark Fesen, Greg Nanney, Cinderella Chavez, Restituto Tibayan, Anis Toumeh, Jose Velasco, Jocelyn De Yao and Robert Rodriguez for their expert opinion and valuable input into our paper. We are also grateful to two anonymous referees and the editor for their constructive comments, which have improved the exposition and clarity of this paper.
Funding Information:
We gratefully acknowledge the support of the National Science Foundation CAREER Award under Grant # CBET-1554018 and Flossie E. West Memorial Foundation Award. We thank medical oncologists at Kansas Cancer Care Centers including Drs. Mark Fesen, Greg Nanney, Cinderella Chavez, Restituto Tibayan, Anis Toumeh, Jose Velasco, Jocelyn De Yao and Robert Rodriguez for their expert opinion and valuable input into our paper. We are also grateful to two anonymous referees and the editor for their constructive comments, which have improved the exposition and clarity of this paper.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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.
KW - Analytical hierarchy process (AHP)
KW - Breast cancer
KW - Decision support tools
KW - Inclusion and exclusion of treatment criteria
KW - Medical decision making
KW - Multi-criteria decision making
KW - Multi-criteria treatment ranking algorithm (MCRA)
KW - National comprehensive cancer network (NCCN) guidelines
KW - Patient-tailored treatment strategies
KW - Risk factors
UR - http://www.scopus.com/inward/record.url?scp=85039753371&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039753371&partnerID=8YFLogxK
U2 - 10.1016/j.omega.2017.12.005
DO - 10.1016/j.omega.2017.12.005
M3 - Article
AN - SCOPUS:85039753371
SN - 0305-0483
VL - 82
SP - 83
EP - 101
JO - Omega (United Kingdom)
JF - Omega (United Kingdom)
ER -