A risk-based modeling approach for radiation therapy treatment planning under tumor shrinkage uncertainty |
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| Affiliation: | 1. Department of Industrial Engineering, University of Houston, 4800 Calhoun Road, Houston, TX 77204, USA;2. PROS Revenue Management, Houston, TX 77002, USA;3. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;1. Carey Business School, Johns Hopkins University, 100 International Drive, Baltimore, MD 21202, United States;2. Department of Information Systems and Business Analytics, Florida International University, Miami, FL 33199, United States;1. College of Auditing and Evaluation, Nanjing Audit University, Nanjing, Jiangsu Province, 211815, China;2. Schulich School of Business, York University, Toronto, Ontario M3J 1P3, Canada;3. Foisie Business School, Worcester Polytechnic Institute, Worcester, MA 01609, USA;1. Department of Finance, National Central University, Taiwan;2. Department of Finance, National Taiwan University, Taiwan;3. Department of International Business, National Taiwan University, Taiwan;4. Chinese Academy of Mathematics and Systems Science, China;1. School of Business, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA;2. Lally School of Management, Rensselaer Polytechnic Institute, 110 8th Street, Pittsburgh Building, Troy, NY 12180, USA;3. Division of Economic and Risk Analysis, US Securities and Exchange Commission, 100 F St NE, Washington DC 20549, USA;4. Department of Electrical, Computer & Systems Engineering, Rensselaer Polytechnic Institute, Jonsson Engineering Center 6048, Troy, NY 12180, USA;1. Leiden University Mathematical Institute, Niels Bohrweg 1, 2333 CA, Leiden, NL, UK;2. Department of Management Science, Center for Transportation and Logistics, Lancaster University Management School, Bailrigg, Lancaster LA1 4YX, UK;1. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA;2. School of Information Science and Technology, Osaka University, Suita, Japan;3. Department of Economics, Rice University, Houston, TX, USA;4. Department of Economics, University of Arkansas, Fayetteville, AR, USA |
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| Abstract: | Robust optimization approaches have been widely used to address uncertainties in radiation therapy treatment planning problems. Because of the unknown probability distribution of uncertainties, robust bounds may not be correctly chosen, and a risk of undesirable effects from worst-case realizations may exist. In this study, we developed a risk-based robust approach, embedded within the conditional value-at-risk representation of the dose-volume constraint, to deal with tumor shrinkage uncertainty during radiation therapy. The objective of our proposed model is to reduce dose variability in the worst-case scenarios as well as the total delivered dose to healthy tissues and target dose deviations from the prescribed dose, especially, in underdosed scenarios. We also took advantage of adaptive radiation therapy in our treatment planning approach. This fractionation technique considers the response of the tumor to treatment up to a particular point in time and reoptimizes the treatment plan using an estimate of tumor shrinkage. The benefits of our model were tested in a clinical lung cancer case. Four plans were generated and compared: static, nominal-adaptive, robust-adaptive, and conventional robust (worst-case) optimization. Our results showed that the robust-adaptive model, which is a risk-based model, achieved less dose variability and more control on the worst-case scenarios while delivering the prescribed dose to the tumor target and sparing organs at risk. This model also outperformed other models in terms of tumor dose homogeneity and plan robustness. |
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