Minimizing metastatic risk in radiotherapy fractionation schedules.
Metastasis is the process by which cells from a primary tumor disperse and form new tumors at distant anatomical locations. The treatment and prevention of metastatic cancer remains an extremely challenging problem. This work introduces a novel biologically motivated objective function to the radiation optimization community that takes into account metastatic risk instead of the status of the primary tumor. In this work, we consider the problem of developing fractionated irradiation schedules that minimize production of metastatic cancer cells while keeping normal tissue damage below an acceptable level. A dynamic programming framework is utilized to determine the optimal fractionation scheme. We evaluated our approach on a breast cancer case using the heart and the lung as organs-at-risk (OAR). For small tumor [Formula: see text] values, hypo-fractionated schedules were optimal, which is consistent with standard models. However, for relatively larger [Formula: see text] values, we found the type of schedule depended on various parameters such as the time when metastatic risk was evaluated, the [Formula: see text] values of the OARs, and the normal tissue sparing factors. Interestingly, in contrast to standard models, hypo-fractionated and semi-hypo-fractionated schedules (large initial doses with doses tapering off with time) were suggested even with large tumor α/β values. Numerical results indicate the potential for significant reduction in metastatic risk.
Industrial and Systems Engineering, University of Minnesota, Minneapolis MN, USA.
Hamidreza
H
Badri
Jagdish
J
Ramakrishnan
Kevin
K
Leder
D001943:Breast Neoplasms / Q000532:radiotherapy* / Q000556:secondary; D019583:Dose Fractionation, Radiation; D005260:Female; D006321:Heart / Q000528:radiation effects*; D006801:Humans; D008168:Lung / Q000528:radiation effects*; D015233:Models, Statistical; D009362:Neoplasm Metastasis; D058958:Organs at Risk / Q000528:radiation effects*; D011880:Radiotherapy Planning, Computer-Assisted