LAUSR

BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and deadly type of primary cancers of the brain and central nervous system in adults. Despite the importance of designing a personalized treatment regimen for the patient, clinical trials prescribe a set of conventional regimens for GBM patients. We propose a computerized framework for designing chemo-radiation therapy (CRT) regimen based on patient characteristics. METHODS An intelligent agent, based on deep reinforcement learning, interacts with a virtual personalized GBM. The proposed deep Q network (DQN) uses a deep neural network to estimate the state - action value function. The algorithm stores agent experiences in a replay memory to be used for training of the deep neural network. Also, the proliferation-invasion model is used to simulate spatiotemporal dynamics of GBM growth and its response to therapeutic agents. RESULTS Assuming tumor size at the end of the treatment course as a measure of the quality of the treatment regimen, experiments show that the proposed DQN is superior to the Q learning. Also, while the quality of the protocols obtained by the Q learning as well as its convergence speed decreases sharply with the increase in the dimensions of the state-action value function, the DQN is relatively robust against increasing the initial tumor size or lengthening the treatment period. CONCLUSION Our results suggest that the optimal personalized treatment regimen may differ from the conventional regimens suggested by clinical trials. Given the scalability of the proposed DQN in designing treatment regimen for real size tumors, as well as its superiority over previous models, it is a suitable tool for designing personalized CRT regimen for GBM patients.