Deep neural networks for neuro-oncology: Towards patient individualized design of chemo-radiation therapy for Glioblastoma patients.

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.

A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients.

Background And Objectives: Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival.

Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide.

Background: Glioblastoma multiforme (GBM) is the most frequent primary brain tumor in adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In Silico models of GBM growth provide an appropriate foundation for analysis and comparison of different regimens. We propose a mathematical frame for patient specific design of optimal chemotherapy regimens for GBM patients.