Advanced Deep Network With Attention And Genetic-Driven Reinforcement Learning Layer For An Efficient Cancer Treatment Outcome Prediction

In the last few years, medical researchers have investigated promising approaches for cancer treatment, leading to a major interest in the immunotherapeutic approach. The target of immunotherapy is to boost a subject's immune system in order to fight cancer. However, scientific studies confirmed that not all patients have a positive response to immunotherapy treatment. Medical research has long been engaged in the search for predictive immunotherapeutic-response bio-markers. Based on these considerations, we developed a non-invasive advanced pipeline with a downstream 3D deep classifier with attention and reinforcement learning for early prediction of patients responsive to immunotherapeutic treatment from related chest-abdomen CT-scan imaging. We have tested the proposed pipeline within a clinical trial that recruited patients with metastatic bladder cancer. Our experiment results achieved accuracy close to 93\%.