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Immunotherapy emerges as promising approach for treating cancer. Encouragingfindings have validated the efficacy of immunotherapy medications in addressingtumors, resulting in prolonged survival rates and notable reductions intoxicity compared to conventional chemotherapy methods. However, the pool ofeligible patients for immunotherapy remains relatively small, indicating a lackof comprehensive understanding regarding the physiological mechanismsresponsible for favorable treatment response in certain individuals whileothers experience limited benefits. To tackle this issue, the authors presentan innovative strategy that harnesses a non-linear cellular architecture inconjunction with a deep downstream classifier. This approach aims to carefullyselect and enhance 2D features extracted from chest-abdomen CT images, therebyimproving the prediction of treatment outcomes. The proposed pipeline has beenmeticulously designed to seamlessly integrate with an advanced embedded Pointof Care system. In this context, the authors present a compelling case studyfocused on Metastatic Urothelial Carcinoma (mUC), a particularly aggressiveform of cancer. Performance evaluation of the proposed approach underscores itseffectiveness, with an impressive overall accuracy of approximately 93%

Non-Linear Self Augmentation Deep Pipeline for Cancer Treatment outcome Prediction