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

TL;DR

This paper introduces a novel deep learning pipeline utilizing non-linear self-augmentation to improve the prediction of cancer treatment outcomes from CT images, specifically targeting immunotherapy response in metastatic urothelial carcinoma.

Contribution

It presents an innovative non-linear cellular architecture combined with a deep classifier for enhanced feature selection and outcome prediction in cancer immunotherapy.

Findings

Achieved approximately 93% accuracy in predicting treatment outcomes.

Effectively selected and enhanced 2D features from CT images.

Demonstrated integration with Point of Care systems for real-world application.

Abstract

Immunotherapy emerges as promising approach for treating cancer. Encouraging findings have validated the efficacy of immunotherapy medications in addressing tumors, resulting in prolonged survival rates and notable reductions in toxicity compared to conventional chemotherapy methods. However, the pool of eligible patients for immunotherapy remains relatively small, indicating a lack of comprehensive understanding regarding the physiological mechanisms responsible for favorable treatment response in certain individuals while others experience limited benefits. To tackle this issue, the authors present an innovative strategy that harnesses a non-linear cellular architecture in conjunction with a deep downstream classifier. This approach aims to carefully select and enhance 2D features extracted from chest-abdomen CT images, thereby improving the prediction of treatment outcomes. The proposed pipeline has been meticulously designed to seamlessly integrate with an advanced embedded Point of Care system. In this context, the authors present a compelling case study focused on Metastatic Urothelial Carcinoma (mUC), a particularly aggressive form of cancer. Performance evaluation of the proposed approach underscores its effectiveness, with an impressive overall accuracy of approximately 93%