New Approach for Predicting Optimal Neoadjuvant Treatment in Breast Cancer Patients Using a Dynamic Treatment Graph

Abstract

Multi-omics approaches play a crucial role in breast cancer treatment planning by providing a comprehensive view of the molecular underpinnings of the disease this information is invaluable for tailoring treatment strategies to individual patients, improving treatment outcomes, and advancing our understanding of breast cancer biology. The selection of an appropriate; treatment strategy is crucial and can be challenging, as it depends on various factors such as the stage of the disease, patient health, and the response to previous treatments. In recent times, there has been a; growing interest in the development of decision-making systems aimed at assisting oncologists in the selection of the most appropriate treatment strategies. Data mining techniques, specifically Reinforcement Learning (RL) and Recurrent Neural Networks (RNN), have gained widespread usage in enhancing the precision of breast cancer treatment response predictions. These techniques enable the extraction of valuable insights from extensive datasets, unveiling intricate relationships among various aspects of the disease and potential treatments. Within this study, we introduce an innovative approach grounded in a dynamic treatment graph and the application of RL and RNN. This approach excels in its capacity to anticipate the optimal treatment based on a patient’s historical treatment experiences and responses. The central goal of implementing the dynamic treatment graph is to provide valuable guidance to oncologists in formulating the most effective therapeutic protocols while concurrently investigating the intricate interplay between treatment responses. The effectiveness of our prediction; model is thoroughly evaluated using a diverse array of performance metrics.