Machine Learning Models for Predicting Treatment Response in Cancer Patients: Developing machine learning models to predict the response of cancer patients to different treatment modalities

Abstract

Cancer treatment efficacy varies widely among patients due to individual differences in genetics, lifestyles, and tumor biology. Predicting treatment response is crucial for optimizing patient outcomes and minimizing unnecessary side effects. Machine learning (ML) models offer a promising approach to personalize cancer treatment by predicting response to different modalities. This paper reviews the current landscape of ML models in predicting treatment response in cancer patients and proposes a novel framework for developing robust prediction models. We present a comprehensive overview of the key challenges and opportunities in this field, highlighting the importance of integrating diverse data sources and leveraging advanced ML techniques. Our framework emphasizes the need for transparent and interpretable models to facilitate clinical decision-making. We demonstrate the utility of ML models through case studies in breast, lung, and colorectal cancer, showcasing their potential to improve treatment outcomes and patient quality of life. Finally, we discuss future directions and the potential impact of ML in personalized cancer treatment.