Authors: Ritu Jain
Abstract: Immunotherapy has revolutionized cancer treatment by harnessing the immune system to recognize and eliminate malignant cells. However, despite its promising outcomes, patient responses to immunotherapy are highly heterogeneous, with many experiencing minimal benefits or adverse reactions. Accurately predicting which patients will respond positively is a critical challenge for clinicians aiming to tailor treatments effectively. Machine learning (ML), a branch of artificial intelligence capable of analyzing complex, high-dimensional datasets, has emerged as a powerful tool to develop predictive models that can forecast patient responses to immunotherapy. This paper explores the diverse ML techniques applied to immunotherapy response prediction, the integration of multi-omics and clinical data, the challenges faced in clinical translation, and future opportunities for advancing personalized cancer therapy through ML-driven insights.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.559
