Technology Predictive Modeling for Patient Response in Cancer Immunotherapy: A Machine Learning Perspective

Cancer immunotherapy, particularly the use of immune checkpoint inhibitors (ICIs), has revolutionized oncology. However, patient response to these therapies is highly variable, creating an urgent need for robust predictive biomarkers. Machine learning (ML) offers a powerful framework for integrating high-dimensional, multi-modal data to develop predictive models that can guide clinical decision-making. This review provides a perspective on the application of ML in predicting patient response to cancer immunotherapy. We explore the critical data modalities, including genomic, transcriptomic, clinical, digital pathology, and medical imaging data, that serve as inputs for these models. We then survey the primary ML approaches being utilized, from classical supervised learning algorithms like Support Vector Machines and ensemble methods such as Random Forest and XGBoost, to advanced deep learning architectures. A comparative table of these models is presented. Furthermore, we discuss the significant challenges in the field, including model interpretability, data heterogeneity, and the necessity for prospective clinical validation. By transforming complex datasets into actionable predictions, ML-driven models hold immense promise for advancing precision oncology and personalizing immunotherapy treatment strategies for cancer patients.

Keywords:

Immunotherapy , Machine Learning , Predictive Modeling , Precision Oncology , Biomarkers , Deep Learning