A Multimodal Big Data and Explainable AI Framework for Personalized Cancer Care: Extending Methods for Clinical Translation

Abstract

Recent advancements in big data analytics and artificial intelligence (AI) have revolutionized precision oncology, enabling the prediction of therapy responses, patient stratification, and the personalization of treatment courses. This study expands upon the methodologies and results presented in "Leveraging Big Data Analytics for Personalized Cancer Treatment: An Overview of Current Approaches and Future Directions" (Journal of Engineering, 2025) by formulating and implementing an innovative framework for multimodal, data-driven cancer care. The system amalgamates genetic, transcriptomic, imaging, clinical, and patient-reported data streams with machine learning models, causal inference methodologies, and explainable AI to produce personalized treatment-effect predictions. The suggested approach, named OncoSage, exhibits enhanced stability, predictive accuracy, and interpretability when evaluated against benchmark datasets such as TCGA, METABRIC, and TCIA, surpassing traditional models. Significant contributions encompass schema-first data governance, uncertainty quantification using conformal prediction, target-trial emulation for treatment impact estimation, and fairness-aware monitoring in federated environments. The findings underscore the clinical relevance of explainable big-data pipelines in oncology, providing clear and ethically sound decision assistance that connects computational capabilities with practical implementation in clinical settings. This study enhances the expanding field of translational cancer informatics by offering a replicable, therapeutically pertinent, and governance-oriented framework for future customized oncology systems.