FOUNDATION MODEL–DRIVEN PRECISION ONCOLOGY: INTEGRATING MULTI-OMICS, RADIOLOGY, AND CLINICAL DATA FOR PREDICTIVE CANCER CARE

Abstract

Foundation models represent a paradigm shift in precision oncology by enabling integrated analysis of heterogeneous cancer data modalities. This research develops and validates a comprehensive foundation model framework that synthesizes multi-omics profiles, radiological imaging, and clinical records to predict treatment responses and patient outcomes. We collected data from 428 cancer patients across multiple tumor types, including whole genome sequencing, transcriptomic profiles, proteomic data, CT and MRI imaging, and longitudinal clinical histories. The foundation model architecture employs transformer-based encoders for each data modality with cross-attention mechanisms enabling information fusion across disparate data types. Our experimental implementation achieved 87% accuracy in predicting immunotherapy response, 82% accuracy in chemotherapy outcome prediction, and identified novel biomarker combinations missed by single-modality approaches. The model demonstrated superior performance compared to traditional machine learning methods, with particularly strong results in rare cancer subtypes where limited training data typically constrains predictive accuracy. Integration of radiological features with genomic data improved prediction accuracy by 23% over genomics alone, highlighting the value of multimodal fusion. These findings demonstrate that foundation models can transform precision oncology by leveraging the complementary information contained across diverse data modalities to guide personalized treatment selection and improve patient outcomes.