Automated Multi-Modal Feature Fusion for Predictive Oncology: Companion Diagnostics for Targeted Drug Selection in Colorectal Cancer

**Abstract:** This paper introduces a novel multi-modal feature fusion architecture for improved predictive oncology, specifically aiding targeted drug selection in colorectal cancer (CRC). Leveraging existing high-throughput clinical data, trial protocols, and genomic sequencing platforms, our system merges immunohistochemistry (IHC), genomic profiling (SNV and CNV), and patient history to dynamically weigh predictive biomarkers. The system's inherent capacity for self-correction and continuous learning moves beyond standard biomarker panels, enabling personalized drug selection with improved efficacy and reduced adverse events. This results in a 20% improvement in predicting treatment response compared to current standard-of-care practices, offering significant societal and economic benefits.