Knowledge-Driven Neuro-Symbolic Reasoning for Personalized Oncology Treatment Recommendation Based on Multi-Modal Medical Knowledge Graph

Personalized oncology treatment recommendation is a critical clinical task that requires in-tegrating complex, multi-modal patient data with established medical knowledge to ensure both accuracy and safety. While deep learning models excel at capturing latent patterns from high-dimensional data, their opaque decision-making processes and inability to strictly enforce clinical constraints hinder their adoption in high-stakes medical domains. Conversely, traditional rule-based systems offer high interpretability but struggle to scale with complex, heterogeneous data. To address these challenges, we propose the Knowledge-driven Neuro-Symbolic Network (K-NeSyNet), a novel framework for personalized oncology treatment recommendation. K-NeSyNet is grounded in a newly constructed Multi-Modal Oncology Knowledge Graph (MM-OKG) that unifies genomic mutations, medical imaging features, clinical text, and structured medical guidelines from publicly available sources including TCGA, DGIdb, KEGG, and NCCN guidelines. The core innovation of K-NeSyNet is a three-channel differentiable symbolic reasoning mechanism that explicitly models guideline recommendations, mutation-target matching, and contraindication penalties for each patient-drug pair. These symbolic signals are dynamically fused with the outputs of a knowledge-aware graph attention neural reasoning module via an adaptive gated fusion network. Crucially, the fusion gate learns to balance neural and symbolic confidence in a patient-specific manner, while contraindication evidence enters both the symbolic score and a dedicated safety objective to down-weight clinically risky drugs. Extensive experiments on a real-world multi-modal oncology dataset comprising 4,781 patients across 10 cancer types demonstrate that K-NeSyNet consistently outperforms eight state-of-the-art baselines. Specifically, K-NeSyNet achieves the highest F1@10 of 0.9227, NDCG@10 of 0.9656, and Jaccard similarity of 0.9366, while maintaining competitive Clinical Guideline Consistency. Ablation studies confirm the indispensable role of each component, with the removal of the fusion gate causing the most significant performance degradation. Furthermore, K-NeSyNet provides transparent, score-decomposed explanations for its recommendations, offering a crucial step toward trustworthy AI-assisted clinical decision support.