Multi-Modal Deep Learning Framework for Personalized Treatment Decision Support in Early-Stage Non-Small Cell Lung Cancer

Personalized treatment for early-stage non-small cell lung cancer (NSCLC), particularly in choosing between SBRT and surgery, is challenging due to complex, heterogeneous patient data. We introduce MM-Care, a novel deep learning framework for objective, interpretable, and personalized treatment decision support. MM-Care integrates patient-specific CT imaging, clinical indicators, and genomic data through a sophisticated multi-branch neural network. Its core innovations include multi-modal feature extraction, an adaptive Transformer-based fusion network for deep inter-modal interaction, and a dual-task prediction head for overall survival and local control across both interventions. An explainable decision report module, utilizing feature importance methods, enhances clinical trust. Evaluated on public and proprietary cohorts comprising thousands of patients, MM-Care consistently outperforms traditional models and deep learning baselines. Our experiments demonstrate superior prognostic performance for survival and local control. Ablation studies validate critical architectural contributions. Human evaluation with oncologists confirms high trust, utility, and interpretability, showing significant time savings and strong agreement with expert consensus. MM-Care also achieves high accuracy in aligning with retrospectively identified optimal treatment choices. These results highlight MM-Care’s robust capability to provide precise, patient-specific prognostic predictions and optimal treatment recommendations, poised to significantly enhance personalized medicine in early-stage NSCLC.