Non-invasive Lung Cancer Diagnosis and Prognosis Through Multimodal Clinical Data

Lung cancer remains the most prevalent cancer worldwide, yet challenges like late-stage diagnosis and limited treatment options continue to pose serious public health concerns. The persistently low five-year survival rate highlights the urgent need for more effective diagnostic and prognostic methods. In response, we propose MMLCA, a multimodal learning framework designed to predict EGFR mutation types and survival outcomes in lung cancer patients. MMLCA integrates diverse medical data sources, including lung CT images, clinical notes, laboratory results, and basic information, using a hierarchical cross-attention mechanism that captures complementary insights across modalities. By leveraging the full spectrum of available patient data, MMLCA significantly enhances predictive accuracy. Our experiments show that MMLCA consistently outperforms traditional approaches, suggesting it could help clinicians make more accurate predictions and support more personalized treatment decisions.