AI-enabled molecular phenotyping and prognostic predictions in lung cancer through multimodal clinical information integration.

ABSTRACT: Lung cancer remains the leading cause of cancer-related mortality worldwide. The need for cost-effective, non-invasive methods to detect specific gene mutations for targeted therapy and predict patient survival outcomes underscores the importance of advancing diagnostic and prognostic capabilities. Contemporary lung cancer diagnostic models often fail to integrate diverse patient data, leading to incomplete clinical assessments. To address these challenges, we propose LUCID, a multimodal data integration framework designed to predict epidermal growth factor receptor (EGFR) mutation status and survival outcomes in patients with lung cancer. Tailored for early-stage clinical assessment, LUCID leverages lung computed tomography (CT) images, chief complaints, laboratory test results, and demog

SUBMITTER: Lu Y 

PROVIDER: S-EPMC12281446 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature