Development and validation of a multi-task deep learning model integrating PET-CT radiomics, clinical variables, and EBV DNA for prognostic prediction in locally advanced nasopharyngeal carcinoma

Accurate prognostic prediction in locally advanced nasopharyngeal carcinoma (LA-NPC) remains challenging due to tumor heterogeneity and complex treatment responses. Existing prognostic models relying on single imaging modalities or traditional machine learning algorithms inadequately capture the complex non-linear interactions among metabolic, anatomical, and molecular biomarkers, and fail to leverage the synergistic information between functional metabolic data from positron emission tomography (PET) and anatomical structural information from computed tomography (CT). This study developed and validated a multi-task deep learning (MTDL) model integrating PET-CT imaging, clinical variables, and Epstein-Barr virus (EBV) DNA to predict overall survival (OS) and progression-free survival (PFS) in LA-NPC patients.