PDAC remains one of the leading causes of cancer-related mortality, largely due to late diagnosis and limited predictive tools for treatment response. EUS represents the most sensitive modality for detecting pancreatic lesions and allows tissue acquisition for histological confirmation.
Recent advances in AI, including machine learning (ML) and deep learning (DL), have demonstrated strong potential in improving diagnostic accuracy, prognostic stratification, and prediction of treatment response in oncology.
The EUS-AI-R study aims to integrate multimodal data, including EUS imaging, clinical variables, radiological imaging, digital histopathology, and molecular data, into AI-based predictive models capable of estimating chemotherapy response and survival outcomes in PDAC patients.
The study consists of two phases:
* Retrospective phase: development, training, and internal validation of AI models using approximately 500 patients from an institutional database.
* Prospective phase: application of the selected model to an independent cohort (200 patients) to assess feasibility, calibration, and real-world performance.
Multiple modality-specific models (EUS-based, clinical-based, radiology-based, pathology-based) and a multimodal integrated model will be developed and compared. Model performance will be evaluated using AUC-ROC, sensitivity, specificity, calibration, and concordance index for survival outcomes.
The study is observational and does not modify standard clinical practice.
