Toward Clinically Actionable Prediction of Recurrence and Metastatic Progression in Pancreatic Ductal Adenocarcinoma: A Systematic Review.
Authors
Affiliations (3)
Affiliations (3)
- Department of Medicine, Columbia University Irving Medical Center, New York, NY.
- Department of Radiology, Columbia University Irving Medical Center, New York, NY.
- Center for Advanced Diagnostic Research, Columbia University Irving Medical Center, New York, NY.
Abstract
Pancreatic ductal adenocarcinoma (PDAC) has high rates of early recurrence (ER), occult metastasis, and poor survival despite curative-intent therapy. Predictive factors and models span clinical, biomarker, imaging, radiomics, pathologic, and molecular domains, but clinical readiness remains uncertain. We performed a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Five databases were searched for studies evaluating predictors or models of PDAC recurrence, metastasis, lymph node metastasis (LNM), occult metastatic disease, disease-free survival (DFS) or recurrence-free survival, or recurrence patterns. Studies were screened and coded for end point, modality, model type, validation, discrimination, calibration, clinical utility, and predictor timing. Of 234 full-text records, 208 studies were included, comprising 144 prediction models and 64 predictor/prognostic factor studies. Computed tomography/radiomics (141/208), clinical/electronic health record variables (104/208), and serum/urine biomarkers (46/208) were the most common modalities. External validation, calibration, and decision-curve analyses were reported in 52, 36, and 38 model studies, respectively. AUCs ranged from 0.70 to 0.92 for ER, 0.75-0.95 for LNM, and 0.71-0.88 for liver or occult metastasis; DFS/relapse-free survival models had C-indices of 0.62-0.82. One externally validated occult-metastasis model had a C-statistic of 0.56. Model maturity was limited by retrospective design, heterogeneous end points, incomplete calibration reporting, and postoperative variables in preoperative models. Prediction models for PDAC recurrence and metastatic progression are rapidly expanding, particularly through radiomics, machine learning, and multimodal modeling. Clinical translation requires decision-aligned design, transparent reporting, external validation, calibration, interpretability, and prospective evaluation of clinical utility.