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Predictive factors and models span clinical, biomarker, imaging, radiomics, pathologic, and molecular domains, but clinical readiness remains uncertain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-26-00200","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T20:00:16Z","timestamp":1791921616000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Toward Clinically Actionable Prediction of Recurrence and Metastatic Progression in Pancreatic Ductal Adenocarcinoma: A Systematic Review"],"prefix":"10.1200","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9315-7231","authenticated-orcid":true,"given":"Liyuan","family":"Gong","sequence":"first","affiliation":[{"name":"Department of Medicine, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3262-0853","authenticated-orcid":true,"given":"Cadence","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Medicine, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice","family":"Agyekum","sequence":"additional","affiliation":[{"name":"Department of Medicine, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0339-834X","authenticated-orcid":true,"given":"Nitish","family":"Aswani","sequence":"additional","affiliation":[{"name":"Department of Medicine, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4407-8566","authenticated-orcid":true,"given":"Jeong Yun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Medicine, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Shahinnian","sequence":"additional","affiliation":[{"name":"Department of Medicine, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9817-2414","authenticated-orcid":true,"given":"Gulam","family":"Manji","sequence":"additional","affiliation":[{"name":"Department of Medicine, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2402-3787","authenticated-orcid":true,"given":"Stella K.","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Columbia University Irving Medical Center, New York, NY"},{"name":"Center for Advanced Diagnostic Research, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2819-7576","authenticated-orcid":true,"given":"Chin","family":"Hur","sequence":"additional","affiliation":[{"name":"Department of Medicine, Columbia University Irving Medical Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"233","reference":[{"key":"e_1_3_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejso.2023.04.009"},{"key":"e_1_3_8_3_2","doi-asserted-by":"publisher","DOI":"10.1097\/MS9.0000000000004919"},{"key":"e_1_3_8_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.surg.2025.109647"},{"key":"e_1_3_8_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16223803"},{"key":"e_1_3_8_6_2","first-page":"e1911","article-title":"Biological risk based on preoperative serum CA19\u20109 and histological grade predicts prognosis and improves accuracy of classification in patients with pancreatic ductal adenocarcinoma","volume":"6","author":"Chang S","year":"2023","unstructured":"Chang S, Liu Y, Liang Y, et al: Biological risk based on preoperative serum CA19\u20109 and histological grade predicts prognosis and improves accuracy of classification in patients with pancreatic ductal adenocarcinoma. 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