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This study aimed to develop and validate an integrated radiomic\u2010pathology (Rad\u2010Path) model for ER prediction and to elucidate its underlying biological mechanisms.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods<\/jats:bold>\n                    : A retrospective cohort of 225 PDAC patients who underwent R0 resection was included. Preoperative CT images and whole\u2010slide images (WSI) were collected for the extraction of radiomic features and computational pathology features. Selected features were used to develop 11 distinct machine learning models. The SHapley Additive exPlanations (SHAP) algorithm was employed to evaluate feature importance. Single\u2010cell RNA sequencing (scRNA\u2010seq) and spatial transcriptomics (ST) were performed on prospectively collected specimens.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results<\/jats:bold>\n                    : The final Rad\u2010Path model achieved AUCs of 0.851 and 0.814 in the internal and external validation cohorts, respectively. The predicted ER group was specifically linked to the enrichment of fibroblasts and pancreatic stellate cells, as well as dysregulation in extracellular matrix (ECM)\u2010related pathways. This finding was validated histopathologically, as predicted ER patients predominantly displayed a \u201creactive\u2010dominant\u201d phenotype marked by abundant activated fibroblasts and ECM deposition.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion<\/jats:bold>\n                    : Our study offers a high\u2010performance predictive model for ER in PDAC and establishes ECM remodeling as a key biological mechanism underlying the predictions.\n                  <\/jats:p>","DOI":"10.1002\/advs.202523985","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:12:28Z","timestamp":1776075148000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6952-2064","authenticated-orcid":false,"given":"Sihang","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Radiology Peking Union Medical College Hospital Chinese Academy of Medical Sciences &amp; 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