{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:17:42Z","timestamp":1788221862885,"version":"build-2803163510"},"reference-count":43,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"2","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Predicting recurrence of pancreatic cancer after surgery could inform clinical decision making, including adjuvant therapies and follow-up. This study aimed to develop and validate a deep learning model using digitized whole-slide images (WSI) of histopathology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>Publicly available WSI of pancreatic ductal adenocarcinoma resections from three cohorts were used for training. The model consisted of a pan-cancer foundation model to generate embeddings, mean-pooling across tissue patches, and then a fully connected neural network. Model predictions were compared with human-labeled histopathologic features and genomic alterations. The model was externally validated in a meta-analysis of a single-center cohort from Princess Margaret Cancer Centre, a multicenter cohort from France, and the PRODIGE 24 trial of adjuvant chemotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      The deep learning model was trained on 12,594 tissue patches from 257 patients. High-risk classifications were associated with squamous morphology, reactive stroma, tumor cellularity, and necrosis, whereas low-risk classifications were associated with tubulopapillary and conventional morphologies, as well as deserted stroma. High-risk cancers were enriched for basal-like gene expression profiles and distinct oncogenic pathways. In a meta-analysis of the external cohorts, the hazard ratio (HR) for death comparing high-versus low-risk cancers was 1.49 (95% CI, 1.25 to 1.79,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001), whereas the HR for recurrence or death was 1.41 (95% CI, 1.19 to 1.68,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001). The classifications remained prognostic among moderately differentiated cancers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>An open-source deep learning model using WSI from pancreatic cancer resections generated risk classifications that correlated with histopathologic and genomic features. Classifications were externally validated in a meta-analysis of three cohorts. This model could be applied to WSI to provide individualized prognostic information for patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-25-00268","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:00:45Z","timestamp":1777492845000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning of Histopathology Predicts Outcomes After Surgery for Pancreatic Cancer"],"prefix":"10.1200","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8727-3375","authenticated-orcid":true,"given":"Avelyn","family":"Wong","sequence":"first","affiliation":[{"name":"Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada"},{"name":"PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, Ontario, Canada"},{"name":"Vector Institute for Artificial Intelligence, 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