{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:30:33Z","timestamp":1793125833699,"version":"4.5.0"},"reference-count":32,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"26","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["J Clin Oncol"],"published-print":{"date-parts":[[2026,9,10]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Predictive biomarkers to guide selection of first-line chemotherapy for advanced pancreatic ductal adenocarcinoma (PDAC) are an unmet clinical need. This study used the Computational Histology Artificial Intelligence (CHAI) platform to develop and validate a histomorphology-based G-chemo versus F-chemo (GvF) biomarker that predicts benefit from first-line fluoropyrimidine-based (F-chemo) versus gemcitabine-based (G-chemo) regimens.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>The CHAI platform extracted quantitative histomorphologic features from whole-slide images of hematoxylin and eosin\u2013stained diagnostic biopsies. In a multi-institutional development cohort, features associated with differential outcomes as measured by time to next treatment or death (TNTD) between F-chemo\u2013treated and G-chemo\u2013treated patients produced continuous biomarker scores, which were dichotomized into G-pref or F-pref results. The biomarker and threshold were locked. An independent validation cohort from the prospective COMPASS and Know Your Tumor studies assessed differential treatment outcomes by TNTD and overall survival (OS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      There were 477 patients (development: 178; validation: 299). In validation, among 173 F-pref patients, those treated with F-chemo had significantly better outcomes than G-chemo for both TNTD (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .035; median TNTD: F-chemo 8.6 months; G-chemo 7.5 months) and OS (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .003; median OS: F-chemo 14.4 months; G-chemo 11.7 months). Among 126 G-pref patients, G-chemo had significantly superior TNTD (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .038; median TNTD: F-chemo 7.2 months; G-chemo 9.6 months), but no difference in OS (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .5; median OS: F-chemo 12.4 months; G-chemo 14.3 months). In propensity score\u2013weighted analysis, the biomarker predicted treatment effect (biomarker-treatment interaction TNTD\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001; OS\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .005). RNA subtypes were associated with TNTD and OS but did not predict differential treatment effects (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .3).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>The histomorphology-based GvF biomarker predicted differential treatment benefit of first-line GvF. This biomarker can guide optimal treatment selection for first-line therapy in advanced PDAC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/jco-25-02199","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:59:09Z","timestamp":1770843549000},"page":"2496-2506","update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":7,"title":["Development and Validation of a Computational Histology Artificial Intelligence\u2013Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer"],"prefix":"10.1200","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2079-9177","authenticated-orcid":true,"given":"Andrew E.","family":"Hendifar","sequence":"first","affiliation":[{"name":"Cedars Sinai Medical Center, Los Angeles, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viswesh","family":"Krishna","sequence":"additional","affiliation":[{"name":"Valar 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