{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:29:41Z","timestamp":1777984181149,"version":"3.51.4"},"reference-count":27,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Neuroendocrinology"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Neuroendocrine tumors (NETs) frequently present at a metastatic stage, particularly with liver metastases. Identifying the site of the primary tumor is critical for guiding therapy but often proves difficult. Small intestine NETs are especially distinct in their prognosis and treatment. To address this challenge, we developed a novel,\u00a0machine learning\u2010based tool to predict the site of origin\u2014specifically small intestine or pancreas\u2014using routine hematoxylin and eosin (H&amp;E)\u2010stained slides from hepatic metastases. To avoid mislabeling in the clinically relevant scenario of any possible tumor origin, the method applies a two\u2010step approach with optional abstention for uncertain classifications or non\u2010small intestine\/non\u2010pancreas cases. In a retrospective, clinically realistic cohort with unrestricted tumor origin, the model identified small intestine NETs with a sensitivity of 71.4% at 100% specificity and positive predictive value (PPV), and high negative predictive value. A relevant subset of pancreatic NETs can also be reliably detected (sensitivity 33.3%, specificity 94.1%, PPV 85.7%). Generalizability and robustness were rigorously validated on an external dataset using different scanners, institutions, and resection techniques. The tool is intended as an additional method where other diagnostic modalities remain inconclusive regarding the location of the primary tumor. To facilitate further research and clinical translation, all models and extracted features are publicly released.<\/jats:p>","DOI":"10.1111\/jne.70134","type":"journal-article","created":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T06:26:45Z","timestamp":1770445605000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A new, machine learning\u2010based approach to metastatic neuroendocrine tumors of unknown origin"],"prefix":"10.1111","volume":"38","author":[{"given":"Jiaxi","family":"L\u00fc","sequence":"first","affiliation":[{"name":"Institute for Applied Medical Informatics University Medical Center Hamburg\u2010Eppendorf  Hamburg Germany"},{"name":"Institute of Computational Neuroscience University Medical Center Hamburg\u2010Eppendorf  Hamburg Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tania","family":"Amin","sequence":"additional","affiliation":[{"name":"I. 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