{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:52:15Z","timestamp":1786978335715,"version":"build-2736575974"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82073640"],"award-info":[{"award-number":["82073640"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91959127"],"award-info":[{"award-number":["91959127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20389"],"award-info":[{"award-number":["U21A20389"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["pathsocjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Pathology"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Artificial intelligence approaches to analyze pathological images (pathomic) for outcome prediction have not been sufficiently considered in the field of pituitary research. A total of 5,504 hematoxylin &amp; eosin\u2010stained pathology image tiles from 58 acromegalic patients with a good or poor outcome were integrated with other clinical and genetic information to train a low\u2010rank fusion convolutional neural network (LFCNN). The model was externally validated in 1,536 patches from an external cohort. The primary outcome was the time to the first endocrine remission after stereotactic radiosurgery (SRS). The median time of initial endocrine remission was 43\u2009months (interquartile range [IQR]: 13\u201360\u2009months) after SRS, and the 24\u2010month initial cumulative remission rate was 57.9% (IQR: 46.4\u201372.3%). The patient\u2010wise accuracy of the LFCNN model in predicting the primary outcome was 92.9% in the internal test dataset, and the sensitivity and specificity were 87.5 and 100.0%, respectively. The LFCNN model was a strong predictor of initial cumulative remission in the training cohort (hazard ratio [HR] 9.58, 95% confidence interval [CI] 3.89\u201323.59;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001) and was higher than that of established prognostic markers. The predictive value of the LFCNN model was further validated in an external cohort (HR 9.06, 95% CI 1.14\u201372.25;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.012). In this proof\u2010of\u2010concept study, clinically and genetically useful prognostic markers were integrated with digital images to predict endocrine outcomes after SRS in patients with active acromegaly. The model considerably outperformed established prognostic markers and can potentially be used by clinicians to improve decision\u2010making regarding adjuvant treatment choices. \u00a9 2022 The Pathological Society of Great Britain and Ireland.\n                  <\/jats:p>","DOI":"10.1002\/path.5974","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T11:12:00Z","timestamp":1654254720000},"page":"49-57","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Low\u2010rank fusion convolutional neural network for prediction of remission after stereotactic radiosurgery in patients with acromegaly: a proof\u2010of\u2010concept study"],"prefix":"10.1002","volume":"258","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5478-3555","authenticated-orcid":false,"given":"Nidan","family":"Qiao","sequence":"first","affiliation":[{"name":"Department of Neurosurgery Huashan Hospital  Shanghai PR 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