{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:50:19Z","timestamp":1791474619210,"version":"4.3.4"},"reference-count":39,"publisher":"Walter de Gruyter GmbH","issue":"4-5","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Research Fund Project of Anhui Medical University","award":["2022xkj254, 2022xkj004"],"award-info":[{"award-number":["2022xkj254, 2022xkj004"]}],"id":[{"id":"https:\/\/ror.org\/03xb04968","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Key Project of Health Application Medicine Research in Hefei","award":["Hwk2022zd011"],"award-info":[{"award-number":["Hwk2022zd011"]}]},{"name":"Key Project of Natural Science of Bengbu Medical University","award":["2024byzd366"],"award-info":[{"award-number":["2024byzd366"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_bmt-2026-0052_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Hypertensive intracerebral hemorrhage (HICH) is a frequently encountered and highly lethal cerebrovascular disorder, and postoperative rebleeding remains one of its most feared complications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0052_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a nomogram featuring a hybrid architecture that integrates radiomics-derived signatures and deep learning representations from CT imaging together with routine clinical parameters, with the goal of enhancing the individualized prediction of rebleeding risk following surgical intervention in HICH patients. A total of 151 individuals diagnosed with HICH were prospectively enrolled and randomly assigned to a training set (n=105) and a validation set (n=46) following a 7:3\u00a0ratio.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0052_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The resulting model outperformed single-domain approaches relying solely on traditional clinical indicators or deep learning features. In the training cohort, the nomogram yielded an AUC of 0.993 (95\u202f% CI: 0.982\u20131.000), while in the internal testing cohort, the AUC reached 0.860 (95\u202f% CI: 0.745\u20130.974). The model highlighted several key predictors associated with postoperative rebleeding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0052_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Overall, the integrated nomogram, embedding clinical data, radiomic phenotypes, and deep learning markers, exhibited robust predictive capability in assessing rebleeding risk among patients with HICH. Ongoing research is needed to further refine and validate the model in broader clinical settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2026-0052","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:00:40Z","timestamp":1773352840000},"page":"363-376","source":"Crossref","is-referenced-by-count":1,"title":["CT-based hybrid deep learning\u2013radiomics framework for predicting postoperative rebleeding in hypertensive intracerebral hemorrhage"],"prefix":"10.1515","volume":"71","author":[{"given":"Weiwei","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Neurosurgery , The Third People\u2019s Hospital of Hefei, Hefei Third Clinical College of Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery , The Third People\u2019s Hospital of Hefei, Hefei Third Clinical College of Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery , The Third People\u2019s Hospital of Hefei, Hefei Third Clinical College of Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Ji","sequence":"additional","affiliation":[{"name":"Imaging Center , 12485 The Third People\u2019s Hospital of Hefei, Hefei Third Clinical College of Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"2026100814512986580_j_bmt-2026-0052_ref_001","doi-asserted-by":"crossref","unstructured":"Hawkes, MA, Rabinstein, AA. 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