{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:50:14Z","timestamp":1791474614438,"version":"4.3.4"},"reference-count":44,"publisher":"Walter de Gruyter GmbH","issue":"4-5","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"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-2026-0199_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>We developed a multimodal fusion model combining clinical data and deep transfer learning for early progressive cerebral contusion (PCC) prediction, providing precise clinical support for treatment decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0199_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using a single-center retrospective cohort design, we analyzed 196 cerebral contusion patients between January 2022 and June 2024. PCC was characterized by a contusion volume increase of at least 30\u202f% on CT scans within 24\u202fh. Patients were categorized into a progression group (n=98) and a non-progression group (n=98). The dataset was split into a training coh59 participants, maintaining a 7:3 ratort of 137 participants and a validation cohort of io. A nomogram was developed by combining ResNet-50-based deep transfer learning features with clinical variables. Model performance was assessed through ROC curves, calibration plots, and decision curve analysis, while Grad-CAM was used to evaluate interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0199_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The integrated nomogram demonstrated superior performance with AUC values of 0.999 (95\u202f% CI: 0.998\u20131.000) in the training cohort and 0.972 (95\u202f% CI: 0.939\u20131.000) in the validation cohort, surpassing the standalone DTL and clinical models. Grad-CAM demonstrated accurate lesion localization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0199_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The multimodal fusion model integrating DTL and clinical features shows excellent predictive performance and significant clinical value in early PCC prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2026-0199","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T03:56:20Z","timestamp":1781841380000},"page":"395-405","source":"Crossref","is-referenced-by-count":1,"title":["Early prediction of progressive cerebral contusion using a deep transfer learning\u2013enhanced multimodal nomogram"],"prefix":"10.1515","volume":"71","author":[{"given":"Wanqin","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Emergency , The Third People\u2019s Hospital of Hefei, Hefei Third Clinical College of Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Emergency , The Third People\u2019s Hospital of Hefei, Hefei Third Clinical College of Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Nursing , 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":[[2026,6,22]]},"reference":[{"key":"2026100814512981728_j_bmt-2026-0199_ref_001","doi-asserted-by":"crossref","unstructured":"Baldon, IV, Amorim, AC, Santana, LM, Solla, DJ, Kolias, A, Hutchinson, P, et al.. 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