{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:11:35Z","timestamp":1790241095177,"version":"4.1.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jinhua City Science and Technology Bureau key project plan","award":["2023-3-090"],"award-info":[{"award-number":["2023-3-090"]}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Post-traumatic hydrocephalus (PTH) impairs recovery after traumatic brain injury (TBI), yet reliable early risk stratification based solely on baseline assessment remains limited. We aimed to develop and temporally validate a preoperative CT\u2013based radiomics model for early prediction of PTH and to assess the incremental value of clinical predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this retrospective single-center cohort, 341 consecutive adults undergoing neurosurgical treatment for TBI were included. Patients treated in 2021\u20132023 (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f289) formed the training cohort, and those treated in 2024 (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f52) served as a temporally independent validation cohort. A total of 11,998 radiomics features were extracted from 14 automatically segmented intracranial regions on the last preoperative CT. Feature selection was performed using mutual information filtering and LASSO regression. Machine learning classifiers were benchmarked, and a clinical\u2013radiomics fusion model was constructed using stacking. Model performance was evaluated using discrimination, calibration, and decision curve analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>PTH occurred in 27.3% of the training cohort and 23.1% of the validation cohort. In temporal validation, the clinical, radiomics, and fusion models achieved AUCs of 0.769 (95% CI, 0.608\u20130.906), 0.783 (95% CI, 0.604\u20130.929), and 0.865 (95% CI, 0.735\u20130.958), respectively. The fusion model also demonstrated acceptable calibration and greater net benefit across clinically relevant thresholds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Baseline preoperative CT radiomics enables early risk stratification of PTH after TBI. Integration with clinical predictors provides incremental predictive value under temporal validation and may support individualized surveillance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2026.1861928","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T08:50:56Z","timestamp":1790239856000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Baseline CT radiomics for early prediction of post-traumatic hydrocephalus in traumatic brain injury: development and temporal validation"],"prefix":"10.3389","volume":"17","author":[{"given":"Chunwang","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine","place":["Jinhua, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaijie","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine","place":["Jinhua, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xujian","family":"Shui","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine","place":["Jinhua, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine","place":["Jinhua, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuejun","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine","place":["Jinhua, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine","place":["Jinhua, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1159\/000547563","article-title":"The burden of traumatic brain injury, its causes, and future trend predictions in 204 countries and territories (1990\u20132021): results from the global burden of disease study 2021","volume":"60","author":"Liu","year":"2025","journal-title":"Neuroepidemiology"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"527","DOI":"10.3390\/neurolint13040052","article-title":"Hydrocephalus and 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