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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Adult diffuse gliomas are clinically and molecularly heterogeneous, complicating risk stratification and personalized management. We introduce GlioSurv, a multimodal transformer model based on an accelerated failure time framework to integrate multiparametric MRI, clinical and molecular variables, and treatment data for personalized survival prediction. In a retrospective analysis of 1944 patients, including one internal cohort (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009891; mean OS 32.2 months) and three external cohorts (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200984, 470, 499; mean OS 26.1, 18.8, 19.0 months), GlioSurv demonstrated robust discrimination (IAUC: 0.68\u20130.86), calibration (IBS: 0.10\u20130.21) and concordance (C-index: 0.61\u20130.80). It significantly outperformed a convolutional neural network, a vision transformer, and a non-imaging multimodal transformer (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.01). Sequential integration of imaging, clinical, molecular, then treatment data, progressively improved C-index from 0.69 to 0.80 (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). Interpretability analyses confirmed established prognostic factors and indicate the potential of GlioSurv to support personalized survival prediction and risk-stratified decision-making in diffuse glioma.\n                  <\/jats:p>","DOI":"10.1038\/s41746-025-02018-x","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T15:38:40Z","timestamp":1763134720000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["GlioSurv: interpretable transformer for multimodal, individualized survival prediction in diffuse glioma"],"prefix":"10.1038","volume":"8","author":[{"given":"Junhyeok","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heeseong","family":"Eum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minchul","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Hye","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chul Kee","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung Hong","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Soo","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoseob","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyu Sung","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"2018_CR1","first-page":"394","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray, F. et al. 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