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Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n\u2005=\u200519; internal validation), and prospective (n\u2005=\u200529; external validation from 8 distinct centers). Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics\/MGMT\/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P\u2005=\u2005.038) and performed similarly to a combined imaging\/nonimaging model (P\u2005&amp;gt;\u2005.05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P\u2005=\u2005.003).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/neuonc\/noae017","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T18:28:56Z","timestamp":1706552936000},"page":"1138-1151","source":"Crossref","is-referenced-by-count":31,"title":["Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study)"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0867-1565","authenticated-orcid":false,"given":"Alysha","family":"Chelliah","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences, King\u2019s College London , London , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David 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