{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T18:36:10Z","timestamp":1777228570426,"version":"3.51.4"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Institute for Health and Care Research (NIHR) Academic Clinical Fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Meningiomas are the most common primary intracranial neoplasms. A significant unmet need exists for noninvasive biomarkers to risk-stratify patients and guide decision-making. The integrated risk score (IRS), which combines histopathological grade, DNA methylation class, and copy number variation (CNV), is a powerful predictor of risk of progression. We aimed to predict the molecular-morphologic IRS from pre-operative T1-weighted contrast-enhanced MRI scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 2 multi-institutional prospectively compiled datasets, including patients with histologically confirmed intracranial meningiomas that had matched DNA methylation and (CNV) profiles. We first developed a radiomics model and trained a support vector machine (SVM) classifier. We then developed a convolutional neural network based on ResNet101, and a vision transformer (ViT). Model performance was assessed using accuracy, area under the curve, precision, recall, and F1-scores.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Seventy-six patients met the inclusion criteria. Distributions per WHO class and IRS were 53 (I), 22 (II), 1 (III), and 53 (low), 18 (intermediate) and 5 (high), respectively. The SVM model achieved an AUC of 80.4% and accuracy of 78.2%. The ResNet101 model achieved 85.3% accuracy. The ViT model outperformed, achieving 89.4% accuracy and 89.1% precision in predicting low versus intermediate\/high IRS tumors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study represents the first application of the ViT architecture to predict, with high accuracy, the prognostically highly relevant IRS from routine pre-operative neuroimaging. This pipeline provides a biologically informed and clinically relevant model with potential for future use in early risk stratification and decision support in the management of patients with meningiomas.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/noajnl\/vdag017","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T12:52:46Z","timestamp":1769086366000},"source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence pipeline predicts the integrated molecular-morphologic risk score of meningiomas from routine preoperative MRI"],"prefix":"10.1093","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4951-8586","authenticated-orcid":false,"given":"Damjan","family":"Veljanoski","sequence":"first","affiliation":[{"name":"Peninsula Medical School, University of Plymouth"},{"name":"University Hospitals Plymouth NHS Trust South West Neurosurgery Centre, , 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