{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T03:27:38Z","timestamp":1788924458464,"version":"build-2803163510"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":34,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neuroimaging"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Purpose<\/jats:title>\n                    <jats:p>Grading meningioma guides treatment choices from follow\u2010up to surgical resection with adjuvant radiation. Radiomics may offer a non\u2010invasive alternative to biopsies. We assessed radiomic features (RFs) for distinguishing Grade 1 and Grade 2 meningiomas on preoperative multiparametric MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Presurgical T1\u2010weighted (T1), T2\u2010weighted (T2), T2 gradient echo\u2010weighted (T2GRE), fluid\u2010attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC), and T1\u2010weighted contrast\u2010enhanced (T1CE). MRI sequences of histopathologically diagnosed meningiomas were collected retrospectively. Each volume had 75 RFs extracted from semimanually segmented tumors using MintLesion Research (Version 3.10). The Lasso method selected variables from imputed data, and 10\u2010fold cross\u2010validation determined the optimal regularization parameter. For Lasso\u2010retained variables, multivariate effects were estimated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Out of 150 patients (67.3% women), 110 (73.3%) had Grade 1 meningiomas, and 40 (26.7%) Grade 2. The strongest metrics to distinguish meningiomas Grade 1 versus Grade 2 were intensity histogram coefficient of variation on T1CE (odds ratio [OR] 0.47, 95% confidence interval [CI] 0.23\u20130.88;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.028), maximum histogram gradient on T1 (OR 2.11, 95% CI 1.18\u20134.82;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.043), and intensity histogram quartile coefficient of dispersion on FLAIR (OR 0.53, 95% CI 0.31\u20130.89;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.021). The combined RFs achieved an area under the curve of 0.814 (95% CI, 0.732\u20130.896) for grading differentiation. Texture features and metrics extracted from T2, T2GRE, and ADC sequences did not discriminate meningioma grading.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Histogram\u2010based first\u2010order RFs from T1, FLAIR, and T1CE may predict meningioma grades preoperatively. Larger, multicenter studies are needed to confirm these findings, providing insights for clinical decision\u2010making and personalized treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jon.70127","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T09:13:03Z","timestamp":1770196383000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Brain MRI Radiomic First\u2010Order Features for Presurgical Prediction of Meningioma Grading"],"prefix":"10.1111","volume":"36","author":[{"given":"Camilo","family":"Pineda\u2010Ibarra","sequence":"first","affiliation":[{"name":"Department of Neuroradiology Diagnostic Imaging Centre Hospital Clinic de Barcelona  Barcelona Spain"},{"name":"Department of Radiology Diagnostic Imaging Centre (CDI) and IDIBAPS Hospital Cl\u00ednic de Barcelona  Barcelona Spain"},{"name":"Facultat de Medicina i Ci\u00e8ncies de la Salut Universitat de Barcelona Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2791-6599","authenticated-orcid":false,"given":"Josep","family":"Puig","sequence":"additional","affiliation":[{"name":"Department of Radiology Diagnostic Imaging Centre (CDI) and IDIBAPS Hospital Cl\u00ednic de Barcelona  Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego","family":"Nu\u00f1ez\u2010Leiva","sequence":"additional","affiliation":[{"name":"Neuroradiology, Radiology Department Hospital Clinico Regional Dr. Guillermo Grant Benavete Concepcion University  Concepcion Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yensa","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology Diagnostic Imaging Centre Hospital Clinic de Barcelona  Barcelona 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