{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T04:52:22Z","timestamp":1785819142946,"version":"3.56.0"},"reference-count":35,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neuroimaging"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Purpose<\/jats:title><jats:p>To develop and test a decision tree for predicting contrast enhancement quality and shape using precontrast magnetic resonance imaging (MRI) sequences in a large adult\u2010type diffuse glioma cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Preoperative MRI scans (development\/optimization\/test sets: <jats:italic>n<\/jats:italic>\u00a0=\u00a031\/38\/303, male\u00a0=\u00a017\/22\/189, mean age\u00a0=\u00a052\/59\/56.7\u00a0years, high\u2010grade glioma\u00a0=\u00a022\/33\/249) were retrospectively evaluated, including pre\u2010 and postcontrast T1\u2010weighted, T2\u2010weighted, fluid\u2010attenuated inversion recovery, and diffusion\u2010weighted imaging sequences. Enhancement prediction decision tree (EPDT) was developed using development and optimization sets, incorporating four imaging features: necrosis, diffusion restriction, T2 inhomogeneity, and nonenhancing tumor margins. EPDT accuracy was assessed on a test set by three raters of variable experience. True enhancement features (gold standard) were evaluated using pre\u2010 and postcontrast T1\u2010weighted images. Statistical analysis used confusion matrices, Cohen's\/Fleiss\u2019 kappa, and Kendall's <jats:italic>W<\/jats:italic>. Significance threshold was <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a0.05.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Raters 1, 2, and 3 achieved overall accuracies of\u00a0.86 (95% confidence interval [CI]:\u00a0.81\u2010.90),\u00a0.89 (95% CI:\u00a0.85\u2010.92), and\u00a0.92 (95% CI:\u00a0.89\u2010.95), respectively, in predicting enhancement quality (marked, mild, or no enhancement). Regarding shape, defined as the thickness of enhancing margin (solid, rim, or no enhancement), accuracies were\u00a0.84 (95% CI:\u00a0.79\u2010.88),\u00a0.88 (95% CI:\u00a0.84\u2010.92), and\u00a0.89 (95% CI:\u00a0.85\u2010.92). Intrarater intergroup agreement comparing predicted and true enhancement features consistently reached substantial levels (\u2265.68 [95% CI:\u00a0.61\u2010.75]). Interrater comparison showed at least moderate agreement (group: \u2265.42 [95% CI:\u00a0.36\u2010.48], pairwise: \u2265.61 [95% CI:\u00a0.50\u2010.72]). Among the imaging features in the EPDT, necrosis assessment displayed the highest intra\u2010 and interrater consistency (\u2265.80 [95% CI:\u00a0.73\u2010.88]).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed EPDT has high accuracy in predicting enhancement patterns of gliomas irrespective of rater experience.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jon.13233","type":"journal-article","created":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T04:20:40Z","timestamp":1726806040000},"page":"673-693","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Human performance in predicting enhancement quality of gliomas using gadolinium\u2010free MRI sequences"],"prefix":"10.1111","volume":"34","author":[{"given":"Aynur","family":"Azizova","sequence":"first","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUMC  Amsterdam The Netherlands"},{"name":"Imaging and Biomarkers Cancer Center Amsterdam  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivar J. 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