BACKGROUND AND PURPOSE: 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-type diffuse glioma cohort. METHODS: Preoperative MRI scans (development/optimization/test sets: n = 31/38/303, male = 17/22/189, mean age = 52/59/56.7 years, high-grade glioma = 22/33/249) were retrospectively evaluated, including pre- and postcontrast T1-weighted, T2-weighted, fluid-attenuated inversion recovery, and diffusion-weighted 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- and postcontrast T1-weighted images. Statistical analysis used confusion matrices, Cohen's/Fleiss' kappa, and Kendall's W. Significance threshold was p < .05. RESULTS: Raters 1, 2, and 3 achieved overall accuracies of .86 (95% confidence interval [CI]: .81-.90), .89 (95% CI: .85-.92), and .92 (95% CI: .89-.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 .84 (95% CI: .79-.88), .88 (95% CI: .84-.92), and .89 (95% CI: .85-.92). Intrarater intergroup agreement comparing predicted and true enhancement features consistently reached substantial levels (≥.68 [95% CI: .61-.75]). Interrater comparison showed at least moderate agreement (group: ≥.42 [95% CI: .36-.48], pairwise: ≥.61 [95% CI: .50-.72]). Among the imaging features in the EPDT, necrosis assessment displayed the highest intra- and interrater consistency (≥.80 [95% CI: .73-.88]). CONCLUSION: The proposed EPDT has high accuracy in predicting enhancement patterns of gliomas irrespective of rater experience.

2nd Faculty of Medicine Department of Pathophysiology Charles University Prague Czech Republic

Brain Imaging Amsterdam Neuroscience Amsterdam The Netherlands

Department of Radiology and Nuclear Medicine Amsterdam UMC location VUMC Amsterdam The Netherlands

Faculty of Medicine Ankara Yıldırım Beyazıt University Ankara Türkiye

Helmholtz Zentrum Dresden Rossendorf Institute of Radiopharmaceutical Cancer Research Dresden Germany

Imaging and Biomarkers Cancer Center Amsterdam Amsterdam The Netherlands

Motol University Hospital Prague Czech Republic

Queen Square Institute of Neurology and Center for Medical Image Computing University College London London UK

University Medical Center Vrije Universiteit Amsterdam Amsterdam The Netherlands

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