Research Information System
Journal of Neuroimaging, 2024 (SCI-Expanded)
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.