Human Performance in Predicting Enhancement Quality of Gliomas Using Gadolinium-Free MRI Sequences

Background and Purpose: To develop and test a decision tree for predicting contrast enhancement quality and shape using pre-contrast 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 post-contrast 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 post-contrast T1-weighted images. Statistical analysis used confusion matrices, Cohen’s/Fleiss’ kappa, and Kendall’s W. Significance threshold was P < 0.05. Results: Raters 1, 2, and 3 achieved overall accuracies of 0.86 [95%-confidence interval (CI): 0.81-0.90], 0.89 (95%-CI: 0.85-0.92), and 0.92 (95%-CI: 0.89-0.95), 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 0.84 (95%-CI: 0.79-0.88), 0.88 (95%-CI: 0.84-0.92), and 0.89 (95%-CI: 0.85-0.92). Intra-rater inter-group agreement comparing predicted and true enhancement features consistently reached substantial levels [≥0.68 (95%-CI: 0.61-0.75). Inter-rater comparison showed at least moderate agreement (group: ≥0.42 (95%-CI: 0.36-0.48), pairwise: ≥0.61 (95%-CI: 0.50-0.72)]. Among the imaging features in the EPDT, necrosis assessment displayed the highest intra- and inter-rater consistency [≥0.80 (95%-CI: 0.73-0.88)]. Conclusion: The proposed enhancement prediction decision tree has high accuracy in predi