AI Article Synopsis
- The study aimed to create and validate a decision tree model for predicting the quality and shape of contrast enhancement in adult diffuse glioma patients using precontrast MRI scans.
- It involved analyzing MRI data from 31 development, 38 optimization, and 303 test sets to develop an enhancement prediction decision tree (EPDT) based on four key imaging features.
- The results showed that raters had high accuracy (ranging from 86% to 92%) in predicting enhancement quality and shape, with strong agreement on necrosis assessment among raters.
Article Abstract
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.
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| http://dx.doi.org/10.1111/jon.13233 | DOI Listing |
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