Development of a radiomic classification model RadMenGR for discriminating Grade 1 and Grade 2 intracranial meningioma

Background. Preoperative differentional diagnosis of meningioma grade remains challenging with routine brain magnetic resonance imaging (MRI). The lack of reliable non-invasive tools limits the potential for early risk stratification and treatment planning.

Aim: To develop an interpretable classification radiomic model RadMenGR for predicting meningioma grade (Grade I or Grade II) based on contrast-enhanced T1-weighted images.

Material and Methods. This retrospective single-center study was conducted using the open-source anonymized dataset MeningiomaSEG-CLASS. 95 patients were included in the analysis (53 with Grade 1 and 42 with Grade 2 tumors). 105 radiomic features were extracted from manually segmented MR images using PyRadiomics. Classification was performed with a Naive Bayes algorithm following feature discretization using the Entropy / MDL method. Diagnostic performance was assessed using the area under the curve (AUC), sensitivity, specificity, and accuracy. Bootstrap analysis with 10,000 iterations and a 95% confidence interval was used for validation.

Results. On the validation cohort (n = 46), the ROC-AUC was 0.805 (95% CI: 0.671–0.915). The lower bound of the 95% CI for the AUC exceeded the value under the null hypothesis (AUC = 0.63), confirming the statistical significance of the results (p < 0.05). Conclusion. This study developed an interpretable radiomic classification model for the differential diagnosis of Grade 1 and Grade 2 meningiomas. The application of a Naive Bayes algorithm to features extracted from contrast-enhanced T1-weighted images and transformed using a discretization method enabled the achievement of a significant level of diagnostic accuracy. However, the width of the confidence interval points to a lack of model robustness, necessitating validation on an independent cohort.

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