Development and External Validation of an MRI-based Radiomics Nomogram to Distinguish Circumscribed Astrocytic Gliomas and Diffuse Gliomas: A Multicenter Study

Abstract

The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHOCNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs). We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions. After tumor segmentation, three volume of interest (VOI) types were obtained: VOItumor and peritumor, VOIwhole, and VOIinterface. Clinical and combined models (incorporating radiomics and clinical features) were also established. To address imbalances in training dataset, Synthetic Minority Oversampling Technique was employed. A total of 475 patients (DGs: n=338, CAGs: n=137) were analyzed. The VOIinterface model demonstrated the best performance for differentiating CAGs from DGs, achieving an area under the curve (AUC) of 0.806 and area under the precision-recall curve (PRAUC)of 0.894 in the cross-validation set. Using analysis of variance (ANOVA) feature selector and Support Vector Machine (SVM) classifier, sevenfeatures were selected. The model achieved an AUC and AUPRC of 0.912 and 0.972 in the internal validation dataset, and 0.897 and 0.930 in the external validation dataset. The combined model, incorporating interface radiomics and clinical features, showed improved performance in the external validation set, with an AUC of 0.94 and PRAUC of 0.959. Radiomics models incorporating the peritumoral area demonstrate greater potential for distinguishing CAGs from DGs compared to intratumoral models. These findings may hold promise for evaluating tumor nature before surgery and improving clinical management of glioma patients.