GliomaAI: An Open-Source 3D Slicer Extension for Explainable Preoperative Classification of Low-Grade Versus High-Grade Gliomas from MRI

Although numerous studies have developed promising diagnostic algorithms, many have yet to be translated into workflow-integrated tools. Gliomas represent a substantial proportion of primary brain tumors, and their preoperative differentiation by tumor grade can provide critical information for patient management. In this study, we present and evaluate GliomaAI, an open-source 3D Slicer extension for the automated classification of low-grade versus high-grade gliomas from preoperative MRI. The extension is organized into two complementary frameworks for MRI preprocessing and classification with explainability. Four 3D CNN architectures evaluated in our previous work were integrated into a unified segmentation-free workflow, with their final deployment retrained on T1-CE and FLAIR volumes from the multicohort dataset of 1194 patients. GliomaAI offers both individual CNN classification and an ensemble configuration. For single-model predictions, Grad-CAM heatmaps can be interactively overlaid on either MRI volume to aid interpretation of the model’s decision. Explainability is further enhanced through 3D rendering of the generated heatmaps, providing spatial inspection of displayed activations. After excluding overlaps with the model-development cohort, independent validation was conducted on the remaining BraTS-19 cases (26 LGG, 208 HGG). GliomaAI-CNN achieved a balanced accuracy of 82.2% and an AUC of 0.945. The extension provides an accessible, computationally efficient bridge toward clinical application.