Abstract B020: Simple quantitative characterization of high-grade gliomas using 3D slicer

Abstract Introduction: High-grade gliomas (HGGs) are highly aggressive primary brain tumors characterized by rapid growth, heterogenous morphology, and diffuse infiltration into surrounding tissue. Accurate, reproducible quantification of tumor size and location is essential for research, treatment planning, and outcome prediction. Standard neuroimaging analysis often requires advanced computation skills, limiting accessibility for many researchers and clinicians. Methods: T1-weighted MRI volumes and corresponding segmentation masks from BraTS 2020 training dataset were reconstructed from 2D slice files into complete 3D volumes using a Python-based workflow. Tumors were analyzed in 3D Slicer v5.8.1 using simple, reproducible metrics: total tumor volume, maximum cross-sectional area, and anatomic lobe location. All measurements were obtained within Slicer using volume and region-of-interest tools, without requiring advanced programming skills. Results: This approach enabled standardized classification of tumors by size and spatial distribution across patients. Rebuilt 3D volumes allowed accurate visualization in axial, coronal, and sagittal planes, and segmentation overlays facilitated consistent measurement of tumor extent and location. Discussion/Clinical Significance: The resulting dataset provides a foundation for comparing morphological features, correlating tumor characteristics with clinical parameters, and supporting studies in treatment planning or outcome prediction. This workflow demonstrates that straightforward, user-friendly tools can generate quantitative neuroimaging data suitable for research, teaching, and preliminary analyses, making volumetric and locational assessment of HGGs accessible even without extensive computational expertise. Citation Format: Madeline Godziela, John Ashurst. Simple quantitative characterization of high-grade gliomas using 3D slicer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr B020.