Validation of open-source deep learning segmentation tools for automated glioma volumetry: a narrative review of Dice scores, workflow efficiency, and clinical RANO 2.0 implementation.

Background: Segmentation enables extraction of quantitative imaging features to enhance glioma diagnosis by volumetric measurements and treatment response assessment. This narrative review evaluates open-source software for glioma segmentation and alignment with Response Assessment in Neuro-Oncology (RANO 2.0) volumetric criteria. Approach: In this narrative review, we evaluated thirteen open-source tools selected for multimodal MRI sequence support (T1W, T1CE, T2W, FLAIR), performance on public datasets (BraTS Challenge), and applicability to RANO 2.0 volumetry. Assessment included Dice scores, workflow efficiency, advantages, limitations, and clinical translation potential. Results: Tools achieved Dice scores 0.73-0.92 for tumor subregions. Despite high analytical validation, clinical utility is limited: 85% of treatment response studies have bias risk in patient selection per QUADAS-2 appraisal. Critically, as of November 2024, no automated tools have been formally validated specifically against RANO 2.0 criteria, despite their 2023 emphasis on volumetric standardization. Conclusion: Open-source segmentation tools show promise for standardizing glioma volumetry with emerging tools (GlioMODA, AutoRANO) explicitly targeting RANO 2.0-compatible volumetric assessment. Hybrid approaches combining open-source innovation with commercial clinical integration could optimize clinical translation.