ArticleNeuro-oncology advances
GlioMODA: Robust glioma segmentation in clinical routine.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- 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.Frontiers in neurologyReview
- Enhancing efficiency in pediatric brain tumor segmentation using a pathologically diverse single-center clinical dataset.Neuro-oncology advancesArticle
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Authors and funding
11 authors.
Funding
Abstract
Background: Precise glioma segmentation in magnetic resonance imaging (MRI) is essential for accurate diagnosis, optimal treatment planning, and advancing clinical research. However, most deep learning approaches require complete, standardized MRI protocols that are frequently unavailable in routine clinical practice. This study presents and evaluates GlioMODA, a robust deep learning framework designed for automated glioma segmentation that delivers consistent high performance across varied and incomplete MRI protocols. Methods: GlioMODA was trained and validated on the BraTS 2021 dataset (1251 training, 219 testing cases), systematically assessing performance across 11 clinically relevant MRI protocol combinations. Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and panoptic quality metrics. Volumetric accuracy was benchmarked against manual ground truth, and statistical significance was established via Wilcoxon signed‑rank tests with Benjamini-Yekutieli correction. Results: GlioMODA demonstrated state-of-the-art segmentation accuracy across tumor subregions, maintaining robust performance with incomplete or heterogeneous MRI protocols. Protocols including both T1-weighted contrast-enhanced and T2-FLAIR sequences yielded volumetric differences vs manual ground truth that were not statistically significant for enhancing tumor (median difference 55 mm³, Conclusions: GlioMODA facilitates reliable, automated glioma segmentation using a streamlined 2‑sequence protocol (T1‑contrast + T2‑FLAIR), supporting clinical workflow optimization and broader implementation of quantitative volumetry compatible with RANO 2.0 criteria. GlioMODA is published as an open-source, easy-to-use Python package at https://github.com/BrainLesion/GlioMODA/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.