ArticleNeuro-oncology advances
Beyond proof-of-concept: Validating robust automated diffuse lower-grade glioma segmentation for clinical applications in longitudinal follow-up.
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 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multidisciplinary recommendations for routine follow-up of isocitrate dehydrogenase‑mutant glioma in 2026: Adapting clinical practice to the molecular era.Neuro-oncology practice · 2026Review
- Distinct molecular profiles characterize the spontaneous growth rate of IDHmt low-grade astrocytomas and oligodendrogliomas.Neuro-oncology · 2026Article
- Modeling tumor volume trajectory and outcome identifies 3 distinct response classes in lower-grade IDH-mutant glioma receiving first-line chemotherapy.Neuro-oncology advancesArticle
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Monitoring diffuse lower-grade glioma (DLGG) evolution on MRI is challenging due to their infiltrative nature and post-surgical deformations. The RANO criteria recommend assessing the 2D tumor size, but volume analysis remains the gold standard for calculating growth rate. Manual tumor segmentation, however, is time-consuming, limiting its use in clinical practice. Automated segmentation tools like nnU-Net are promising but require clinical validation. Methods: We used 1971 MRI exams from 207 DLGG patients to train and validate an in house-developed nnU-Net al.orithm. The dataset included scans from various MRI systems, with 2D and 3D FLAIR and T1-weighted acquisitions, that were divided into derivation ( Results: The median nnU-Net DSC and IoU were 0.93 and 0.86, respectively. In the validation cohort, 64% of exams had excellent (≥0.9), 31% good ([0.7-0.9]), 3.5% unsatisfactory ([0.5-0.7]), and 1.5% poor DSC (<0.5). Higher DSC correlated with larger tumor volumes ( Conclusions: Our automated nnU-Net segmentation tool demonstrates high accuracy and potential for clinical integration, enhancing DLGG monitoring and management.
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