ArticleFrontiers in neurology
A single model for glioblastoma segmentation with and without T2-FLAIR: independent validation of a targeted dropout strategy.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: To evaluate targeted T2 fluid-attenuated inversion recovery (T2-FLAIR) dropout for robust automated glioblastoma segmentation and volumetry when T2-FLAIR is unavailable, while preserving performance when the full MRI protocol is available. Materials and methods: In this retrospective multi-dataset study, 3D nnU-Net models were developed on BraTS 2021 after excluding UPenn-GBM cases (remaining Results: In the UPenn-GBM validation cohort, performance was preserved with the full MRI protocol: overall median DSC was 94.8% [interquartile range (IQR) 90.0-97.1%] with 35% dropout and 95.0% (IQR 90.3-97.1%) without dropout. In the T2-FLAIR-unavailable scenario, targeted dropout improved overall median DSC from 81.0% (IQR 75.1-86.4%) to 93.4% (IQR 89.1-96.2%). Whole-tumor DSC improved from 60.4 to 92.6%, whole-tumor 95th percentile Hausdorff distance from 17.24 mm to 2.45 mm, and whole-tumor volume bias from -45.6 mL to 0.83 mL. A dedicated three-sequence nnU-Net achieved similar performance without T2-FLAIR (overall DSC 93.8%, WT DSC 93.5%), suggesting that much of this recovery reflects adaptation to the reduced-input setting rather than dropout training specifically. Conclusion: In the independent withheld UPenn-GBM cohort, targeted T2-FLAIR dropout preserved complete-protocol performance and remained robust when T2-FLAIR was unavailable. Because a dedicated three-sequence model matched this performance without T2-FLAIR, the value of targeted dropout lies not in superior missing-sequence accuracy but in providing a single model that operates across both complete and T2-FLAIR-unavailable inference.
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