ArticleCommunications medicine2025
Segmentation-free pretherapeutic assessment of BRAF-status in pediatric low-grade gliomas.
Article in Communications medicine, 2025. 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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Who cites it
2 citing papers in PubMed.
- A deep learning-based automated pipeline for colorectal cancer detection in contrast-enhanced CT images.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026Article
- Segmentation-free pretherapeutic assessment of BRAF-status in pediatric low-grade gliomas.Communications medicine · 2025Article
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10 authors.
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Abstract
backgroundBRAF status is crucial for treating pediatric low-grade gliomas (pLGG) and can be assessed non-invasively from segmented tumor regions on MRI using machine learning (ML). However, there are limitations to manual and automated tumor segmentations. This study assessed the performance of automated segmentation algorithms and a segmentation-free ML classification pipeline.
methodsMolecularly characterized tumors and whole-brain FLAIR MR images were collected from 455 patients with pLGG treated between 1999 and 2023 at a children's hospital. Three medical segmentation models, TransBTS, MedNeXt, and MedicalNet, were evaluated. Next, we developed a model to identify BRAF status from whole-brain FLAIR MRI, without any reliance on segmentations. We then implemented a novel pretraining regimen that embedded segmentation knowledge into the whole-brain FLAIR MRI classification model. Finally, we trained and evaluated a baseline model that used semiautomatic whole tumor volume segmentations as inputs.
resultsHere we show that the MedNeXt segmentation model (mean Dice score: 0.555) outperformed MedicalNet (0.516) and TransBTS (0.449) (p < 0.05 for all comparisons). The MedNeXt classification model achieved a one-vs-rest area under the ROC curve of 0.741 using the whole brain FLAIR sequence as an input, without any segmentation knowledge. This was improved to 0.772 through pretraining on the segmentation task, which was not significantly different from the baseline semiautomatic whole tumor volume segmentation-based model (0.756, p-value: 0.141).
conclusionsBRAF status can be assessed non-invasively using ML models based on whole-brain FLAIR sequences. Dependence on inconsistent manual or automated segmentations can be reduced by integrating tumor region information into the model through pretraining.
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