Abstract
BACKGROUND: BRAF 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. METHODS: Molecularly 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. RESULTS: Here 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). CONCLUSIONS: BRAF 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.