Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning

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“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop and externally test a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma (pLGG). Materials and Methods This retrospective study included two pLGG datasets with linked genomic and diagnostic T2-weighted MRI data of patients: BCH (development dataset, n = 214 [60 (28%) BRAF-Fusion, 50 (23%) BRAF V600E, 104 (49%) wild-type), and the Children’s Brain Tumor Network (external testing, n = 112 [60 (53%) with BRAF-Fusion, 17 (15%) BRAF-V600E, 35 (32%) wild-type]). A deep learning pipeline was developed to classify BRAF mutational status (V600E versus Fusion versus Wild-Type) via a two-stage process: 1) 3D tumor segmentation and extraction of axial tumor images, and 2) slice-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary endpoint of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we developed a novel metric, COMDist (Center of Mass Distance), that quantifies the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with AUC of 0.82 [95% CI: 0.72–0.91], 0.87 [95% CI: 0.61–0.97], and 0.85[95% CI: 0.66–0.95] for Wild-Type, BRAF-Fusion and BRAF-V600E, respectively, on internal testing. On external testing, the pipeline yielded AUC of 0.72 [95% CI: 0.64–0.86], 0.78 [95% CI: 0.61–0.89], and 0.72 [95% CI: 0.64–0.88] for Wild-Type, BRAF-Fusion and BRAF-V600E classes, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario. ©RSNA, 2024

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