Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning.
medRxiv
; 2023 Nov 22.
Article
in En
| MEDLINE
| ID: mdl-37609311
ABSTRACT
Purpose:
To develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG. Materials andMethods:
We conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients BCH (development dataset, n=214 [60 (28%) BRAF fusion, 50 (23%) BRAF V600E, 104 (49%) wild-type), and Child Brain Tumor Network (CBTN) (external validation, n=112 [60 (53%) BRAF-Fusion, 17 (15%) BRAF-V600E, 35 (32%) wild-type]). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs. fusion vs. wildtype) 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. We investigated knowledge-transfer and self-supervised approaches 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, that quantifies the accuracy of 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 macro-average AUC (0.82 [95% CI 0.70-0.90]) and accuracy (77%) on internal validation, with an AUC improvement of +17.7% and a COMDist improvement of +6.4% versus training from scratch. On external validation, the TransferX model yielded AUC (0.73 [95% CI 0.68-0.88]) and accuracy (75%).Conclusion:
Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
MedRxiv
Year:
2023
Type:
Article
Affiliation country:
United States