Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning.
Divyanshu TakZezhong YeAnna ZapaischykovaAidan BoydSridhar VajapeyamRishi ChopraYining ZhaHasaan HayatSanjay P PrabhuKevin X LiuHesham ElhalawaniAli NabavidazehAriana FamiliarAdam ResnickSabine MuellerHugo J W L AertsPratiti BandopadhayayKeith LigonDaphne Haas-KoganTina PoussaintBenjamin H KannPublished in: medRxiv : the preprint server for health sciences (2023)
An innovative training approach combining self-supervision and transfer learning ("TransferX") is developed to boost model performance in low data settings;TransferX enables the development of a scan-to-prediction pipeline for pediatric LGG mutational status (BRAF V600E, fusion, or wildtype) with high accuracy and mild performance degradation on external validation;An evaluation metric "COMDist" is proposed to increase interpretability and quantify the accuracy of the model's attention around the tumor.