Phenotype augmentation using generative AI for isocitrate dehydrogenase mutation prediction in glioma.

ABSTRACT: This study investigated the effects of feature augmentation, which uses generated images with specific imaging features, on the performance of isocitrate dehydrogenase (IDH) mutation prediction models in gliomas. A total of 598 patients were included from our institution (310 training, 152 internal test) and the Cancer Genome Atlas (136 external test). Score-based diffusion models were used to generate T2-weighted, FLAIR, and contrast-enhanced T1-weighted image triplets. Three neuroradiologists independently assessed visual Turing tests and various morphological features. Multivariable logistic regression models were developed using real images, random augmented data, and feature-augmented datasets. While random augmentation yielded models with AUCs comparable to real image-based models, i

SUBMITTER: Jung HK 

PROVIDER: S-EPMC12331907 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature