Deep learning radiomics nomograms predict IDH genotype in glioma patients: a multicenter study

Motivation: It is unclear whether deep learning radiomics nomograms (DLRN) can noninvasively predict isocitrate dehydrogenase (IDH) genotypes in glioma patients. Goal To explore the feasibility of DLRN in predicting IDH genotype. Goal(s): To explore the feasibility of DLRN in predicting IDH genotype. Approach: T2WI-based DLRN was developed and validated in two centers (Center I, n=342 and Center II, n=60) to predict IDH genotype and evaluate its association with prognosis in glioma patients. Results: The proposed model had an area under the curve(AUC)of 0.98 in an externally validated cohort, and DLRN scores were significantly associated with the overall survival of glioma patients. Impact: The proposed DLRN can accurately predict IDH genotypes and provide a useful tool for targeted therapy of patients with IDH mutations.