Keywords: Diagnosis/Prediction, Radiomics, glioma, deep learning radiomics nomograms
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.
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