External Validation of IDH Mutation Detection Using Radiomics Extracted from T2-weighted MRI with Machine Learning

Motivation: Isocitrate dehydrogenase (IDH) mutation plays a key role in the prognosis of gliomas. Several studies have detected the IDH mutation using radiomics. However, few studies focused on the generalizability of radiomics-based machine learning models. Goal(s): To externally validate the ability of radiomics for noninvasive detection of IDH mutation using multi-site data. Approach: Radiomics of T2w MRI of UCSF-PDGM dataset (Cohort 1) was used for training machine learning models, then externally validated at the local dataset (Cohort 2). Results: T2w MRI-radiomics could identify the IDH mutation with an accuracy of 0.89 on Cohort 1, which was externally validated with an accuracy of 0.73. Impact: External validation studies are important for investigating the generalizability of machine learning models. The models based on T2w MRI-radiomics resulted in 0.89 accuracy in the training dataset, with a slightly lower accuracy on external validation dataset for identifying IDH mutation.