Keywords: Analysis/Processing, Radiomics, glioma, machine learning, generalizability
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords