Keywords: Diagnosis/Prediction, Brain
Motivation: The WHO classifies posterior fossa ependymomas (PF-EPNs) into Groups A (PFA) and B (PFB) based on DNA methylation patterns, resulting in distinct clinical outcomes but posing challenges for molecular diagnosis.
Goal(s): We aimed to develop a radiomics model based on multimodal MRI to predict PF-EPN subtypes and prognosis.
Approach: Using a large cohort, we developed a support vector machine (SVM) classifier that utilizes T1WI, T2WI, CET1WI and age to differentiate PFA from PFB.
Results: The model achieved AUCs of 0.937, 0.926, with accuracies of 0.927, 0.875 in internal and prospective test sets, respectively, and successfully stratified PF-EPNs into high- and low-risk groups.
Impact: The multimodal MRI-based radiomics model predicts molecular subtypes of PF-EPNs and enables risk stratification, provides non-invasive insights for clinical treatment decisions. This approach facilitates patient selection for targeted genetic analysis, enhances treatment precision, and improves monitoring and family counseling.
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