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Abstract #1384

Multimodal MRI-based radiomics model for molecular subtypes prediction and prognosis evaluation of posterior fossa ependymoma

Yangyang Li1, Dan Cheng1, Junjie Li1, Zhizheng Zhuo1, Minghao Wu1, Xianchang Zhang2, and Yaou Liu1
1Departments of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 2Siemens Healthiness Ltd, MR Research Collaboration Team, Beijing, China

Synopsis

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.

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