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Abstract #1543

Machine learning radiomics for prediction of posterior cranial fossa ependymoma PFA and PFB subgroups

Rui Xu1, Hanjiaerbieke Kukun1, Guangxu Han2, and Yunling Wang1
1The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 2GE HealthCare MR Research, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Radiomics

Motivation: Ependymoma (EP) is a prevalent intracranial tumor in children, with significant implications of its molecular subtypes on treatment outcomes. However, there is currently a lack of non-invasive methods to distinguish between these molecular subtypes of EP.

Goal(s): To develop an non-invasive method to distinguish between these molecular subtypes of EP.

Approach: MRI data from 43 ependymoma patients was analyzed in this study, with radiomic features and machine learning used to distinguish molecular subtypes.

Results: The AUC for the proposed model was 0.840 (0.723-0.957) in the training group and 0.900 (0.698-1.000) in the test group.

Impact: The proposed machine learning model effectively distinguishes between the molecular subtypes of ependymoma, showing strong performance and enhancing diagnostic accuracy, which is expected to provide valuable insights for clinical decision-making.

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Keywords