• Hanbing Shao, Ni Chen, Xiaorui Su, Linmao Zheng, Xibiao Yang, Xinyue Wan, Simin Zhang, Qiaoyue Tan, Shuang Li, Qiyong Gong, and Qiang Yue.
• Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.
• World Neurosurg. 2023 Jul 1; 175: e1283e1291e1283-e1291.

ObjectiveTo explore the predictive value of quantitative features extracted from conventional magnetic resonance imaging (MRI) in distinguishing Zinc Finger Translocation Associated (ZFTA)-RELA fusion-positive and wild-type ependymomas.MethodsTwenty-seven patients with pathologically confirmed ependymomas (17 patients with ZFTA-RELA fusions and 10 ZFTA-RELA fusion-negative patients) who underwent conventional MRI were enrolled in this retrospective study. Two experienced neuroradiologists who were blinded to the histopathological subtypes independently extracted imaging features using Visually Accessible Rembrandt Images annotations. The consistency between the readers was evaluated with the Kappa test. The imaging features with significant differences between the 2 groups were obtained using the least absolute shrinkage and selection operator regression model. Logistic regression analysis and receiver operating characteristic analysis were performed to analyze the diagnostic performance of the imaging features in predicting the ZFTA-RELA fusion status in ependymoma.ResultsThere was a good interevaluator agreement on the imaging features (kappa value range 0.601-1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive performance in identifying ZFTA-RELA fusion-positive and ZFTA-RELA fusion-negative ependymomas (C-index = 0.862 and area under the curve= 0.8618).ConclusionsQuantitative features extracted from preoperative conventional MRI by Visually Accessible Rembrandt Images provide high discriminatory accuracy in predicting the ZFTA-RELA fusion status of ependymoma.Copyright © 2023 Elsevier Inc. All rights reserved.

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