Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi‐center cohort study of 114 cases

Author:

Li Liyan1ORCID,Fu Yan2,Zhang Yinping2,Mao Yipu3,Huang Deyou4,Yi Xiaoping25678ORCID,Wang Jing9,Tan Zeming10,Jiang Muliang1,Chen Bihong T.11

Affiliation:

1. Department of Radiology First Affiliated Hospital of Guangxi Medical University Nanning P. R. China

2. Department of Radiology Xiangya Hospital, Central South University Changsha P. R. China

3. Department of Radiology Nanning First People's Hospital Nanning P. R. China

4. Department of Radiology Affiliated Hospital of Youjiang Medical University for Nationalities Baise P. R. China

5. National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology Xiangya Hospital Changsha P. R. China

6. National Clinical Research Center for Geriatric Disorders Xiangya Hospital, Central South University Changsha P. R. China

7. Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital Central South University Changsha P. R. China

8. Hunan Engineering Research Center of Skin Health and Disease Xiangya Hospital, Central South University Changsha P. R. China

9. Department of Neurology Xiangya Hospital, Central South University Changsha P. R. China

10. Department of Neurosurgery Xiangya Hospital, Central South University Changsha P. R. China

11. Department of Diagnostic Radiology City of Hope National Medical Center Duarte California USA

Abstract

AbstractBackgroundIntracranial extraventricular ependymoma (IEE) is an ependymoma located in the brain parenchyma outside the ventricles. IEE has overlapping clinical and imaging characteristics with glioblastoma multiforme (GBM) but different treatment strategy and prognosis. Therefore, an accurate preoperative diagnosis is necessary for optimizing therapy for IEE.MethodsA retrospective multicenter cohort of IEE and GBM was identified. MR imaging characteristics assessed with the Visually Accessible Rembrandt Images (VASARI) feature set and clinicopathological findings were recorded. Independent predictors for IEE were identified using multivariate logistic regression, which was used to construct a diagnostic score for differentiating IEE from GBM.ResultsCompared to GBM, IEE tended to occur in younger patients. Multivariate logistic regression analysis identified seven independent predictors for IEE. Among them, 3 predictors including tumor necrosis rate (F7), age, and tumor‐enhancing margin thickness (F11), demonstrated higher diagnostic performance with an Area Under Curve (AUC) of more than 70% in distinguishing IEE from GBM. The AUC was 0.85, 0.78, and 0.70, with sensitivity of 92.98%, 72.81%, and 96.49%, and specificity of 65.50%, 73.64%, and 43.41%, for F7, age, and F11, respectively.ConclusionWe identified specific MR imaging features such as tumor necrosis and thickness of enhancing tumor margins that could help to differentiate IEE from GBM. Our study results should be helpful to assist in diagnosis and clinical management of this rare brain tumor.

Funder

Natural Science Foundation of Hunan Province

Publisher

Wiley

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology

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