Meningioma grading using conventional MRI histogram analysis based on 3D tumor measurement.


Journal

European journal of radiology
ISSN: 1872-7727
Titre abrégé: Eur J Radiol
Pays: Ireland
ID NLM: 8106411

Informations de publication

Date de publication:
Jan 2019
Historique:
received: 10 08 2018
revised: 04 11 2018
accepted: 18 11 2018
entrez: 3 1 2019
pubmed: 3 1 2019
medline: 26 2 2019
Statut: ppublish

Résumé

To evaluate the application of conventional MRI histogram analysis based on the whole tumor measurement on assessing meningioma grading. This retrospective study was approved by the institutional review board. A total amount of 90 patients with meningioma were enrolled and the preoperative MRI of them were analyzed. To be specific, the patient group were consisted of 45 patients with grade I, 38 with grade II, and 7 with grade III meningioma. Grade I meningioma is classified as low grade meningioma (LGM), whereas Grade II and III meningioma were combined and classified as high grade meningioma (HGM). ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI. 3D ROI signal intensity histogram and all its parameters were obtained. Independent t-test and Kruskal-Wallis test were used for comparison between two groups. Univariate logistic regression analysis and Spearman's correlation analysis were used to screen for the parameters with high predictive efficiency, while multivariate logistic regression analysis was used to determine the optimal model for the classification of meningioma. There were significant differences observed between HGM and LGM groups regarding to histogram volume count, uniformity of three sequences, range of T1WI and T2WI, kurtosis, standard deviation, variance, max intensity of T2WI, skewness, mean deviation, minimum intensity, mean value, the 5 Histogram analysis of conventional MRI based on 3D tumor measurement can be applied in the assessment of meningioma grading in clinical.

Identifiants

pubmed: 30599872
pii: S0720-048X(18)30411-X
doi: 10.1016/j.ejrad.2018.11.016
pii:
doi:

Types de publication

Evaluation Study Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

45-53

Informations de copyright

Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.

Auteurs

Xiaoxin Li (X)

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Yanwei Miao (Y)

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. Electronic address: ywmiao716@163.com.

Liang Han (L)

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Junyi Dong (J)

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Yan Guo (Y)

Life Science, GE Healthcare, Shenyang, Liaoning, China.

Yuqing Shang (Y)

Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, Connecticut, USA.

Lizhi Xie (L)

GE Healthcare, MR Research China, Beijing, China.

Qingwei Song (Q)

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Ailian Liu (A)

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

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