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

Xiaoxin Li; Yanwei Miao; Liang Han; Junyi Dong; Yan Guo; Yuqing Shang; Lizhi Xie; Qingwei Song; Ailian Liu

doi:10.1016/j.ejrad.2018.11.016

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

Eur J Radiol. 2019 Jan:110:45-53. doi: 10.1016/j.ejrad.2018.11.016. Epub 2018 Nov 22.

Authors

Xiaoxin Li¹, Yanwei Miao², Liang Han¹, Junyi Dong¹, Yan Guo³, Yuqing Shang⁴, Lizhi Xie⁵, Qingwei Song¹, Ailian Liu¹

Affiliations

¹ Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
² Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. Electronic address: ywmiao716@163.com.
³ Life Science, GE Healthcare, Shenyang, Liaoning, China.
⁴ Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, Connecticut, USA.
⁵ GE Healthcare, MR Research China, Beijing, China.

PMID: 30599872
DOI: 10.1016/j.ejrad.2018.11.016

Abstract

Purpose: To evaluate the application of conventional MRI histogram analysis based on the whole tumor measurement on assessing meningioma grading.

Materials and methods: 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.

Results: 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^th percentile, the 10^th percentile, the 25^th percentile, the 50^th percentile, the 75^th percentile, and the 90^th percentile of contrasted T1WI. Volume count and uniformity were high predictive parameters in distinguishing HGM from LGM. Logistic regression model included contrasted T1WI histogram parameters (i.e. minimum intensity, volume count, skewness, uniformity, and the 75^th percentile) showed the best diagnostic efficiency for meningioma grade, with a sensitivity and specificity of 83.9% and 77.4% (AUC = 0.834, cutoff value = 0.413), respectively. The optimal model was achieved with a sensitivity of 71.4% and a specificity of 78.6% in the test set (AUC = 0.791, cutoff value = 0.413).

Conclusions: Histogram analysis of conventional MRI based on 3D tumor measurement can be applied in the assessment of meningioma grading in clinical.

Keywords: Grade; Histogram; Magnetic resonance imaging; Meningiomas.

Publication types

Evaluation Study

MeSH terms

Female
Humans
Magnetic Resonance Imaging / methods
Male
Meningeal Neoplasms / pathology*
Meningioma / pathology*
Middle Aged
Neoplasm Grading / methods
Retrospective Studies
Sensitivity and Specificity