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Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.

TitleGrading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.
Publication TypeJournal Article
Year of Publication2020
AuthorsZhang S, Chiang GChia-Yi, Knapp JMarion, Zecca CM, He D, Ramakrishna R, Magge RS, Pisapia DJ, Fine HAlan, Tsiouris AJohn, Zhao Y, Heier LA, Wang Y, Kovanlikaya I
JournalJ Neuroradiol
Volume47
Issue4
Pagination272-277
Date Published2020 Jun
ISSN0150-9861
KeywordsAged, Brain, Female, Humans, Male, Meningeal Neoplasms, Meningioma, Middle Aged, Multiparametric Magnetic Resonance Imaging, Neoplasm Grading, Retrospective Studies, ROC Curve
Abstract

BACKGROUND AND PURPOSE: The ability to predict high-grade meningioma preoperatively is important for clinical surgical planning. The purpose of this study is to evaluate the performance of comprehensive multiparametric MRI, including susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) in predicting high-grade meningioma both qualitatively and quantitatively.

METHODS: Ninety-two low-grade and 37 higher grade meningiomas in 129 patients were included in this study. Morphological characteristics, quantitative histogram analysis of QSM and ADC images, and tumor size were evaluated to predict high-grade meningioma using univariate and multivariate analyses. Receiver operating characteristic (ROC) analyses were performed on the morphological characteristics. Associations between Ki-67 proliferative index (PI) and quantitative parameters were calculated using Pearson correlation analyses.

RESULTS: For predicting high-grade meningiomas, the best predictive model in multivariate logistic regression analyses included calcification (β=0.874, P=0.110), peritumoral edema (β=0.554, P=0.042), tumor border (β=0.862, P=0.024), tumor location (β=0.545, P=0.039) for morphological characteristics, and tumor size (β=4×10, P=0.004), QSM kurtosis (β=-5×10, P=0.058), QSM entropy (β=-0.067, P=0.054), maximum ADC (β=-1.6×10, P=0.003), ADC kurtosis (β=-0.013, P=0.014) for quantitative characteristics. ROC analyses on morphological characteristics resulted in an area under the curve (AUC) of 0.71 (0.61-0.81) for a combination of them. There were significant correlations between Ki-67 PI and mean ADC (r=-0.277, P=0.031), 25 percentile of ADC (r=-0.275, P=0.032), and 50 percentile of ADC (r=-0.268, P=0.037).

CONCLUSIONS: Although SWI and QSM did not improve differentiation between low and high-grade meningiomas, combining morphological characteristics and quantitative metrics can help predict high-grade meningioma.

DOI10.1016/j.neurad.2019.05.002
Alternate JournalJ Neuroradiol
PubMed ID31136748
PubMed Central IDPMC6876125
Grant ListR01 NS090464 / NS / NINDS NIH HHS / United States
R01 NS095562 / NS / NINDS NIH HHS / United States