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Magnetic Resonance Fingerprinting for Preoperative Meningioma Consistency Prediction

Published:November 05, 2021DOI:https://doi.org/10.1016/j.acra.2021.09.008

      Rationale and Objectives

      Preoperative meningioma consistency prediction is highly beneficial for surgical planning and prognostication. We aimed to use magnetic resonance fingerprinting (MRF)-derived T1 and T2 values to preoperatively predict meningioma consistency.

      Materials and Methods

      A total of 51 patients with meningiomas were enrolled in this study. MRF, T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging were performed in all patients before surgery using a 3T MRI scanner. MRF-derived T1 and T2 values, T1-weightd and T2-weighted signal intensities, as well as apparent diffusion coefficient value yield from diffusion-weighted imaging were compared between the soft, moderate and hard meningiomas. Receiver operating characteristic curve analyses were used to determine the diagnostic performance of T1, T2 value, and a combination of T1 and T2 values.

      Results

      After Bonferroni corrections, quantitative T1 and T2 values yielded from MRF were significantly different between the soft, moderate and hard meningiomas (all p < 0.05). T2 signal intensity was significantly different between the soft and hard, soft and moderate meningiomas (both p < 0.05), whereas was not significantly different between the moderate and hard meningiomas. However, T1 signal intensity and apparent diffusion coefficient value had no significant differences between the soft, moderate and hard meningiomas (all p > 0.05). The combination of T1 and T2 values had greater areas under receiver operating characteristic curve curves compared to individual T1 or T2 value.

      Conclusion

      MRF may help to preoperatively differentiate between the soft, moderate and hard meningiomas and may be useful in guiding the surgical planning.

      Key Words

      Abbreviations:

      AUC (area under the ROC curve), ADC (apparent diffusion coefficient), DWI (diffusion-weighted imaging), FOV (field of view), ICC (intraclass correlation coefficient), MRE (magnetic resonance elastography), MRF (magnetic resonance fingerprinting), MRI (magnetic resonance imaging), ROC (receiver operating characteristic curve), ROI (region of interest), TE (echo time), TR (repetition time), T1WI (T1-weighted imaging), T2WI (T2-weighted imaging), WHO (World Health Organization)
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      References

        • Rogers L
        • Barani I
        • Chamberlain M
        • et al.
        Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review.
        J Neurosurg. 2015; 122: 4-23
        • Marosi C
        • Hassler M
        • Roessler K
        • et al.
        Meningioma.
        Crit Rev Oncol Hematol. 2008; 67: 153-171
        • Watanabe K
        • Kakeda S
        • Yamamoto J
        • et al.
        Prediction of hard meningiomas: quantitative evaluation based on the magnetic resonance signal intensity.
        Acta Radiologica. 2016; 57: 333-340
        • Smith KA
        • Leever JD
        • Chamoun RB.
        Predicting consistency of meningioma by magnetic resonance imaging.
        J Neurol Surg B Skull Base. 2015; 76: 225-229
        • Little KM
        • Friedman AH
        • Sampson JH
        • et al.
        Surgical management of petroclival meningiomas: defining resection goals based on risk of neurological morbidity and tumor recurrence rates in 137 patients.
        Neurosurgery. 2005; 56: 546-559
        • Hoover JM
        • Morris JM
        • Meyer FB.
        Use of preoperative magnetic resonance imaging T1 and T2 sequences to determine intraoperative meningioma consistency.
        Surg Neurol Int. 2011; 2: 142
        • Carpeggiani P
        • Crisi G
        • Trevisan C.
        MRI of intracranial meningiomas: correlations with histology and physical consistency.
        Neuroradiology. 1993; 35: 532-536
        • Yao A
        • Pain M
        • Balchandani P
        • et al.
        Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review.
        Neurosurg Rev. 2018; 41: 745-753
        • Sitthinamsuwan B
        • Khampalikit I
        • Nunta-aree S
        • et al.
        Predictors of meningioma consistency: a study in 243 consecutive cases.
        Acta Neurochir (Wien). 2012; 154: 1383-1389
        • Ma D
        • Gulani V
        • Seiberlich N
        • et al.
        Magnetic resonance fingerprinting.
        Nature. 2013; 495: 187-192
        • Körzdörfer G
        • Kirsch R
        • Liu K
        • et al.
        Reproducibility and repeatability of MR fingerprinting relaxometry in the human brain.
        Radiology. 2019; 292: 429-437
        • Badve C
        • Yu A
        • Dastmalchian S
        • et al.
        MR fingerprinting of adult brain tumors: initial experience.
        AJNR Am J Neuroradiol. 2017; 38: 492-499
        • Liao C
        • Wang K
        • Cao X
        • et al.
        Detection of lesions in mesial temporal lobe epilepsy by using MR fingerprinting.
        Radiology. 2018; 288: 804-812
        • Wang K
        • Cao X
        • Wu D
        • et al.
        Magnetic resonance fingerprinting of temporal lobe white matter in mesial temporal lobe epilepsy.
        Ann Clin Transl Neurol. 2019; 6: 1639-1646
        • Jiang Y
        • Ma D
        • Seiberlich N
        • et al.
        MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout.
        Magn Reson Med. 2015; 74: 1621-1631
        • Shiroishi MS
        • Cen SY
        • Tamrazi B
        • et al.
        Predicting meningioma consistency on preoperative neuroimaging studies.
        Neurosurg Clin N Am. 2016; 27: 145-154
        • Nitta H
        • Yamashima T
        • Yamashita J
        • et al.
        An ultrastructural and immunohistochemical study of extracellular matrix in meningiomas.
        Histol Histopathol. 1990; 5: 267-274
        • Du G
        • Lewis MM
        • Sica C
        • et al.
        Magnetic resonance T1w/T2w ratio: a parsimonious marker for Parkinson disease.
        Ann Neurol. 2019; 85: 96-104
        • Mittal S
        • Pradhan G
        • Singh S
        • et al.
        T1 and T2 mapping of articular cartilage and menisci in early osteoarthritis of the knee using 3-Tesla magnetic resonance imaging.
        Pol J Radiol. 2019; 84: e549-e564
        • Shiguetomi-Medina JM
        • Ramirez-Gl JL
        • Stødkilde-Jørgensen H
        • et al.
        Systematized water content calculation in cartilage using T1-mapping MR estimations: design and validation of a mathematical model.
        J Orthop Traumatol. 2017; 18: 217-220
        • Badve C
        • Yu A
        • Rogers M
        • et al.
        Simultaneous T1 and T2 brain relaxometry in asymptomatic volunteers using magnetic resonance fingerprinting.
        Tomography. 2015; 1: 136-144
        • Ma D
        • Jones SE
        • Deshmane A
        • et al.
        Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions.
        J Magn Reson Imaging. 2019; 49: 1333-1346
        • European Society of Radiology (ESR)
        Magnetic Resonance Fingerprinting - a promising new approach to obtain standardized imaging biomarkers from MRI.
        Insights Imaging. 2015; 6: 163-165
        • Romani R
        • Tang WJ
        • Mao Y
        • et al.
        Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas.
        Acta Neurochir (Wien). 2014; 156: 1837-1845
        • Murphy MC
        • Huston J
        • Glaser KJ
        • et al.
        Preoperative assessment of meningioma stiffness using magnetic resonance elastography.
        J Neurosurg. 2013; 118: 643-648
        • Sotak CH.
        Nuclear magnetic resonance (NMR) measurement of the apparent diffusion coefficient (ADC) of tissue water and its relationship to cell volume changes in pathological states.
        Neurochem Int. 2004; 45: 569-582
        • Tropine A
        • Dellani PD
        • Glaser M
        • et al.
        Differentiation of fibroblastic meningiomas from other benign subtypes using diffusion tensor imaging.
        J Magn Reson Imaging. 2007; 25: 703-708
        • Murphy MC
        • Huston 3rd, J
        • Glaser KJ
        • et al.
        Preoperative assessment of meningioma stiffness using magnetic resonance elastography.
        J Neurosurg. 2013; 118: 643-648