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CC BY-NC-ND 4.0 · Asian J Neurosurg 2022; 17(04): 614-620
DOI: 10.1055/s-0042-1758849
Original Article

Benefits of Combined MRI Sequences in Meningioma Consistency Prediction: A Prospective Study of 287 Consecutive Patients

Kriengsak Limpastan
1   Neurosurgery Unit, Clinical Surgical Research Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
,
Kittisak Unsrisong
2   Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
,
Tanat Vaniyapong
1   Neurosurgery Unit, Clinical Surgical Research Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
,
Thunya Norasetthada
1   Neurosurgery Unit, Clinical Surgical Research Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
,
Wanarak Watcharasaksilp
1   Neurosurgery Unit, Clinical Surgical Research Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
,
Chumpon Jetjumnong
1   Neurosurgery Unit, Clinical Surgical Research Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
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Abstract

Objective Consistency of meningiomas is one of the most important factors affecting the completeness of removal and major risks of meningioma surgery. This study used preoperative magnetic resonance imaging (MRI) sequences in single and in combination to predict meningioma consistency.

Methods The prospective study included 287 intracranial meningiomas operated on by five attending neurosurgeons at Chiang Mai University Hospital from July 2012 through June 2020. The intraoperative consistency was categorized in four grades according to the method of surgical removal and intensity of ultrasonic aspirator, then correlated with preoperative tumor signal intensity pattern on MRI including T1-weighted image, T2-weighted image (T2WI), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted image (DWI), which were described as hypointensity, isointensity, and hyperintensity signals which were blindly interpreted by one neuroradiologist.

Results Among 287 patients, 29 were male and 258 female. The ages ranged from 22 to 83 years. A total of 189 tumors were situated in the supratentorial space and 98 were in the middle fossa and infratentorial locations. Note that 125 tumors were found to be of soft consistency (grades 1, 2) and 162 tumors of hard consistency (grades 3, 4). Hyperintensity signals on T2WI, FLAIR, and DWI were significantly associated with soft consistency of meningiomas (relative risk [RR] 2.02, 95% confidence interval [CI] 1.35–3.03, p = 0.001, RR 2.19, 95% CI 1.43–3.35, p < 0.001, and RR 1.47, 95% CI 1.02–2.11, p = 0.037, respectively). Further, chance to be soft consistency significantly increased when two and three hyperintensity signals were combined (RR 2.75, 95% CI 1.62–4.65, p ≤ 0.001, RR 2.79, 95% CI 1.58–4.93, p < 0.001, respectively). Hypointensity signals on T2WI, FLAIR, and DWI were significantly associated with hard consistency of meningiomas (RR 1.82, 95% CI 1.18–2.81, p = 0.007, RR 1.80, 95% CI 1.15–2.83, p = 0.010, RR 1.67, 95% CI 1.07–2.59, p = 0.023, respectively) and chance to be hard consistency significantly increased when three hypointensity signals were combined (RR 1.82, 95% CI 1.11–2.97, p = 0.017).

Conclusion T2WI, FLAIR, and DWI hyperintensity signals of the meningiomas was solely significantly associated with soft consistency and predictive value significantly increased when two and three hyperintensity signals were combined. Each of hypointensity signals on T2WI, FLAIR, and DWI was significantly associated with hard consistency of tumors and tendency to be hard consistency significantly increased when hypointensity was found in all three sequences.

Keywords

combined MRI - consistency - meningioma consistency - preoperative MRI - prediction - sequences


Publikationsverlauf

Artikel online veröffentlicht:
10. Dezember 2022

© 2022. Asian Congress of Neurological Surgeons. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Ong T, Bharatha A, Alsufayan R, Das S, Lin AW. MRI predictors for brain invasion in meningiomas. Neuroradiol J 2021; 34 (01) 3-7
    CrossrefPubMedGoogle Scholar
  • 2 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 (10) 1837-1845
    CrossrefPubMedGoogle Scholar
  • 3 Bai Y, Zhang R, Zhang X. et al. Magnetic resonance fingerprinting for preoperative meningioma consistency prediction. Acad Radiol 2022; 29 (08) e157-e165
    CrossrefPubMedGoogle Scholar
  • 4 Cepeda S, Arrese I, García-García S. et al. Meningioma consistency can be defined by combining the radiomic features of magnetic resonance imaging and ultrasound elastography. A pilot study using machine learning classifiers. World Neurosurg 2021; 146: e1147-e1159
    CrossrefPubMedGoogle Scholar
  • 5 Kashimura H, Inoue T, Ogasawara K. et al. Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg 2007; 107 (04) 784-787
    CrossrefPubMedGoogle Scholar
  • 6 Miyoshi K, Wada T, Uwano I. et al. Predicting the consistency of intracranial meningiomas using apparent diffusion coefficient maps derived from preoperative diffusion-weighted imaging. J Neurosurg 2020; 1-8 DOI: 10.3171/2020.6.JNS20740.
    CrossrefPubMedGoogle Scholar
  • 7 Shiroishi MS, Cen SY, Tamrazi B. et al. Predicting meningioma consistency on preoperative neuroimaging studies. Neurosurg Clin N Am 2016; 27 (02) 145-154
    CrossrefPubMedGoogle Scholar
  • 8 Sitthinamsuwan B, Khampalikit I, Nunta-aree S, Srirabheebhat P, Witthiwej T, Nitising A. Predictors of meningioma consistency: a study in 243 consecutive cases. Acta Neurochir (Wien) 2012; 154 (08) 1383-1389
    CrossrefPubMedGoogle Scholar
  • 9 Smith KA, Leever JD, Chamoun RB. Predicting consistency of meningioma by magnetic resonance imaging. J Neurol Surg B Skull Base 2015; 76 (03) 225-229
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 10 Watanabe K, Kakeda S, Yamamoto J. et al. Prediction of hard meningiomas: quantitative evaluation based on the magnetic resonance signal intensity. Acta Radiol 2016; 57 (03) 333-340
    CrossrefPubMedGoogle Scholar
  • 11 Zada G, Yashar P, Robison A. et al. A proposed grading system for standardizing tumor consistency of intracranial meningiomas. Neurosurg Focus 2013; 35 (06) E1
    CrossrefPubMedGoogle Scholar
  • 12 Suzuki Y, Sugimoto T, Shibuya M, Sugita K, Patel SJ. Meningiomas: correlation between MRI characteristics and operative findings including consistency. Acta Neurochir (Wien) 1994; 129 (1-2): 39-46
    CrossrefPubMedGoogle Scholar
  • 13 Chen TC, Zee CS, Miller CA. et al. Magnetic resonance imaging and pathological correlates of meningiomas. Neurosurgery 1992; 31 (06) 1015-1021 , discussion 1021–1022
    PubMedGoogle Scholar
  • 14 Maiuri F, Iaconetta G, de Divitiis O, Cirillo S, Di Salle F, De Caro ML. Intracranial meningiomas: correlations between MR imaging and histology. Eur J Radiol 1999; 31 (01) 69-75
    CrossrefPubMedGoogle Scholar
  • 15 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
    CrossrefPubMedGoogle Scholar
  • 16 Yamaguchi N, Kawase T, Sagoh M, Ohira T, Shiga H, Toya S. Prediction of consistency of meningiomas with preoperative magnetic resonance imaging. Surg Neurol 1997; 48 (06) 579-583
    CrossrefPubMedGoogle Scholar
  • 17 Claus EB, Bondy ML, Schildkraut JM, Wiemels JL, Wrensch M, Black PM. Epidemiology of intracranial meningioma. Neurosurgery 2005; 57 (06) 1088-1095 , discussion 1088–1095
    CrossrefPubMedGoogle Scholar
  • 18 Pamir MNBP, Fahlbusch R. Meningiomas: A Comprehensive Text. Philadelphia, PA: Elsevier; 2010
    Google Scholar
  • 19 Limpastan K. Planum sphenoidale meningioma with major vascular encasement: a report of 2 cases. Bull Neurosurg Assoc Thailand 2000; 10 (01) 3-10
    Google Scholar
  • 20 Watts J, Box G, Galvin A, Brotchie P, Trost N, Sutherland T. Magnetic resonance imaging of meningiomas: a pictorial review. Insights Imaging 2014; 5 (01) 113-122
    CrossrefPubMedGoogle Scholar
  • 21 Chen CJ, Tseng YC, Hsu HL, Jung SM. Microcystic meningioma: importance of obvious hypointensity on T1-weighted magnetic resonance images. J Comput Assist Tomogr 2008; 32 (01) 130-134
    CrossrefPubMedGoogle Scholar
  • 22 Radwan W, Lucke-Wold B, Cheyuo C, Ahn J, Gyure K, Bhatia S. Lipomatous meningioma: case report and review of the literature. Case Stud Surg 2016; 2 (04) 58-61
    CrossrefPubMedGoogle Scholar
  • 23 Shaman MA, Zak IT, Kupsky WJ. Best cases from the AFIP: involuted sclerotic meningioma. Radiographics 2003; 23 (03) 785-789
    CrossrefPubMedGoogle Scholar
  • 24 Ortega-Porcayo LA, Ballesteros-Zebadúa P, Marrufo-Meléndez OR. et al. Prediction of mechanical properties and subjective consistency of meningiomas using T1-T2 assessment versus fractional anisotropy. World Neurosurg 2015; 84 (06) 1691-1698
    CrossrefPubMedGoogle Scholar
  • 25 Smith KA, Leever JD, Hylton PD, Camarata PJ, Chamoun RB. Meningioma consistency prediction utilizing tumor to cerebellar peduncle intensity on T2-weighted magnetic resonance imaging sequences: TCTI ratio. J Neurosurg 2017; 126 (01) 242-248
    CrossrefPubMedGoogle Scholar