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  • Prediction of Consistency of Meningioma by Magnetic Resonance (MR) Imaging

    Final Number:

    Kyle Anthony Smith MD; Roukoz B. Chamoun MD; John Leever MD; Ernest John Madarang

    Study Design:

    Subject Category:

    Meeting: Congress of Neurological Surgeons 2013 Annual Meeting

    Introduction: Meningioma consistency is an important characteristic as it affects the difficulty of surgery. Regarding preoperative prediction of consistency, several methods have been proposed; however, they lack objectivity and reproducibility. We propose a new method for prediction based on Tumor to Cerebellar peduncle T2-weighted imaging Intensity (TCTI) ratios and introduce an objective, intraoperative grading method.

    Methods: Twenty consecutive patients who underwent resection of meningioma were examined. An intraoperative consistency scale was applied to these lesions prospectively by the operating surgeon based on Cavitron ultrasonic aspirator intensity used. Tumors were classified as A, very soft; B, soft/intermediate; or C, fibrous. Using T2-weighted MR sequence, regions of interest were chosen from the lesion and from the cerebellar peduncle, and the TCTI ratio was calculated. Tumor consistency grades and TCTI ratios were then correlated.

    Results: Of the 20 tumors evaluated prospectively, 7 were classified as very soft, 9 as soft/intermediate, and 4 as fibrous. TCTI ratios for fibrous tumors were all </= 1; very soft tumors were >/= 1.8, except for one outlier of 1.66; and soft/intermediate tumors were >1 to <1.8.

    Conclusions: Predicting the consistency of meningioma can significantly affect the decision-making of the operating surgeon. We propose a method using quantifiable ROI intensity ratios of tumor in comparison to the cerebellar peduncle, as a uniform and reproducible way to predict tumor consistency. Additionally, the intraoperative consistency was graded in an objective and clinically significant way.

    Patient Care: Predicting the consistency of meningioma can affect the decision-making of the surgeon. We propose a method of tumor consistency prediction which may better assist surgeons with preoperative planning for tumor resection.

    Learning Objectives: By the conclusion of this session, participants should be able to: 1) Understand the importance of meningioma consistency and its impact on surgical difficulty, 2) Describe current methods of preoperative tumor consistency prediction, and 3) Understand our proposed tumor consistency grading scale and method of T2-weighted imaging prediction.

    References: 1. Carpeggiani P, Crisi G, Trevisan C. MRI of intracranial meningiomas: correlations with histology and physical consistency. Neuroradiol. 1993;35:532-6. 2. Chernov MF, Kasuya H, Nakaya K, Kato K, Ono Y, Yoshida S, et al. H-MRS of intracranial meningiomas: what it can add to known clinical and MRI predictors of the histopathological and biological characteristics of the tumor? Clin Neurol Neurosur. 2011;113:202-12. 3. 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. 4. Kashimura H, Inoue T, Ogasawara K, Arai H, Otawara Y, Kanbara Y, et al. Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg. 2007;107(4):784-7. 5. Kendall B, Pullicino P. Comparison of consistency of meningiomas and CT appearances. Neuroradiol. 1979;18:173-6. 6. Maiuri F, Iaconetta G, de Divitiis O, Cirillo S, Di Salle F, De Caro ML. Intracranial meningiomas: correlations between MR imaging and histology. Euro J Radiol. 1997;31:69-75. 7. Murphy MC, Huston III J, Glaser KJ, Manduca A, Meyer FB, Lanzino G, et al. Preoperative assessment of meninioma stiffness using magnetic resonance elastography. J Neurosurg. 2013;118:643-8. 8. Pfisterer WK, Nieman RA, Scheck AC, Coons SW, Spetzler RF, Preul MC. Using ex vivo proton magnetic resonance spectroscopy to reveal associations between biochemical and biological features of meningiomas. Neurosurg Focus. 2010;28(1):E12. 9. Sitthinamsuwan B, Khampalikit I, Witthiwej T, Nitising A. Predictors of meningioma consistency: a study in 243 consecutive cases. Acta Neurochir. 2012;154:1383-9. 10. Soyama N, Kuratsu J-i, Ushio Y. Correlation between magnetic resonance images and histology in meningiomas: T2-weighted images indicate collagen contents in tissues. Neurol Med Chir. 1995;35:438-41. 11. Suzuki Y, Sugimoto T, Shibuya M, Sugita K, Patel SJ. Meningiomas: correlation between MRI characteristics and operative findings including consistency. Acta Neurochir. 1994;129:39-46. 12. 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;48:579-83. 13. Yrjana SK, Tuominen H, Karttunen A, Lahdesluoma N, Heikkinen E, Koivukangas J. Low-field MR imaging of meningiomas including dynamic contrast enhancement study: evaluation of surgical and histopathologic characteristics. Am J Neuroradiol. 2006;27:2128-34.

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