Predicting the consistency of intracranial meningiomas using apparent diffusion coefficient maps derived from preoperative diffusion-weighted imaging

View More View Less
  • 1 Department of Neurosurgery and
  • 2 Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, and
  • 3 Division of Medical Engineering, Department of Information Science, Iwate Medical University School of Medicine, Morioka; and
  • 4 Department of Physical Therapy, Hirosaki University School of Health Science, Hirosaki, Japan
Restricted access

Purchase Now

USD  $45.00

JNS + Pediatrics - 1 year subscription bundle (Individuals Only)

USD  $505.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $600.00
Print or Print + Online

OBJECTIVE

The consistency of meningiomas is a critical factor affecting the difficulty of resection, operative complications, and operative time. The apparent diffusion coefficient (ADC) is derived from diffusion-weighted imaging (DWI) and is calculated using two optimized b values. While the results of comparisons between the standard ADC and the consistency of meningiomas vary, the shifted ADC has been reported to be strongly correlated with liver stiffness. The purpose of the present prospective cohort study was to determine whether preoperative standard and shifted ADC maps predict the consistency of intracranial meningiomas.

METHODS

Standard (b values 0 and 1000 sec/mm2) and shifted (b values 200 and 1500 sec/mm2) ADC maps were calculated using preoperative DWI in patients undergoing resection of intracranial meningiomas. Regions of interest (ROIs) were placed within the tumor on standard and shifted ADC maps and registered on the navigation system. Tumor tissue located at the registered ROI was resected through craniotomy, and its stiffness was measured using a durometer. The cutoff point lying closest to the upper left corner of a receiver operating characteristic (ROC) curve was determined for the detection of tumor stiffness such that an ultrasonic aspirator or scissors was always required for resection. Each tumor tissue sample with stiffness greater than or equal to or less than this cutoff point was defined as hard or soft tumor, respectively.

RESULTS

For 76 ROIs obtained from 25 patients studied, significant negative correlations were observed between stiffness and the standard ADC (ρ = −0.465, p < 0.01) and the shifted ADC (ρ = −0.490, p < 0.01). The area under the ROC curve for detecting hard tumor (stiffness ≥ 20.8 kPa) did not differ between the standard ADC (0.820) and the shifted ADC (0.847) (p = 0.39). The positive predictive value (PPV) for the combination of a low standard ADC and a low shifted ADC for detecting hard tumor was 89%. The PPV for the combination of a high standard ADC and a high shifted ADC for detecting soft tumor (stiffness < 20.8 kPa) was 81%.

CONCLUSIONS

A combination of standard and shifted ADC maps derived from preoperative DWI can be used to predict the consistency of intracranial meningiomas.

ABBREVIATIONS ADC = apparent diffusion coefficient; AUC = area under the ROC curve; DWI = diffusion-weighted imaging; NPV = negative predictive value; PPV = positive predictive value; ROC = receiver operating characteristic; ROI = region of interest; SPGR = spoiled gradient–recalled acquisition in the steady state.

JNS + Pediatrics - 1 year subscription bundle (Individuals Only)

USD  $505.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $600.00

Contributor Notes

Correspondence Kuniaki Ogasawara: Iwate Medical University, Morioka, Japan. kuogasa@iwate-med.ac.jp.

INCLUDE WHEN CITING Published online November 13, 2020; DOI: 10.3171/2020.6.JNS20740.

Disclosures Dr. Ogasawara reports receiving a consigned research fund from Nihon Medi-Physics Co., Ltd.

  • 1

    Jääskeläinen J. Seemingly complete removal of histologically benign intracranial meningioma: late recurrence rate and factors predicting recurrence in 657 patients. A multivariate analysis. Surg Neurol. 1986; 26(5): 461469.

    • Search Google Scholar
    • Export Citation
  • 2

    Murphy MC, Huston J III, Glaser KJ, Preoperative assessment of meningioma stiffness using magnetic resonance elastography. J Neurosurg. 2013; 118(3): 643648.

    • Search Google Scholar
    • Export Citation
  • 3

    Sitthinamsuwan B, Khampalikit I, Nunta-aree S, Predictors of meningioma consistency: a study in 243 consecutive cases. Acta Neurochir (Wien). 2012; 154(8): 13831389.

    • Search Google Scholar
    • Export Citation
  • 4

    Yao A, Pain M, Balchandani P, Shrivastava RK. Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review. Neurosurg Rev. 2018; 41(3): 745753.

    • Search Google Scholar
    • Export Citation
  • 5

    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.

    • Search Google Scholar
    • Export Citation
  • 6

    Ortega-Porcayo LA, Ballesteros-Zebadúa P, Marrufo-Meléndez OR, Prediction of mechanical properties and subjective consistency of meningiomas using T1-T2 assessment versus fractional anisotropy. World Neurosurg. 2015; 84(6): 16911698.

    • Search Google Scholar
    • Export Citation
  • 7

    Smith KA, Leever JD, Chamoun RB. Predicting consistency of meningioma by magnetic resonance imaging. J Neurol Surg B Skull Base. 2015; 76(3): 225229.

    • Search Google Scholar
    • Export Citation
  • 8

    Watanabe K, Kakeda S, Yamamoto J, Prediction of hard meningiomas: quantitative evaluation based on the magnetic resonance signal intensity. Acta Radiol. 2016; 57(3): 333340.

    • Search Google Scholar
    • Export Citation
  • 9

    Kashimura H, Inoue T, Ogasawara K, Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg. 2007; 107(4): 784787.

    • Search Google Scholar
    • Export Citation
  • 10

    Romani R, Tang WJ, Mao Y, Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas. Acta Neurochir (Wien). 2014; 156(10): 18371845.

    • Search Google Scholar
    • Export Citation
  • 11

    Tropine A, Dellani PD, Glaser M, Differentiation of fibroblastic meningiomas from other benign subtypes using diffusion tensor imaging. J Magn Reson Imaging. 2007; 25(4): 703708.

    • Search Google Scholar
    • Export Citation
  • 12

    Yogi A, Koga T, Azama K, Usefulness of the apparent diffusion coefficient (ADC) for predicting the consistency of intracranial meningiomas. Clin Imaging. 2014; 38(6): 802807.

    • Search Google Scholar
    • Export Citation
  • 13

    Phuttharak W, Boonrod A, Thammaroj J, Preoperative MRI evaluation of meningioma consistency: a focus on detailed architectures. Clin Neurol Neurosurg. 2018; 169: 178184.

    • Search Google Scholar
    • Export Citation
  • 14

    Hughes JD, Fattahi N, Van Gompel J, Higher-resolution magnetic resonance elastography in meningiomas to determine intratumoral consistency. Neurosurgery. 2015; 77(4): 653659.

    • Search Google Scholar
    • Export Citation
  • 15

    Zada G, Yashar P, Robison A, A proposed grading system for standardizing tumor consistency of intracranial meningiomas. Neurosurg Focus. 2013; 35(6): E1.

    • Search Google Scholar
    • Export Citation
  • 16

    Le Bihan D, Ichikawa S, Motosugi U. Diffusion and intravoxel incoherent motion MR imaging–based virtual elastography: a hypothesis-generating study in the liver. Radiology. 2017; 285(2): 609619.

    • Search Google Scholar
    • Export Citation
  • 17

    Iima M, Kataoka M, Kanao S, Intravoxel incoherent motion and quantitative non-gaussian diffusion MR imaging: evaluation of the diagnostic and prognostic value of several markers of malignant and benign breast lesions. Radiology. 2018; 287(2): 432441.

    • Search Google Scholar
    • Export Citation
  • 18

    Uchiyama T, Nagaoka M. Durometer. Article in Japanese. Biomechanisms Japan. 2016; 40: 97102.

  • 19

    Kogo H, Miyabara H, Okawa H, Effect of body flexibility on the lumbar muscle stiffness of community-dwelling elderly. Rigakuryoho Kagaku. 2015; 30(4): 605608.

    • Search Google Scholar
    • Export Citation
  • 20

    Pepe MS, Longton G. Standardizing diagnostic markers to evaluate and compare their performance. Epidemiology. 2005; 16(5): 598603.

  • 21

    Maiuri F, Iaconetta G, de Divitiis O, Intracranial meningiomas: correlations between MR imaging and histology. Eur J Radiol. 1999; 31(1): 6975.

    • Search Google Scholar
    • Export Citation
  • 22

    Muthupillai R, Rossman PJ, Lomas DJ, Magnetic resonance imaging of transverse acoustic strain waves. Magn Reson Med. 1996; 36(2): 266274.

    • Search Google Scholar
    • Export Citation
  • 23

    Castillo M, Smith JK, Kwock L, Wilber K. Apparent diffusion coefficients in the evaluation of high-grade cerebral gliomas. AJNR Am J Neuroradiol. 2001; 22(1): 6064.

    • Search Google Scholar
    • Export Citation
  • 24

    Gauvain KM, McKinstry RC, Mukherjee P, Evaluating pediatric brain tumor cellularity with diffusion-tensor imaging. AJR Am J Roentgenol. 2001; 177(2): 449454.

    • Search Google Scholar
    • Export Citation
  • 25

    Guo AC, Cummings TJ, Dash RC, Provenzale JM. Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology. 2002; 224(1): 177183.

    • Search Google Scholar
    • Export Citation
  • 26

    Le Bihan D, Breton E, Lallemand D, Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988; 168(2): 497505.

    • Search Google Scholar
    • Export Citation
  • 27

    Le Bihan D, Breton E, Lallemand D, MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986; 161(2): 401407.

    • Search Google Scholar
    • Export Citation
  • 28

    Le Bihan D, Poupon C, Amadon A, Lethimonnier F. Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging. 2006; 24(3): 478488.

Metrics

All Time Past Year Past 30 Days
Abstract Views 127 127 127
Full Text Views 32 32 32
PDF Downloads 21 21 21
EPUB Downloads 0 0 0