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T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma

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Abstract

Objective

To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.

Methods

This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman’s rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.

Results

High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001–0.009), lower minimum, and C10 of ADC (p = 0.013–0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).

Conclusion

T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.

Key Points

• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma.

• The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUCs:

Area under the receiver operating characteristic curves

C10:

10th percentile

C90:

90th percentile

CI:

Confidence interval

CNS:

Central nervous system

DWI:

Diffusion-weighted imaging

ECM:

Extracellular matrix

FSPGR:

Fast-spoiled gradient recalled

GRE:

Gradient recalled echo

HGMs:

High-grade meningiomas

ICC:

Intraclass correlation coefficient

IR-FSE:

Inversion recovery fast spin echo

LGMs:

Low-grade meningiomas

LI:

Labelling index

MS:

Multiple sclerosis

ROC:

Receiver operating characteristic

ROI:

Region of interest

WHO:

World Health Organization

References

  1. Shibuya M (2015) Pathology and molecular genetics of meningioma: recent advances. Neurol Med Chir (Tokyo) 55:14–27

    Article  Google Scholar 

  2. Louis D, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 131:803–820

    Article  Google Scholar 

  3. Riemenschneider MJ, Perry A, Reifenberger G (2006) Histological classification and molecular genetics of meningiomas. Lancet Neurol 5:1045–1054

    Article  CAS  Google Scholar 

  4. Ostrom QT, Patil N, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS (2020) Corrigendum to: CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2013-2017. Neuro Oncol. https://doi.org/10.1093/neuonc/noaa269

  5. Banan R, Abbetmeier-Basse M, Hong B et al (2021) The prognostic significance of clinicopathological features in meningiomas: microscopic brain invasion can predict patient outcome in otherwise benign meningiomas. Neuropathol Appl Neurobiol. https://doi.org/10.1111/nan.12700

  6. Goldbrunner R, Stavrinou P, Jenkinson M et al (2021) EANO guideline on the diagnosis and management of meningiomas. Neuro Oncol 23:1821–1834

  7. Kashimura H, Inoue T, Ogasawara K et al (2007) Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg 107:784–787

    Article  Google Scholar 

  8. Swiderska-Chadaj Z, Markiewicz T, Grala B, Lorent M (2016) Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection. Diagn Pathol 11:93

    Article  Google Scholar 

  9. Marciscano A, Stemmer-Rachamimov A, Niemierko A et al (2016) Benign meningiomas (WHO Grade I) with atypical histological features: correlation of histopathological features with clinical outcomes. J Neurosurg 124:106–114

    Article  Google Scholar 

  10. Lin L, Xue Y, Duan Q et al (2019) Grading meningiomas using mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging. Clin Radiol 74:651.e615–651.e623

    Article  Google Scholar 

  11. Lin L, Bhawana R, Xue Y et al (2018) Comparative analysis of diffusional kurtosis imaging, diffusion tensor imaging, and diffusion-weighted imaging in grading and assessing cellular proliferation of meningiomas. AJNR Am J Neuroradiol 39:1032–1038

    Article  CAS  Google Scholar 

  12. Yu H, Wen X, Wu P et al (2019) Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma? Eur Radiol 29:5298–5306

    Article  Google Scholar 

  13. Parsai C, O'Hanlon R, Prasad S, Mohiaddin R (2012) Diagnostic and prognostic value of cardiovascular magnetic resonance in non-ischaemic cardiomyopathies. J Cardiovasc Magnet Resonance : Official J Soc Cardiovasc Magnet Resonance 14:54

    Article  Google Scholar 

  14. Everett RJ, Stirrat CG, Semple SI, Newby DE, Dweck MR, Mirsadraee S (2016) Assessment of myocardial fibrosis with T1 mapping MRI. Clin Radiol 71:768–778

    Article  CAS  Google Scholar 

  15. Haaf P, Garg P, Messroghli DR, Broadbent DA, Greenwood JP, Plein S (2016) Cardiac T1 mapping and extracellular volume (ECV) in clinical practice: a comprehensive review. J Cardiovasc Magn Reson 18:89

    Article  Google Scholar 

  16. Aherne E, Chow K, Carr J (2020) Cardiac T1 mapping: techniques and applications. J Magn Reson Imaging 51:1336–1356

    Article  Google Scholar 

  17. Fernandes JL, Rochitte CE (2015) T1 mapping: technique and applications. Magn Reson Imaging Clin N Am 23:25–34

    Article  Google Scholar 

  18. Child N, Suna G, Dabir D et al (2018) Comparison of MOLLI, shMOLLLI, and SASHA in discrimination between health and disease and relationship with histologically derived collagen volume fraction. Eur Heart J Cardiovasc Imaging 19:768–776

    Article  Google Scholar 

  19. Ramsahye H, He H, Feng X, Li S, Xiong J (2013) Central neurocytoma: radiological and clinico-pathological findings in 18 patients and one additional MRS case. J Neuroradiol 40:101–111

    Article  Google Scholar 

  20. Svolos P, Tsolaki E, Kapsalaki E et al (2013) Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques. Magn Reson Imaging 31:1567–1577

    Article  Google Scholar 

  21. El-Ali A, Agarwal V, Thomas A, Hamilton R, Filippi C (2019) Clinical metric for differentiating intracranial hemangiopericytomas from meningiomas using diffusion weighted MRI. Clin Imaging 54:1–5

    Article  Google Scholar 

  22. Atalay B, Ediz S, Ozbay N (2020) Apparent diffusion coefficient in predicting the preoperative grade of meningiomas. J Coll Physicians Surg Pak 30:1126–1132

    Article  Google Scholar 

  23. Meyer H, Wienke A, Surov A (2020) ADC values of benign and high grade meningiomas and associations with tumor cellularity and proliferation - a systematic review and meta-analysis. J Neurol Sci 415:116975

    Article  Google Scholar 

  24. Xiaoai K, Qing Z, Lei H, Junlin Z (2020) Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging. Neuroradiology 62:601–607

    Article  Google Scholar 

  25. Bozdağ M, Er A, Ekmekçi S (2021) Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas. Acta Radiol 62:401–413

  26. Yiping L, Kawai S, Jianbo W, Li L, Daoying G, Bo Y (2017) Evaluation parameters between intra-voxel incoherent motion and diffusion-weighted imaging in grading and differentiating histological subtypes of meningioma: a prospective pilot study. J Neurol Sci 372:60–69

    Article  Google Scholar 

  27. Zhang S, Chiang G, Knapp J et al (2020) Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping. J Neuroradiol = J Neuroradiol 47:272–277

    Article  Google Scholar 

  28. Ahn SJ, Choi SH, Kim YJ et al (2012) Histogram analysis of apparent diffusion coefficient map of standard and high B-value diffusion MR imaging in head and neck squamous cell carcinoma: a correlation study with histological grade. Acad Radiol 19:1233–1240

    Article  Google Scholar 

  29. Wang S, Kim S, Zhang Y et al (2012) Determination of grade and subtype of meningiomas by using histogram analysis of diffusion-tensor imaging metrics. Radiology 262:584–592

    Article  Google Scholar 

  30. Just N (2014) Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 111:2205–2213

    Article  CAS  Google Scholar 

  31. Liu HS, Chiang SW, Chung HW et al (2018) Histogram analysis of T2*-based pharmacokinetic imaging in cerebral glioma grading. Comput Methods Prog Biomed 155:19–27

    Article  Google Scholar 

  32. Murayama K, Nishiyama Y, Hirose Y et al (2018) Differentiating between central nervous system lymphoma and high-grade glioma using dynamic susceptibility contrast and dynamic contrast-enhanced MR imaging with histogram analysis. Magn Reson Med Sci 17:42–49

    Article  Google Scholar 

  33. Liu P, Chen L, Wang QX et al (2020) Histogram analysis of T2 mapping for detecting early involvement of extraocular muscles in patients with thyroid-associated ophthalmopathy. Sci Rep 10:19445

    Article  CAS  Google Scholar 

  34. Li D, Cui Y, Hou L et al (2021) Diffusion kurtosis imaging-derived histogram metrics for prediction of resistance to neoadjuvant chemoradiotherapy in rectal adenocarcinoma: preliminary findings. Eur J Radiol 144:109963

    Article  Google Scholar 

  35. Xie T, Zhao Q, Fu C, Grimm R, Gu Y, Peng W (2021) Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm). Eur Radiol. https://doi.org/10.1007/s00330-021-08244-7

  36. Barral J, Gudmundson E, Stikov N, Etezadi-Amoli M, Stoica P, Nishimura D (2010) A robust methodology for in vivo T1 mapping. Magn Reson Med 64:1057–1067

    Article  Google Scholar 

  37. Just M, Thelen M (1988) Tissue characterization with T1, T2, and proton density values: results in 160 patients with brain tumors. Radiology 169:779–785

    Article  CAS  Google Scholar 

  38. Komiyama M, Yagura H, Baba M et al (1987) MR imaging: possibility of tissue characterization of brain tumors using T1 and T2 values. AJNR Am J Neuroradiol 8:65–70

    CAS  Google Scholar 

  39. Larsson C, Kleppestø M, Grothe I, Vardal J, Bjørnerud A (2015) T1 in high-grade glioma and the influence of different measurement strategies on parameter estimations in DCE-MRI. J Magn Reson Imaging 42:97–104

    Article  Google Scholar 

  40. Andersen C, Astrup J, Gyldensted C (1994) Quantitative MR analysis of glucocorticoid effects on peritumoural oedema associated with intracranial meningiomas and metastases. J Comput Assist Tomogr 18:509–518

    Article  CAS  Google Scholar 

  41. Wang B, Zhang Y, Zhao B et al (2018) Postcontrast T1 mapping for differential diagnosis of recurrence and radionecrosis after gamma knife radiosurgery for brain metastasis. AJNR Am J Neuroradiol 39:1025–1031

    Article  CAS  Google Scholar 

  42. Vrenken H, Geurts J, Knol D et al (2006) Whole-brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology 240:811–820

    Article  Google Scholar 

  43. van Walderveen M, van Schijndel R, Pouwels P, Polman C, Barkhof F (2003) Multislice T1 relaxation time measurements in the brain using IR-EPI: reproducibility, normal values, and histogram analysis in patients with multiple sclerosis. J Magn Reson Imaging 18:656–664

    Article  Google Scholar 

  44. Griffin C, Dehmeshki J, Chard D et al (2002) T1 histograms of normal-appearing brain tissue are abnormal in early relapsing-remitting multiple sclerosis. Mult Scler 8:211–216

    Article  CAS  Google Scholar 

  45. Tan Y, Xu J, Chen R et al (2018) Use of T relaxation time in rotating frame (T ρ) and apparent diffusion coefficient to estimate cerebral stroke evolution. J Magnet Resonance Imaging : JMRI 48:1247–1254

    Article  Google Scholar 

  46. Ayerbe J, Lobato RD, de la Cruz J et al (1999) Risk factors predicting recurrence in patients operated on for intracranialmeningioma. A multivariate analysis. Acta Neurochir (Wien) 141:921–932

  47. Takahashi JA, Ueba T, Hashimoto N, Nakashima Y, Katsuki N (2004) The combination of mitotic and Ki-67 indices as a useful method for predicting short-term recurrence of meningiomas. Surg Neurol 61(149-155):discussion 155-146

    Google Scholar 

  48. Nagar VA, Ye JR, Ng WH et al (2008) Diffusion-weighted MR imaging: diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation. AJNR Am J Neuroradiol 29:1147–1152

    Article  CAS  Google Scholar 

  49. Okuducu AF, Zils U, Michaelis SA, Michaelides S, von Deimling A (2006) Ets-1 is up-regulated together with its target gene products matrix metalloproteinase-2 and matrix metalloproteinase-9 in atypical and anaplastic meningiomas. Histopathology 48:836–845

    Article  CAS  Google Scholar 

  50. Piechnik SK, Jerosch-Herold M (2018) Myocardial T1 mapping and extracellular volume quantification: an overview of technical and biological confounders. Int J Card Imaging 34:3–14

    Article  Google Scholar 

  51. Adams LC, Ralla B, Jurmeister P et al (2019) Native T1 mapping as an in vivo biomarker for the identification of higher-grade renal cell carcinoma: correlation with histopathological findings. Invest Radiol 54:118–128

    Article  Google Scholar 

  52. Ma R, Geng Y, Gan L et al (2021) Quantitative T1 mapping MRI for the assessment of extraocular muscle fibrosis in thyroid-associated ophthalmopathy. Endocrine. https://doi.org/10.1007/s12020-021-02873-0

  53. Yamasaki F, Kurisu K, Satoh K et al (2005) Apparent diffusion coefficient of human brain tumors at MR imaging. Radiology 235:985–991

    Article  Google Scholar 

  54. Surov A, Gottschling S, Mawrin C et al (2015) Diffusion-weighted imaging in meningioma: prediction of tumor grade and association with histopathological parameters. Transl Oncol 8:517–523

    Article  Google Scholar 

  55. Sanverdi SE, Ozgen B, Oguz KK et al (2012) Is diffusion-weighted imaging useful in grading and differentiating histopathological subtypes of meningiomas? Eur J Radiol 81:2389–2395

    Article  Google Scholar 

  56. Park YW, Kim S, Ahn SS et al (2020) Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade. Eur Radiol 30:4615–4622

    Article  Google Scholar 

  57. Hsu C, Pai C, Kao H, Hsueh C, Hsu W, Lo C (2010) Do aggressive imaging features correlate with advanced histopathological grade in meningiomas? J Clin Neurosci 17:584–587

    Article  Google Scholar 

  58. Choi Y, Kim S, Youn I, Kang B, Park W, Lee A (2017) Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: comparison with ER-positive subtype. PLoS One 12:e0177903

    Article  Google Scholar 

  59. Surov A, Ginat D, Lim T et al (2018) Histogram analysis parameters apparent diffusion coefficient for distinguishing high and low-grade meningiomas: a multicenter study. Transl Oncol 11:1074–1079

    Article  Google Scholar 

  60. Gihr G, Horvath-Rizea D, Garnov N et al (2018) Diffusion profiling via a histogram approach distinguishes low-grade from high-grade meningiomas, can reflect the respective proliferative potential and progesterone receptor status. Mol Imaging Biol 20:632–640

    Article  CAS  Google Scholar 

  61. Bohara M, Nakajo M, Kamimura K et al (2020) Histological grade of meningioma: prediction by intravoxel incoherent motion histogram parameters. Acad Radiol 27:342–353

    Article  Google Scholar 

  62. Lu S, Kim S, Kim N, Kim H, Choi C, Lim Y (2015) Histogram analysis of apparent diffusion coefficient maps for differentiating primary CNS lymphomas from tumefactive demyelinating lesions. AJR Am J Roentgenol 204:827–834

    Article  Google Scholar 

  63. He W, Xiao X, Li X et al (2019) Whole-tumor histogram analysis of apparent diffusion coefficient in differentiating intracranial solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma. Eur J Radiol 112:186–191

    Article  Google Scholar 

  64. Lu Y, Liu L, Luan S, Xiong J, Geng D, Yin B (2019) The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest. Eur Radiol 29:1318–1328

    Article  Google Scholar 

  65. Li X, Miao Y, Han L et al (2019) Meningioma grading using conventional MRI histogram analysis based on 3D tumor measurement. Eur J Radiol 110:45–53

    Article  Google Scholar 

  66. Pond J, Morgan T, Hatanpaa K, Yetkin Z, Mickey B, Mendelsohn D (2015) Chordoid meningioma: differentiating a rare World Health Organization grade II tumor from other meningioma histologic subtypes using MRI. AJNR Am J Neuroradiol 36:1253–1258

    Article  CAS  Google Scholar 

  67. King AD, Chow KK, Yu KH et al (2013) Head and neck squamous cell carcinoma: diagnostic performance of diffusion-weighted MR imaging for the prediction of treatment response. Radiology 266:531–538

    Article  Google Scholar 

  68. Cho GY, Moy L, Kim SG et al (2016) Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. Eur Radiol 26:2547–2558

    Article  Google Scholar 

  69. Westfall PH (2014) Kurtosis as Peakedness, 1905 - 2014. R.I.P. Am Stat 68:191-195

  70. Del Gobbo A, Pellegrinelli A, Gaudioso G et al (2016) Analysis of NSCLC tumour heterogeneity, proliferative and 18F-FDG PET indices reveals Ki67 prognostic role in adenocarcinomas. Histopathology 68:746–751

    Article  Google Scholar 

  71. Becker A, Wagner M, Wurnig M, Boss A (2017) Diffusion-weighted imaging of the abdomen: impact of b-values on texture analysis features. NMR Biomed https://doi.org/10.1002/nbm.3669

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Acknowledgements

We acknowledge PuYeh Wu from GE Healthcare for the technical support.

Funding

This study has received funding from the Joint Funds for the Innovation of Science and Technology, Fujian province (Grant number: 2018Y9044) and Fujian Provincial Health Technology Project (grant number: 2020GGA039).

Author information

Author notes

  1. Tiexin Cao, Rifeng Jiang, and Lingmin Zheng contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China

    Tiexin Cao, Rifeng Jiang, Lingmin Zheng, Rufei Zhang, Zongmeng Wang, Peirong Jiang, Yilin Chen, Tianjin Zhong, Yunjing Xue & Lin Lin

  2. Department of Radiology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, 350014, China

    Xiaodan Chen

  3. Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China

    Hu Chen

  4. GE Healthcare, Beijing, 100176, China

    PuYeh Wu

  5. School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350004, China

    Yunjing Xue & Lin Lin

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  1. Tiexin Cao
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Correspondence to Yunjing Xue or Lin Lin.

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The scientific guarantor of this publication is Dr. Lin Lin, MD, PhD, Fujian Medical University Union Hospital.

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One of the authors of this manuscript (PuYeh Wu) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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Cao, T., Jiang, R., Zheng, L. et al. T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. Eur Radiol 33, 258–269 (2023). https://doi.org/10.1007/s00330-022-09026-5

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