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Utility of texture analysis for objective quantitative ex vivo assessment of meningioma consistency: method proposal and validation

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Abstract

Background

Tumor consistency is considered to be a critical factor for the surgical removal of meningiomas and its preoperative assessment is intensively studied. A significant drawback in the research of predictive methods is the lack of a clear shared definition of tumor consistency, with most authors resorting to subjective binary classification labeling the samples as “soft” and “hard.” This classification is highly observer-dependent and its discrete nature fails to capture the fine nuances in tumor consistency. To compensate for these shortcomings, we examined the utility of texture analysis to provide an objective observer-independent continuous measure of meningioma consistency.

Methods

A total of 169 texturometric measurements were conducted using the Brookfield CT3 Texture Analyzer on meningioma samples from five patients immediately after the removal and on the first, second, and seventh postoperative day. The relationship between measured stiffness and time from sample extraction, subjectively assessed consistency grade and histopathological features (amount of collagen and reticulin fibers, presence of psammoma bodies, predominant microscopic morphology) was analyzed.

Results

The stiffness measurements exhibited significantly lower variance within a sample than among samples (p = 0.0225) and significant increase with a higher objectively assessed consistency grade (p = 0.0161, p = 0.0055). A significant negative correlation was found between the measured stiffness and the time from sample extraction (p < 0.01). A significant monotonic relationship was revealed between stiffness values and amount of collagen I and reticulin fibers; there were no statistically significant differences between histological phenotypes in regard to presence of psammoma bodies and predominant microscopic morphology.

Conclusions

We conclude that the values yielded by texture analysis are highly representative of an intrinsic consistency-related quality of the sample despite the influence of intra-sample heterogeneity and that our proposed method can be used to conduct quantitative studies on the role of meningioma consistency.

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Data availability

Source data and statistical code are available as Supplementary 1 and Supplementary 2.

Abbreviations

ADC:

Apparent diffusion coefficient

ANOVA:

Analysis of variance

DWI:

Diffusion-weighted imaging

H&E:

Hematoxylin and eosin

MRE:

Magnetic resonance elastography

MRI:

Magnetic resonance imaging

References

  1. Alyamany M, Alshardan MM, Jamea AA, ElBakry N, Soualmi L, Orz Y (2018) Meningioma consistency: correlation between magnetic resonance imaging characteristics, operative findings, and histopathological features. Asian J Neurosurg 13(2):324–328. https://doi.org/10.4103/1793-5482.228515

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ansari A, Riyaz S (2020) Two-staged approach for giant hypervascular meningioma resection. Asian J Neurosurg 15(2):349–353. https://doi.org/10.4103/ajns.AJNS_364_19

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bartsch K, Brandl A, Weber P, Wilke J, Bensamoun SF, Bauermeister W, Klingler W, Schleip R (2023) Assessing reliability and validity of different stiffness measurement tools on a multi-layered phantom tissue model. Sci Rep 13(1):815. https://doi.org/10.1038/s41598-023-27742-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Brabec J, Szczepankiewicz F, Lennartsson F, Englund E, Pebdani H, Bengzon J, Knutsson L, Westin CF, Sundgren PC, Nilsson M (2022) Histogram analysis of tensor-valued diffusion MRI in meningiomas: relation to consistency, histological grade and type. NeuroImage Clin 33:102912. https://doi.org/10.1016/j.nicl.2021.102912

    Article  PubMed  Google Scholar 

  5. Chen L, Opara U (2013) Approaches to analysis and modeling texture in fresh and processed foods – a review. J Food Eng 119:497. https://doi.org/10.1016/j.jfoodeng.2013.06.028

    Article  Google Scholar 

  6. Chen TC, Zee CS, Miller CA, Weiss MH, Tang G, Chin L, Levy ML, Apuzzo ML (1992) Magnetic resonance imaging and pathological correlates of meningiomas. Neurosurgery 31(6):1015–1022. https://doi.org/10.1227/00006123-199212000-00005

    Article  CAS  PubMed  Google Scholar 

  7. Fischer AA (1987) Tissue compliance meter for objective, quantitative documentation of soft tissue consistency and pathology. Arch Phys Med Rehabil 68(2):122–125

    CAS  PubMed  Google Scholar 

  8. Friedman HH, Whitney JE, Szczesniak AS (1963) The texturometer—a new instrument for objective texture measurement. J Food Sci 28:390–396

    Article  Google Scholar 

  9. Giuffrè R (1984) Successful radical removal of an intracranial meningioma in 1835 by Professor Pecchioli of Siena. J Neurosurg 60(1):47–51. https://doi.org/10.3171/jns.1984.60.1.0047

    Article  PubMed  Google Scholar 

  10. Hong TH, Choi JI, Park MY, Rha SE, Lee YJ, You YK, Choi MH (2017) Pancreatic hardness: correlation of surgeon’s palpation, durometer measurement and preoperative magnetic resonance imaging features. World J Gastroenterol 23(11):2044–2051. https://doi.org/10.3748/wjg.v23.i11.2044

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hughes JD, Fattahi N, Van Gompel J, Arani A, Meyer F, Lanzino G, Link MJ, Ehman R, Huston J (2015) Higher-resolution magnetic resonance elastography in meningiomas to determine intratumoral consistency. Neurosurgery 77(4):653–659. https://doi.org/10.1227/NEU.0000000000000892

    Article  PubMed  Google Scholar 

  12. Itamura K, Chang KE, Lucas J, Donoho DA, Giannotta S, Zada G (2018) Prospective clinical validation of a meningioma consistency grading scheme: association with surgical outcomes and extent of tumor resection. J Neurosurg 1–5. Advance online publication. https://doi.org/10.3171/2018.7.JNS1838

  13. Kashimura H, Inoue T, Ogasawara K, Arai H, Otawara Y, Kanbara Y, Ogawa A (2007) Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg 107(4):784–787. https://doi.org/10.3171/JNS-07/10/0784

    Article  PubMed  Google Scholar 

  14. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23(8):1231–1251. https://doi.org/10.1093/neuonc/noab106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Maiuri F, Iaconetta G, de Divitiis O, Cirillo S, Di Salle F, De Caro ML (1999) Intracranial meningiomas: correlations between MR imaging and histology. Eur J Radiol 31(1):69–75. https://doi.org/10.1016/s0720-048x(98)00083-7

    Article  CAS  PubMed  Google Scholar 

  16. Miyoshi K, Wada T, Uwano I, Sasaki M, Saura H, Fujiwara S, Takahashi F, Tsushima E, Ogasawara K (2020) Predicting the consistency of intracranial meningiomas using apparent diffusion coefficient maps derived from preoperative diffusion-weighted imaging. J Neurosurg 135(3):969–976. https://doi.org/10.3171/2020.6.JNS20740

    Article  PubMed  Google Scholar 

  17. Netuka D, Masopust V, Belšán T, Kramar F, Hána V, Beneš V (2013) Endoscopic endonasal resection of skull base meningiomas. Ceska Slov Neurol Neurochir 76:446–452

    Google Scholar 

  18. Ogasawara C, Philbrick BD, Adamson DC (2021) Meningioma: a review of epidemiology, pathology, diagnosis, treatment, and future directions. Biomedicines 9(3):319. https://doi.org/10.3390/biomedicines9030319

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Qi H, Joyce K, Boyce M (2003) Durometer hardness and the stress-strain behavior of elastomeric materials. Rubber Chem Technol 76:419–435. https://doi.org/10.5254/1.3547752

    Article  CAS  Google Scholar 

  20. Reiter R, Freise C, Jöhrens K, Kamphues C, Seehofer D, Stockmann M, Somasundaram R, Asbach P, Braun J, Samani A, Sack I (2014) Wideband MRE and static mechanical indentation of human liver specimen: sensitivity of viscoelastic constants to the alteration of tissue structure in hepatic fibrosis. J Biomech 47(7):1665–1674. https://doi.org/10.1016/j.jbiomech.2014.02.034

    Article  PubMed  Google Scholar 

  21. Roo C, Tilleman K, Vercruysse C, Declercq H, T’Sjoen G, Weyers S, Sutter P (2019) Texture profile analysis reveals a stiffer ovarian cortex after testosterone therapy: a pilot study. J Assist Reprod Genet 36. https://doi.org/10.1007/s10815-019-01513-x

  22. Seaman SC, Ali MS, Marincovich A, Li L, Walsh JE, Greenlee JDW (2020) Minimally invasive approaches to anterior skull base meningiomas. J Neurol Surg Part B Skull Base 83(3):254–264. https://doi.org/10.1055/s-0040-1716671

    Article  Google Scholar 

  23. Shi Y, Huo Y, Pan C, Qi Y, Yin Z, Ehman RL, Li Z, Yin X, Du B, Qi Z, Yang A, Hong Y (2022) Use of magnetic resonance elastography to gauge meningioma intratumoral consistency and histotype. NeuroImage Clin 36:103173. https://doi.org/10.1016/j.nicl.2022.103173

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sitthinamsuwan B, Khampalikit I, Nunta-aree S, Srirabheebhat P, Witthiwej T, Nitising A (2012) Predictors of meningioma consistency: a study in 243 consecutive cases. Acta Neurochir 154(8):1383–1389. https://doi.org/10.1007/s00701-012-1427-9

    Article  PubMed  Google Scholar 

  25. Smith KA, Leever JD, Hylton PD, Camarata PJ, Chamoun RB (2017) Meningioma consistency prediction utilizing tumor to cerebellar peduncle intensity on T2-weighted magnetic resonance imaging sequences: TCTI ratio. J Neurosurg 126(1):242–248. https://doi.org/10.3171/2016.1.JNS152329

    Article  PubMed  Google Scholar 

  26. Soyama N, Kuratsu J, Ushio Y (1995) Correlation between magnetic resonance images and histology in meningiomas: T2-weighted images indicate collagen contents in tissues. Neurol Med Chir 35(7):438–441. https://doi.org/10.2176/nmc.35.438

    Article  CAS  Google Scholar 

  27. Suzuki Y, Sugimoto T, Shibuya M, Sugita K, Patel SJ (1994) Meningiomas: correlation between MRI characteristics and operative findings including consistency. Acta Neurochir 129(1–2):39–46. https://doi.org/10.1007/BF01400871

    Article  CAS  PubMed  Google Scholar 

  28. Tang HL, Sun HP, Gong Y, Mao Y, Wu JS, Zhang XL, Xie Q, Xie LQ, Zheng MZ, Wang DJ, Zhu HD, Tang WJ, Feng XY, Chen XC, Zhou LF (2012) Preoperative surgical planning for intracranial meningioma resection by virtual reality. Chin Med J 125(11):2057–2061

    PubMed  Google Scholar 

  29. Tang H, Zhang H, Xie Q, Gong Y, Zheng M, Wang D, Zhu H, Chen X, Zhou L (2014) Application of CUSA Excel ultrasonic aspiration system in resection of skull base meningiomas. Chin J Cancer Res = Chung-kuo yen cheng yen chiu 26(6):653–657. https://doi.org/10.3978/j.issn.1000-9604.2014.12.10

    Article  PubMed  Google Scholar 

  30. Tatelbaum AL (2013) A standard method to characterize texture attributes of fresh and processed foods

  31. Thakur JD, Mallari RJ, Corlin A, Yawitz S, Huang W, Eisenberg A, Sivakumar W, Krauss HR, Griffiths C, Barkhoudarian G, Kelly DF (2020) Minimally invasive surgical treatment of intracranial meningiomas in elderly patients (≥ 65 years): outcomes, readmissions, and tumor control. Neurosurg Focus 49(4):E17. https://doi.org/10.3171/2020.7.FOCUS20515

    Article  PubMed  Google Scholar 

  32. vanRossum G (1995) Python reference manual. Department of Computer Science [CS], (R 9525)

  33. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, …, SciPy 1.0 Contributors (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17(3):261–272. https://doi.org/10.1038/s41592-019-0686-2

  34. Yamada H, Tanikawa M, Sakata T, Aihara N, Mase M (2022) Usefulness of T2 relaxation time for quantitative prediction of meningioma consistency. World Neurosurg 157:e484–e491. https://doi.org/10.1016/j.wneu.2021.10.135

    Article  PubMed  Google Scholar 

  35. Yamaguchi N, Kawase T, Sagoh M, Ohira T, Shiga H, Toya S (1997) Prediction of consistency of meningiomas with preoperative magnetic resonance imaging. Surg Neurol 48(6):579–583. https://doi.org/10.1016/s0090-3019(96)00439-9

    Article  CAS  PubMed  Google Scholar 

  36. Yogi A, Koga T, Azama K, Higa D, Ogawa K, Watanabe T, Ishiuchi S, Murayama S (2014) Usefulness of the apparent diffusion coefficient (ADC) for predicting the consistency of intracranial meningiomas. Clin Imaging 38(6):802–807. https://doi.org/10.1016/j.clinimag.2014.06.016

    Article  PubMed  Google Scholar 

  37. Yoneoka Y, Fujii Y, Takahashi H, Nakada T (2002) Pre-operative histopathological evaluation of meningiomas by 3 0T T2R MRI. Acta Neurochir 144(10):953–957. https://doi.org/10.1007/s00701-002-1005-7

    Article  CAS  PubMed  Google Scholar 

  38. Zada G, Yashar P, Robison A, Winer J, Khalessi A, Mack WJ, Giannotta SL (2013) A proposed grading system for standardizing tumor consistency of intracranial meningiomas. Neurosurg Focus 35(6):E1. https://doi.org/10.3171/2013.8.FOCUS13274

    Article  PubMed  Google Scholar 

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Funding

This work was supported by the Czech Health Research Council (grant nr. NV19-04–00272) and by the project BBMRI-CZ LM2023033, Charles University Cooperation Program, research areas DIAG and METD, and Project of Czech Ministry of Defense MO 1012. The work was supported by the European Regional Development Fund-Project BBMRI-CZ BioBank network—a versatile platform for the research of the etiopathogenesis of diseases, No: EF16_013/0001674.

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Author notes

  1. František Štěpánek and Vladimír Beneš contributed equally as senior authors.

Authors and Affiliations

  1. Department of Neurosurgery and Neurooncology, Military University Hospital, Prague, Czech Republic

    Martin Černý, Michaela May, David Netuka & Vladimír Beneš

  2. 1st Faculty of Medicine, Charles University, Prague, Czech Republic

    Martin Černý & Luděk Šíma

  3. Department of Chemical Engineering, University of Chemistry and Technology, Prague, Czech Republic

    Veronika Lesáková & František Štěpánek

  4. Department of Pathology, Military University Hospital, Prague, Czech Republic

    Jiří Soukup

  5. Department of Radiodiagnostics, Military University Hospital, Prague, Czech Republic

    Vojtěch Sedlák

Authors
  1. Martin Černý
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Contributions

MC conceptualized the study, coordinated patient inclusion, performed statistical analysis, and wrote most sections of the manuscript. VL designed the texturometric examination protocol, evaluated the measurements in a blinded fashion, and wrote the sections on texture analysis. JS performed histopathological examinations and wrote corresponding sections. LS conducted texturometric sample examinations. VS wrote the sections on MRI imaging. MM contributed to sections on clinical significance. DN critically reviewed the manuscript. FS and VB supervised the work on this article as senior researchers.

Corresponding author

Correspondence to Martin Černý.

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Ethical approval

This study was approved by the institutional ethical committee (ref. nr. 108/18–60/2023). Data were anonymized at the time of patient inclusion and treated according to the ethical standards of the Declaration of Helsinki. Written informed consent was obtained from all participants.

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The authors declare no competing interests.

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Černý, M., Lesáková, V., Soukup, J. et al. Utility of texture analysis for objective quantitative ex vivo assessment of meningioma consistency: method proposal and validation. Acta Neurochir 165, 4203–4211 (2023). https://doi.org/10.1007/s00701-023-05867-1

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