Utility of texture analysis for objective quantitative ex vivo assessment of meningioma consistency: method proposal and validation.
Meningioma
Preoperative planning
Texture analysis
Tumor consistency
Journal
Acta neurochirurgica
ISSN: 0942-0940
Titre abrégé: Acta Neurochir (Wien)
Pays: Austria
ID NLM: 0151000
Informations de publication
Date de publication:
04 Dec 2023
04 Dec 2023
Historique:
received:
31
08
2023
accepted:
20
10
2023
medline:
4
12
2023
pubmed:
4
12
2023
entrez:
3
12
2023
Statut:
aheadofprint
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 38044374
doi: 10.1007/s00701-023-05867-1
pii: 10.1007/s00701-023-05867-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Ministerstvo Zdravotnictví Ceské Republiky
ID : NV19-04-00272
Organisme : Ministerstvo Zdravotnictví Ceské Republiky
ID : BBMRI-CZ LM2023033
Organisme : Univerzita Karlova v Praze
ID : Cooperatio Program
Organisme : European Regional Development Fund-Project BBMRI-CZ Biobank network
ID : EF16_013/0001674
Organisme : Ministerstvo Obrany České Republiky
ID : MO1012
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
Références
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
doi: 10.4103/1793-5482.228515
pubmed: 29682029
pmcid: 5898100
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
doi: 10.4103/ajns.AJNS_364_19
pubmed: 32656131
pmcid: 7335117
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
doi: 10.1038/s41598-023-27742-w
pubmed: 36646734
pmcid: 9842673
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
doi: 10.1016/j.nicl.2021.102912
pubmed: 34922122
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
doi: 10.1016/j.jfoodeng.2013.06.028
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
doi: 10.1227/00006123-199212000-00005
pubmed: 1281915
Fischer AA (1987) Tissue compliance meter for objective, quantitative documentation of soft tissue consistency and pathology. Arch Phys Med Rehabil 68(2):122–125
pubmed: 3813858
Friedman HH, Whitney JE, Szczesniak AS (1963) The texturometer—a new instrument for objective texture measurement. J Food Sci 28:390–396
doi: 10.1111/j.1365-2621.1963.tb00216.x
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
doi: 10.3171/jns.1984.60.1.0047
pubmed: 6358429
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
doi: 10.3748/wjg.v23.i11.2044
pubmed: 28373771
pmcid: 5360646
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
doi: 10.1227/NEU.0000000000000892
pubmed: 26197204
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
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
doi: 10.3171/JNS-07/10/0784
pubmed: 17937223
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
doi: 10.1093/neuonc/noab106
pubmed: 34185076
pmcid: 8328013
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
doi: 10.1016/s0720-048x(98)00083-7
pubmed: 10477102
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
doi: 10.3171/2020.6.JNS20740
pubmed: 33186907
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
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
doi: 10.3390/biomedicines9030319
pubmed: 33801089
pmcid: 8004084
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
doi: 10.5254/1.3547752
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
doi: 10.1016/j.jbiomech.2014.02.034
pubmed: 24657103
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
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
doi: 10.1055/s-0040-1716671
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
doi: 10.1016/j.nicl.2022.103173
pubmed: 36081257
pmcid: 9463601
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
doi: 10.1007/s00701-012-1427-9
pubmed: 22743797
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
doi: 10.3171/2016.1.JNS152329
pubmed: 27058200
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
doi: 10.2176/nmc.35.438
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
doi: 10.1007/BF01400871
pubmed: 7998494
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: 22884077
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
doi: 10.3978/j.issn.1000-9604.2014.12.10
pubmed: 25561762
Tatelbaum AL (2013) A standard method to characterize texture attributes of fresh and processed foods
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
doi: 10.3171/2020.7.FOCUS20515
pubmed: 33002879
vanRossum G (1995) Python reference manual. Department of Computer Science [CS], (R 9525)
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
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
doi: 10.1016/j.wneu.2021.10.135
pubmed: 34695610
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
doi: 10.1016/s0090-3019(96)00439-9
pubmed: 9400639
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
doi: 10.1016/j.clinimag.2014.06.016
pubmed: 25082174
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
doi: 10.1007/s00701-002-1005-7
pubmed: 12382122
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
doi: 10.3171/2013.8.FOCUS13274
pubmed: 24289117