Literature DB >> 35953734

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

Tiexin Cao1, Rifeng Jiang1, Lingmin Zheng1, Rufei Zhang1, Xiaodan Chen2, Zongmeng Wang1, Peirong Jiang1, Yilin Chen1, Tianjin Zhong1, Hu Chen3, PuYeh Wu4, Yunjing Xue5,6, Lin Lin7,8.   

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.
© 2022. The Author(s), under exclusive licence to European Society of Radiology.

Entities:  

Keywords:  Diffusion-weighted imaging; Ki-67 antigen; Meningioma; T1 mapping

Year:  2022        PMID: 35953734     DOI: 10.1007/s00330-022-09026-5

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   7.034


  55 in total

1.  Benign meningiomas (WHO Grade I) with atypical histological features: correlation of histopathological features with clinical outcomes.

Authors:  Ariel E Marciscano; Anat O Stemmer-Rachamimov; Andrzej Niemierko; Mykol Larvie; William T Curry; Fred G Barker; Robert L Martuza; Declan McGuone; Kevin S Oh; Jay S Loeffler; Helen A Shih
Journal:  J Neurosurg       Date:  2015-08-14       Impact factor: 5.115

Review 2.  Histological classification and molecular genetics of meningiomas.

Authors:  Markus J Riemenschneider; Arie Perry; Guido Reifenberger
Journal:  Lancet Neurol       Date:  2006-12       Impact factor: 44.182

Review 3.  Cardiac T1 mapping: Techniques and applications.

Authors:  Emily Aherne; Kelvin Chow; James Carr
Journal:  J Magn Reson Imaging       Date:  2019-07-23       Impact factor: 4.813

4.  Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma?

Authors:  Hao Yu; Xinrui Wen; Pingping Wu; Yueqin Chen; Tianyu Zou; Xianlong Wang; Shanshan Jiang; Jinyuan Zhou; Zhibo Wen
Journal:  Eur Radiol       Date:  2019-03-18       Impact factor: 5.315

Review 5.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

Authors:  David N Louis; Arie Perry; Guido Reifenberger; Andreas von Deimling; Dominique Figarella-Branger; Webster K Cavenee; Hiroko Ohgaki; Otmar D Wiestler; Paul Kleihues; David W Ellison
Journal:  Acta Neuropathol       Date:  2016-05-09       Impact factor: 17.088

6.  Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging.

Authors:  Hiroshi Kashimura; Takashi Inoue; Kuniaki Ogasawara; Hiroshi Arai; Yasunari Otawara; Yoshiyuki Kanbara; Akira Ogawa
Journal:  J Neurosurg       Date:  2007-10       Impact factor: 5.115

7.  Comparative Analysis of Diffusional Kurtosis Imaging, Diffusion Tensor Imaging, and Diffusion-Weighted Imaging in Grading and Assessing Cellular Proliferation of Meningiomas.

Authors:  L Lin; R Bhawana; Y Xue; Q Duan; R Jiang; H Chen; X Chen; B Sun; H Lin
Journal:  AJNR Am J Neuroradiol       Date:  2018-05-10       Impact factor: 3.825

8.  Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection.

Authors:  Zaneta Swiderska-Chadaj; Tomasz Markiewicz; Bartlomiej Grala; Malgorzata Lorent
Journal:  Diagn Pathol       Date:  2016-10-07       Impact factor: 2.644

Review 9.  Cardiac T1 Mapping and Extracellular Volume (ECV) in clinical practice: a comprehensive review.

Authors:  Philip Haaf; Pankaj Garg; Daniel R Messroghli; David A Broadbent; John P Greenwood; Sven Plein
Journal:  J Cardiovasc Magn Reson       Date:  2016-11-30       Impact factor: 5.364

Review 10.  Assessment of myocardial fibrosis with T1 mapping MRI.

Authors:  R J Everett; C G Stirrat; S I R Semple; D E Newby; M R Dweck; S Mirsadraee
Journal:  Clin Radiol       Date:  2016-03-19       Impact factor: 2.350

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