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Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps
BACKGROUND: Preoperative differentiation between angiomatous meningioma (AM) and atypical meningioma (ATM) is related to treatment planning. In this study, we explored the utility of apparent diffusion coefficient (ADC) histogram analysis in differentiating AM and ATM, and further assess the correla...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347304/ https://www.ncbi.nlm.nih.gov/pubmed/37456320 http://dx.doi.org/10.21037/qims-22-1224 |
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author | Liu, Xianwang Han, Tao Wang, Yuzhu Liu, Hong Huang, Xiaoyu Zhou, Junlin |
author_facet | Liu, Xianwang Han, Tao Wang, Yuzhu Liu, Hong Huang, Xiaoyu Zhou, Junlin |
author_sort | Liu, Xianwang |
collection | PubMed |
description | BACKGROUND: Preoperative differentiation between angiomatous meningioma (AM) and atypical meningioma (ATM) is related to treatment planning. In this study, we explored the utility of apparent diffusion coefficient (ADC) histogram analysis in differentiating AM and ATM, and further assess the correlations between these parameters and the Ki-67 proliferation index. METHODS: Thirty AM and 35 ATM patients were enrolled and their clinical and conventional magnetic resonance imaging (MRI) features were analyzed in this study. Nine ADC histogram parameters [mean, variance, skewness, and kurtosis, as well as the 1st (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentile of ADC] were selected and compared by independent t-test or Mann-Whitney U test. Diagnostic performance analysis was performed by receiver operating characteristic (ROC) curves. The relationship between ADC histogram parameters and the Ki-67 proliferation index was assessed by Spearman’s correlation coefficient. RESULTS: AM group showed a significantly higher mean [median (interquartile range): 124.07 (22.66) vs. 112.12 (16.04), P<0.001], ADC1 [107.50 (17.00) vs. 82.00 (20.33), P<0.001], ADC10 (mean ± standard deviation: 115.80±12.09 vs. 96.86±9.86, P<0.001), and ADC50 [124.00 (21.13) vs. 109.00 (15.17), P<0.001], compared to the ATM group. Significant correlations were identified between the mean (r=−0.428, P<0.001), ADC1 (r=−0.549, P<0.001), ADC10 (r=−0.529, P<0.001), ADC50 (r=−0.483, P<0.001), and the Ki-67 proliferation index. ROC analysis showed that the best diagnostic performance was achieved by ADC1 (AUC =0.900). Whereas, no differences were found between variance, skewness, kurtosis, ADC90, and ADC99 (P=0.067–0.787). CONCLUSIONS: AM and ATM exhibit overlapping conventional MRI features. ADC histogram analysis, especially ADC1, maybe a reliable quantitative imaging biomarker for differentiation between AM and ATM. |
format | Online Article Text |
id | pubmed-10347304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-103473042023-07-15 Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps Liu, Xianwang Han, Tao Wang, Yuzhu Liu, Hong Huang, Xiaoyu Zhou, Junlin Quant Imaging Med Surg Original Article BACKGROUND: Preoperative differentiation between angiomatous meningioma (AM) and atypical meningioma (ATM) is related to treatment planning. In this study, we explored the utility of apparent diffusion coefficient (ADC) histogram analysis in differentiating AM and ATM, and further assess the correlations between these parameters and the Ki-67 proliferation index. METHODS: Thirty AM and 35 ATM patients were enrolled and their clinical and conventional magnetic resonance imaging (MRI) features were analyzed in this study. Nine ADC histogram parameters [mean, variance, skewness, and kurtosis, as well as the 1st (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentile of ADC] were selected and compared by independent t-test or Mann-Whitney U test. Diagnostic performance analysis was performed by receiver operating characteristic (ROC) curves. The relationship between ADC histogram parameters and the Ki-67 proliferation index was assessed by Spearman’s correlation coefficient. RESULTS: AM group showed a significantly higher mean [median (interquartile range): 124.07 (22.66) vs. 112.12 (16.04), P<0.001], ADC1 [107.50 (17.00) vs. 82.00 (20.33), P<0.001], ADC10 (mean ± standard deviation: 115.80±12.09 vs. 96.86±9.86, P<0.001), and ADC50 [124.00 (21.13) vs. 109.00 (15.17), P<0.001], compared to the ATM group. Significant correlations were identified between the mean (r=−0.428, P<0.001), ADC1 (r=−0.549, P<0.001), ADC10 (r=−0.529, P<0.001), ADC50 (r=−0.483, P<0.001), and the Ki-67 proliferation index. ROC analysis showed that the best diagnostic performance was achieved by ADC1 (AUC =0.900). Whereas, no differences were found between variance, skewness, kurtosis, ADC90, and ADC99 (P=0.067–0.787). CONCLUSIONS: AM and ATM exhibit overlapping conventional MRI features. ADC histogram analysis, especially ADC1, maybe a reliable quantitative imaging biomarker for differentiation between AM and ATM. AME Publishing Company 2023-05-08 2023-07-01 /pmc/articles/PMC10347304/ /pubmed/37456320 http://dx.doi.org/10.21037/qims-22-1224 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Liu, Xianwang Han, Tao Wang, Yuzhu Liu, Hong Huang, Xiaoyu Zhou, Junlin Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps |
title | Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps |
title_full | Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps |
title_fullStr | Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps |
title_full_unstemmed | Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps |
title_short | Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps |
title_sort | differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347304/ https://www.ncbi.nlm.nih.gov/pubmed/37456320 http://dx.doi.org/10.21037/qims-22-1224 |
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