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Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis

BACKGROUND AND OBJECTIVES: The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust e...

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Autores principales: Liang, Jianye, Li, Jing, Li, Zhipeng, Meng, Tiebao, Chen, Jieting, Ma, Weimei, Chen, Shen, Li, Xie, Wu, Yaopan, He, Ni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446186/
https://www.ncbi.nlm.nih.gov/pubmed/32831052
http://dx.doi.org/10.1186/s12885-020-07308-z
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author Liang, Jianye
Li, Jing
Li, Zhipeng
Meng, Tiebao
Chen, Jieting
Ma, Weimei
Chen, Shen
Li, Xie
Wu, Yaopan
He, Ni
author_facet Liang, Jianye
Li, Jing
Li, Zhipeng
Meng, Tiebao
Chen, Jieting
Ma, Weimei
Chen, Shen
Li, Xie
Wu, Yaopan
He, Ni
author_sort Liang, Jianye
collection PubMed
description BACKGROUND AND OBJECTIVES: The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust evidence in this issue using a meta-analysis method. MATERIALS AND METHODS: The researches regarding the differential diagnosis of lung lesions using IVIM-DWI were systemically searched in Pubmed, Embase, Web of science and Wangfang database without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan’s nomogram was used to predict the post-test probabilities. RESULTS: Eleven studies with 481 malignant and 258 benign lung lesions were included. Most include studies showed a low to unclear risk of bias and low concerns regarding applicability. Lung cancer demonstrated a significant lower ADC (SMD = -1.17, P < 0.001), D (SMD = -1.02, P < 0.001) and f values (SMD = -0.43, P = 0.005) than benign lesions, except D* value (SMD = 0.01, P = 0.96). D value demonstrated the best diagnostic performance (sensitivity = 89%, specificity = 71%, AUC = 0.90) and highest post-test probability (57, 57, 43 and 43% for D, ADC, f and D* values) in the differential diagnosis of lung tumors, followed by ADC (sensitivity = 85%, specificity = 72%, AUC = 0.86), f (sensitivity = 71%, specificity = 61%, AUC = 0.71) and D* values (sensitivity = 70%, specificity = 60%, AUC = 0.66). CONCLUSION: IVIM-DWI parameters show potentially strong diagnostic capabilities in the differential diagnosis of lung tumors based on the tumor cellularity and perfusion characteristics, and D value demonstrated better diagnostic performance compared to mono-exponential ADC.
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spelling pubmed-74461862020-08-26 Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis Liang, Jianye Li, Jing Li, Zhipeng Meng, Tiebao Chen, Jieting Ma, Weimei Chen, Shen Li, Xie Wu, Yaopan He, Ni BMC Cancer Research Article BACKGROUND AND OBJECTIVES: The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust evidence in this issue using a meta-analysis method. MATERIALS AND METHODS: The researches regarding the differential diagnosis of lung lesions using IVIM-DWI were systemically searched in Pubmed, Embase, Web of science and Wangfang database without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan’s nomogram was used to predict the post-test probabilities. RESULTS: Eleven studies with 481 malignant and 258 benign lung lesions were included. Most include studies showed a low to unclear risk of bias and low concerns regarding applicability. Lung cancer demonstrated a significant lower ADC (SMD = -1.17, P < 0.001), D (SMD = -1.02, P < 0.001) and f values (SMD = -0.43, P = 0.005) than benign lesions, except D* value (SMD = 0.01, P = 0.96). D value demonstrated the best diagnostic performance (sensitivity = 89%, specificity = 71%, AUC = 0.90) and highest post-test probability (57, 57, 43 and 43% for D, ADC, f and D* values) in the differential diagnosis of lung tumors, followed by ADC (sensitivity = 85%, specificity = 72%, AUC = 0.86), f (sensitivity = 71%, specificity = 61%, AUC = 0.71) and D* values (sensitivity = 70%, specificity = 60%, AUC = 0.66). CONCLUSION: IVIM-DWI parameters show potentially strong diagnostic capabilities in the differential diagnosis of lung tumors based on the tumor cellularity and perfusion characteristics, and D value demonstrated better diagnostic performance compared to mono-exponential ADC. BioMed Central 2020-08-24 /pmc/articles/PMC7446186/ /pubmed/32831052 http://dx.doi.org/10.1186/s12885-020-07308-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Liang, Jianye
Li, Jing
Li, Zhipeng
Meng, Tiebao
Chen, Jieting
Ma, Weimei
Chen, Shen
Li, Xie
Wu, Yaopan
He, Ni
Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis
title Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis
title_full Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis
title_fullStr Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis
title_full_unstemmed Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis
title_short Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis
title_sort differentiating the lung lesions using intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446186/
https://www.ncbi.nlm.nih.gov/pubmed/32831052
http://dx.doi.org/10.1186/s12885-020-07308-z
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