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The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published result...

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Autores principales: Li, Zhipeng, Li, Xinming, Peng, Chuan, Dai, Wei, Huang, Haitao, Li, Xie, Xie, Chuanmiao, Liang, Jianye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655131/
https://www.ncbi.nlm.nih.gov/pubmed/33194685
http://dx.doi.org/10.3389/fonc.2020.575272
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author Li, Zhipeng
Li, Xinming
Peng, Chuan
Dai, Wei
Huang, Haitao
Li, Xie
Xie, Chuanmiao
Liang, Jianye
author_facet Li, Zhipeng
Li, Xinming
Peng, Chuan
Dai, Wei
Huang, Haitao
Li, Xie
Xie, Chuanmiao
Liang, Jianye
author_sort Li, Zhipeng
collection PubMed
description Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI. Methods: Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities. Results: Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, P < 0.001) but a lower MD (SMD = −1.29, P < 0.001) and ADC (SMD = −1.21, P < 0.001) than benign tumors. The MK (SMD = −1.36, P = 0.006) rather than the MD (SMD = 0.29, P = 0.20) or ADC (SMD = 0.26, P = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29). Conclusion: The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma in situ.
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spelling pubmed-76551312020-11-13 The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis Li, Zhipeng Li, Xinming Peng, Chuan Dai, Wei Huang, Haitao Li, Xie Xie, Chuanmiao Liang, Jianye Front Oncol Oncology Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI. Methods: Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities. Results: Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, P < 0.001) but a lower MD (SMD = −1.29, P < 0.001) and ADC (SMD = −1.21, P < 0.001) than benign tumors. The MK (SMD = −1.36, P = 0.006) rather than the MD (SMD = 0.29, P = 0.20) or ADC (SMD = 0.26, P = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29). Conclusion: The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. Frontiers Media S.A. 2020-10-27 /pmc/articles/PMC7655131/ /pubmed/33194685 http://dx.doi.org/10.3389/fonc.2020.575272 Text en Copyright © 2020 Li, Li, Peng, Dai, Huang, Li, Xie and Liang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Li, Zhipeng
Li, Xinming
Peng, Chuan
Dai, Wei
Huang, Haitao
Li, Xie
Xie, Chuanmiao
Liang, Jianye
The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis
title The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis
title_full The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis
title_fullStr The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis
title_full_unstemmed The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis
title_short The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis
title_sort diagnostic performance of diffusion kurtosis imaging in the characterization of breast tumors: a meta-analysis
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655131/
https://www.ncbi.nlm.nih.gov/pubmed/33194685
http://dx.doi.org/10.3389/fonc.2020.575272
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