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Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer

BACKGROUND: Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict...

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Autores principales: Lai, Tianfu, Chen, Xiaofeng, Yang, Zhiqi, Huang, Ruibin, Liao, Yuting, Chen, Xiangguang, Dai, Zhuozhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587620/
https://www.ncbi.nlm.nih.gov/pubmed/36273200
http://dx.doi.org/10.1186/s40644-022-00499-7
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author Lai, Tianfu
Chen, Xiaofeng
Yang, Zhiqi
Huang, Ruibin
Liao, Yuting
Chen, Xiangguang
Dai, Zhuozhi
author_facet Lai, Tianfu
Chen, Xiaofeng
Yang, Zhiqi
Huang, Ruibin
Liao, Yuting
Chen, Xiangguang
Dai, Zhuozhi
author_sort Lai, Tianfu
collection PubMed
description BACKGROUND: Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients. METHODS: A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (K(trans), V(e), K(ep)) and semiquantitative DCE-MRI parameters (W(− in), W(− out), TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS). RESULTS: LVI-positive patients had a higher K(ep) value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and K(ep) value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and K(ep) yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence. CONCLUSION: The quantitative parameter of K(ep) could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00499-7.
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spelling pubmed-95876202022-10-23 Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer Lai, Tianfu Chen, Xiaofeng Yang, Zhiqi Huang, Ruibin Liao, Yuting Chen, Xiangguang Dai, Zhuozhi Cancer Imaging Research Article BACKGROUND: Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients. METHODS: A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (K(trans), V(e), K(ep)) and semiquantitative DCE-MRI parameters (W(− in), W(− out), TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS). RESULTS: LVI-positive patients had a higher K(ep) value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and K(ep) value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and K(ep) yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence. CONCLUSION: The quantitative parameter of K(ep) could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00499-7. BioMed Central 2022-10-22 /pmc/articles/PMC9587620/ /pubmed/36273200 http://dx.doi.org/10.1186/s40644-022-00499-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Lai, Tianfu
Chen, Xiaofeng
Yang, Zhiqi
Huang, Ruibin
Liao, Yuting
Chen, Xiangguang
Dai, Zhuozhi
Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer
title Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer
title_full Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer
title_fullStr Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer
title_full_unstemmed Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer
title_short Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer
title_sort quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587620/
https://www.ncbi.nlm.nih.gov/pubmed/36273200
http://dx.doi.org/10.1186/s40644-022-00499-7
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