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Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation
BACKGROUND: High early recurrence (ER) of hepatocellular carcinoma (HCC) after microwave ablation (MWA) represents a sign of aggressive behavior and severely worsens prognosis. The aim of this study was to estimate the outcome of HCC following MWA and develop a response algorithmic strategy based on...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9429304/ https://www.ncbi.nlm.nih.gov/pubmed/36042507 http://dx.doi.org/10.1186/s40644-022-00471-5 |
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author | Zhang, Zhaohe Yu, Jie Liu, Sisi Dong, Linan Liu, Tiefang Wang, Haiyi Han, Zhiyu Zhang, Xiaojing Liang, Ping |
author_facet | Zhang, Zhaohe Yu, Jie Liu, Sisi Dong, Linan Liu, Tiefang Wang, Haiyi Han, Zhiyu Zhang, Xiaojing Liang, Ping |
author_sort | Zhang, Zhaohe |
collection | PubMed |
description | BACKGROUND: High early recurrence (ER) of hepatocellular carcinoma (HCC) after microwave ablation (MWA) represents a sign of aggressive behavior and severely worsens prognosis. The aim of this study was to estimate the outcome of HCC following MWA and develop a response algorithmic strategy based on multiparametric MRI and clinical variables. METHODS: In this retrospective study, we reviewed the records of 339 patients (mean age, 62 ± 12 years; 106 men) treated with percutaneous MWA for HCC between January 2014 and December 2017 that were evaluated by multiparametric MRI. These patients were randomly split into a development and an internal validation group (3:1). Logistic regression analysis was used to screen imaging features. Multivariate Cox regression analysis was then performed to determine predictors of ER (within 2 years) of MWA. The response algorithmic strategy to predict ER was developed and validated using these data sets. ER rates were also evaluated by Kaplan–Meier analysis. RESULTS: Based on logistic regression analyses, we established an image response algorithm integrating ill-defined margins, lack of capsule enhancement, pre-ablative ADC, ΔADC, and EADC to calculate recurrence scores and define the risk of ER. In a multivariate Cox regression model, the independent risk factors of ER (p < 0.05) were minimal ablative margin (MAM) (HR 0.57; 95% CI 0.35 – 0.95; p < 0.001), the recurrence score (HR: 9.25; 95% CI 4.25 – 16.56; p = 0.021), and tumor size (HR 6.21; 95% CI 1.25 – 10.82; p = 0.014). Combining MAM and tumor size, the recurrence score calculated by the response algorithmic strategy provided predictive accuracy of 93.5%, with sensitivity of 92.3% and specificity of 83.1%. Kaplan–Meier estimates of the rates of ER in the low-risk and high-risk groups were 6.8% (95% CI 4.0 – 9.6) and 30.5% (95% CI 23.6 – 37.4), respectively. CONCLUSION: A response algorithmic strategy based on multiparametric MRI and clinical variables was useful for predicting the ER of HCC after MWA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00471-5. |
format | Online Article Text |
id | pubmed-9429304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94293042022-09-01 Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation Zhang, Zhaohe Yu, Jie Liu, Sisi Dong, Linan Liu, Tiefang Wang, Haiyi Han, Zhiyu Zhang, Xiaojing Liang, Ping Cancer Imaging Research Article BACKGROUND: High early recurrence (ER) of hepatocellular carcinoma (HCC) after microwave ablation (MWA) represents a sign of aggressive behavior and severely worsens prognosis. The aim of this study was to estimate the outcome of HCC following MWA and develop a response algorithmic strategy based on multiparametric MRI and clinical variables. METHODS: In this retrospective study, we reviewed the records of 339 patients (mean age, 62 ± 12 years; 106 men) treated with percutaneous MWA for HCC between January 2014 and December 2017 that were evaluated by multiparametric MRI. These patients were randomly split into a development and an internal validation group (3:1). Logistic regression analysis was used to screen imaging features. Multivariate Cox regression analysis was then performed to determine predictors of ER (within 2 years) of MWA. The response algorithmic strategy to predict ER was developed and validated using these data sets. ER rates were also evaluated by Kaplan–Meier analysis. RESULTS: Based on logistic regression analyses, we established an image response algorithm integrating ill-defined margins, lack of capsule enhancement, pre-ablative ADC, ΔADC, and EADC to calculate recurrence scores and define the risk of ER. In a multivariate Cox regression model, the independent risk factors of ER (p < 0.05) were minimal ablative margin (MAM) (HR 0.57; 95% CI 0.35 – 0.95; p < 0.001), the recurrence score (HR: 9.25; 95% CI 4.25 – 16.56; p = 0.021), and tumor size (HR 6.21; 95% CI 1.25 – 10.82; p = 0.014). Combining MAM and tumor size, the recurrence score calculated by the response algorithmic strategy provided predictive accuracy of 93.5%, with sensitivity of 92.3% and specificity of 83.1%. Kaplan–Meier estimates of the rates of ER in the low-risk and high-risk groups were 6.8% (95% CI 4.0 – 9.6) and 30.5% (95% CI 23.6 – 37.4), respectively. CONCLUSION: A response algorithmic strategy based on multiparametric MRI and clinical variables was useful for predicting the ER of HCC after MWA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00471-5. BioMed Central 2022-08-30 /pmc/articles/PMC9429304/ /pubmed/36042507 http://dx.doi.org/10.1186/s40644-022-00471-5 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 Zhang, Zhaohe Yu, Jie Liu, Sisi Dong, Linan Liu, Tiefang Wang, Haiyi Han, Zhiyu Zhang, Xiaojing Liang, Ping Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation |
title | Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation |
title_full | Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation |
title_fullStr | Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation |
title_full_unstemmed | Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation |
title_short | Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation |
title_sort | multiparametric liver mri for predicting early recurrence of hepatocellular carcinoma after microwave ablation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9429304/ https://www.ncbi.nlm.nih.gov/pubmed/36042507 http://dx.doi.org/10.1186/s40644-022-00471-5 |
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