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Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation

BACKGROUND: The Liver Imaging Reporting and Data System treatment response algorithm (LI-RADS TRA) was developed to evaluate the tumor response of patients with hepatocellular carcinoma (HCC) after locoregional treatments. This study aimed to evaluate the performance of LI-RADS computed tomography (...

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Autores principales: Zhang, Yun, Wang, Jinju, Li, Hui, Zheng, Tianying, Jiang, Hanyu, Li, Mou, Song, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186681/
https://www.ncbi.nlm.nih.gov/pubmed/32355832
http://dx.doi.org/10.21037/atm.2020.03.120
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author Zhang, Yun
Wang, Jinju
Li, Hui
Zheng, Tianying
Jiang, Hanyu
Li, Mou
Song, Bin
author_facet Zhang, Yun
Wang, Jinju
Li, Hui
Zheng, Tianying
Jiang, Hanyu
Li, Mou
Song, Bin
author_sort Zhang, Yun
collection PubMed
description BACKGROUND: The Liver Imaging Reporting and Data System treatment response algorithm (LI-RADS TRA) was developed to evaluate the tumor response of patients with hepatocellular carcinoma (HCC) after locoregional treatments. This study aimed to evaluate the performance of LI-RADS computed tomography (CT) TRA version 2018 in tumor response assessment and survival prediction of patients with single HCC after radiofrequency ablation (RFA). METHODS: Forty patients who underwent RFA for single HCC between 2010 and 2016 were included in this retrospective study. The overall survival (OS) data from all the patients after the first therapy was collected. Two readers independently assessed the pretreatment (within 7 d) and posttreatment (within 90 d after RFA) CT manifestations using the LI-RADS version 2018 CT TRA. Inter-reader agreement was assessed. Another radiologist re-evaluated any divergent results and came to the final conclusion. The performance of LI-RADS version 2018 CT TRA for tumor response assessment and predicting survival of patients with single HCC after RFA was evaluated. RESULTS: Interobserver agreement was moderate between the 2 readers [κ=0.602, 95% confidence interval (CI): 0.390–0.814] when using LI-RADS version 2018 TRA to evaluate tumor response for patients with single HCC after RFA. Patients classified as LR-TR viable had significantly lower OS than those classified as LR-TR nonviable (P=0.005) and LR-TR equivocal (P=0.036). However, the OS between LR-TR nonviable and LR-TR equivocal did not differ significantly (P=0.901). CONCLUSIONS: LI-RADS version 2018 CT TRA can be applied to predict viable or nonviable HCC after RFA. Patients with LR-TR viable had significantly lower OS than those with LR-TR nonviable and LR-TR equivocal. More research is needed to validate the performance of LI-RADS version 2018 TRA in HCC tumor response evaluation, to better grasp the use of the tie-breaking rule, and to improve the accuracy of prediction for tumor viability.
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spelling pubmed-71866812020-04-30 Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation Zhang, Yun Wang, Jinju Li, Hui Zheng, Tianying Jiang, Hanyu Li, Mou Song, Bin Ann Transl Med Original Article BACKGROUND: The Liver Imaging Reporting and Data System treatment response algorithm (LI-RADS TRA) was developed to evaluate the tumor response of patients with hepatocellular carcinoma (HCC) after locoregional treatments. This study aimed to evaluate the performance of LI-RADS computed tomography (CT) TRA version 2018 in tumor response assessment and survival prediction of patients with single HCC after radiofrequency ablation (RFA). METHODS: Forty patients who underwent RFA for single HCC between 2010 and 2016 were included in this retrospective study. The overall survival (OS) data from all the patients after the first therapy was collected. Two readers independently assessed the pretreatment (within 7 d) and posttreatment (within 90 d after RFA) CT manifestations using the LI-RADS version 2018 CT TRA. Inter-reader agreement was assessed. Another radiologist re-evaluated any divergent results and came to the final conclusion. The performance of LI-RADS version 2018 CT TRA for tumor response assessment and predicting survival of patients with single HCC after RFA was evaluated. RESULTS: Interobserver agreement was moderate between the 2 readers [κ=0.602, 95% confidence interval (CI): 0.390–0.814] when using LI-RADS version 2018 TRA to evaluate tumor response for patients with single HCC after RFA. Patients classified as LR-TR viable had significantly lower OS than those classified as LR-TR nonviable (P=0.005) and LR-TR equivocal (P=0.036). However, the OS between LR-TR nonviable and LR-TR equivocal did not differ significantly (P=0.901). CONCLUSIONS: LI-RADS version 2018 CT TRA can be applied to predict viable or nonviable HCC after RFA. Patients with LR-TR viable had significantly lower OS than those with LR-TR nonviable and LR-TR equivocal. More research is needed to validate the performance of LI-RADS version 2018 TRA in HCC tumor response evaluation, to better grasp the use of the tie-breaking rule, and to improve the accuracy of prediction for tumor viability. AME Publishing Company 2020-03 /pmc/articles/PMC7186681/ /pubmed/32355832 http://dx.doi.org/10.21037/atm.2020.03.120 Text en 2020 Annals of Translational Medicine. 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
Zhang, Yun
Wang, Jinju
Li, Hui
Zheng, Tianying
Jiang, Hanyu
Li, Mou
Song, Bin
Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation
title Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation
title_full Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation
title_fullStr Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation
title_full_unstemmed Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation
title_short Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation
title_sort performance of li-rads version 2018 ct treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186681/
https://www.ncbi.nlm.nih.gov/pubmed/32355832
http://dx.doi.org/10.21037/atm.2020.03.120
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