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Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis
SIMPLE SUMMARY: Locoregional therapy (LRT) is widely performed as a nonsurgical treatment for hepatocellular carcinoma (HCC). Following LRT, precise assessment of post-treatment imaging can play an important role in determining residual tumor viability and future treatment for patients with HCC. Owi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8430492/ https://www.ncbi.nlm.nih.gov/pubmed/34503241 http://dx.doi.org/10.3390/cancers13174432 |
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author | Huh, Yeon Jong Kim, Dong Hwan Kim, Bohyun Choi, Joon-Il Rha, Sung Eun |
author_facet | Huh, Yeon Jong Kim, Dong Hwan Kim, Bohyun Choi, Joon-Il Rha, Sung Eun |
author_sort | Huh, Yeon Jong |
collection | PubMed |
description | SIMPLE SUMMARY: Locoregional therapy (LRT) is widely performed as a nonsurgical treatment for hepatocellular carcinoma (HCC). Following LRT, precise assessment of post-treatment imaging can play an important role in determining residual tumor viability and future treatment for patients with HCC. Owing to the need to provide a more standardized image interpretation, Liver Imaging Reporting and Data Systems (LI-RADS) treatment response (TR) algorithm was developed. We conducted a systematic review and meta-analysis to assess the accuracy of each imaging feature of LI-RADS TR (LR-TR) viable category for diagnosing viable HCC after LRT. This meta-analysis of 10 studies comprising 971 patients found that the pooled sensitivity and diagnostic odds ratio were the highest for arterial phase hyperenhancement (APHE), followed by washout appearance and enhancement similar to pretreatment. The diagnostic performance of APHE was significantly different depending on the type of reference standard and MRI contrast agent. The results of this meta-analysis represent the currently available evidence regarding the performance of LR-TR algorithm. ABSTRACT: We aimed to investigate the accuracy of each imaging feature of LI-RADS treatment response (LR-TR) viable category for diagnosing tumor viability of locoregional therapy (LRT)-treated HCC. Studies evaluating the per feature accuracy of the LR-TR viable category on dynamic contrast-enhanced CT or MRI were identified in databases. A bivariate random-effects model was used to calculate the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of LR-TR viable features. Ten studies assessing the accuracies of LR-TR viable features (1153 treated observations in 971 patients) were included. The pooled sensitivities and specificities for diagnosing viable HCC were 81% (95% confidence interval [CI], 63–92%) and 95% (95% CI, 88–98%) for nodular, mass-like, or irregular thick tissue (NMLIT) with arterial phase hyperenhancement (APHE), 55% (95% CI, 34–75%) and 96% (95% CI, 94–98%) for NMLIT with washout appearance, and 21% (95% CI, 6–53%) and 98% (95% CI, 92–100%) for NMLIT with enhancement similar to pretreatment, respectively. Of these features, APHE showed the highest pooled DOR (81 [95% CI, 25–261]), followed by washout appearance (32 [95% CI, 13–82]) and enhancement similar to pretreatment (14 [95% CI, 5–39]). In conclusion, APHE provided the highest sensitivity and DOR for diagnosing viable HCC following LRT, while enhancement similar to pretreatment showed suboptimal performance. |
format | Online Article Text |
id | pubmed-8430492 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84304922021-09-11 Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis Huh, Yeon Jong Kim, Dong Hwan Kim, Bohyun Choi, Joon-Il Rha, Sung Eun Cancers (Basel) Systematic Review SIMPLE SUMMARY: Locoregional therapy (LRT) is widely performed as a nonsurgical treatment for hepatocellular carcinoma (HCC). Following LRT, precise assessment of post-treatment imaging can play an important role in determining residual tumor viability and future treatment for patients with HCC. Owing to the need to provide a more standardized image interpretation, Liver Imaging Reporting and Data Systems (LI-RADS) treatment response (TR) algorithm was developed. We conducted a systematic review and meta-analysis to assess the accuracy of each imaging feature of LI-RADS TR (LR-TR) viable category for diagnosing viable HCC after LRT. This meta-analysis of 10 studies comprising 971 patients found that the pooled sensitivity and diagnostic odds ratio were the highest for arterial phase hyperenhancement (APHE), followed by washout appearance and enhancement similar to pretreatment. The diagnostic performance of APHE was significantly different depending on the type of reference standard and MRI contrast agent. The results of this meta-analysis represent the currently available evidence regarding the performance of LR-TR algorithm. ABSTRACT: We aimed to investigate the accuracy of each imaging feature of LI-RADS treatment response (LR-TR) viable category for diagnosing tumor viability of locoregional therapy (LRT)-treated HCC. Studies evaluating the per feature accuracy of the LR-TR viable category on dynamic contrast-enhanced CT or MRI were identified in databases. A bivariate random-effects model was used to calculate the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of LR-TR viable features. Ten studies assessing the accuracies of LR-TR viable features (1153 treated observations in 971 patients) were included. The pooled sensitivities and specificities for diagnosing viable HCC were 81% (95% confidence interval [CI], 63–92%) and 95% (95% CI, 88–98%) for nodular, mass-like, or irregular thick tissue (NMLIT) with arterial phase hyperenhancement (APHE), 55% (95% CI, 34–75%) and 96% (95% CI, 94–98%) for NMLIT with washout appearance, and 21% (95% CI, 6–53%) and 98% (95% CI, 92–100%) for NMLIT with enhancement similar to pretreatment, respectively. Of these features, APHE showed the highest pooled DOR (81 [95% CI, 25–261]), followed by washout appearance (32 [95% CI, 13–82]) and enhancement similar to pretreatment (14 [95% CI, 5–39]). In conclusion, APHE provided the highest sensitivity and DOR for diagnosing viable HCC following LRT, while enhancement similar to pretreatment showed suboptimal performance. MDPI 2021-09-02 /pmc/articles/PMC8430492/ /pubmed/34503241 http://dx.doi.org/10.3390/cancers13174432 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Systematic Review Huh, Yeon Jong Kim, Dong Hwan Kim, Bohyun Choi, Joon-Il Rha, Sung Eun Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis |
title | Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis |
title_full | Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis |
title_fullStr | Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis |
title_full_unstemmed | Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis |
title_short | Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis |
title_sort | per-feature accuracy of liver imaging reporting and data system locoregional treatment response algorithm: a systematic review and meta-analysis |
topic | Systematic Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8430492/ https://www.ncbi.nlm.nih.gov/pubmed/34503241 http://dx.doi.org/10.3390/cancers13174432 |
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