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Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation

BACKGROUND: A practical prognostic prediction model is absent for hepatocellular carcinoma (HCC) patients after curative ablation. We aimed to develop a radiomics model based on gadoxetic acid disodium-enhanced magnetic resonance (MR) images to predict HCC recurrence after curative ablation. METHODS...

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Detalles Bibliográficos
Autores principales: Zhang, Ling, Cai, Peiqiang, Hou, Jingyu, Luo, Ma, Li, Yonggang, Jiang, Xinhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006953/
https://www.ncbi.nlm.nih.gov/pubmed/33790652
http://dx.doi.org/10.2147/CMAR.S300627
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author Zhang, Ling
Cai, Peiqiang
Hou, Jingyu
Luo, Ma
Li, Yonggang
Jiang, Xinhua
author_facet Zhang, Ling
Cai, Peiqiang
Hou, Jingyu
Luo, Ma
Li, Yonggang
Jiang, Xinhua
author_sort Zhang, Ling
collection PubMed
description BACKGROUND: A practical prognostic prediction model is absent for hepatocellular carcinoma (HCC) patients after curative ablation. We aimed to develop a radiomics model based on gadoxetic acid disodium-enhanced magnetic resonance (MR) images to predict HCC recurrence after curative ablation. METHODS: We retrospectively enrolled 132 patients with HCC who underwent curative ablation. Patients were randomly divided into the training (n = 92) and validation (n = 40) cohorts. Radiomic features were extracted from gadoxetic acid disodium-enhanced MR images of the liver before curative ablation, and various baseline clinical characteristics were collected. Cox regression and random survival forests were used to construct models that incorporated radiomic features and/or clinical characteristics. The predictive performance of the different models was compared using the concordance index (C-index) and decision curves analysis (DCA). A cutoff derived from the combined model was used for risk categorization, and recurrence-free survival (RFS) was compared between groups using the Kaplan-Meier survival curve analysis. RESULTS: Twenty radiomic features and four clinical characteristics were identified and used for model construction. The radiomics model constructed by tumoral and peritumoral radiomic features had better predictive performance (C-index 0.698, 95% confidence interval [CI] 0.640–0.755) compared with the clinical model (C-index 0.614, 95% CI 0.499–0.695), while the combined model had the best predictive performance (C-index 0.706, 95% CI 0.638–0.763). A better net benefit was observed with the combined model compared with the other two models according to the DCA. Distinct RFS distributions were observed when patients were categorized based on the cutoff derived from the combined model (Log rank test, p = 0.007). CONCLUSION: The radiomics model which combined radiomic features extracted from gadoxetic acid disodium-enhanced MR images with clinical characteristics could predict HCC recurrence after curative ablation.
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spelling pubmed-80069532021-03-30 Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation Zhang, Ling Cai, Peiqiang Hou, Jingyu Luo, Ma Li, Yonggang Jiang, Xinhua Cancer Manag Res Original Research BACKGROUND: A practical prognostic prediction model is absent for hepatocellular carcinoma (HCC) patients after curative ablation. We aimed to develop a radiomics model based on gadoxetic acid disodium-enhanced magnetic resonance (MR) images to predict HCC recurrence after curative ablation. METHODS: We retrospectively enrolled 132 patients with HCC who underwent curative ablation. Patients were randomly divided into the training (n = 92) and validation (n = 40) cohorts. Radiomic features were extracted from gadoxetic acid disodium-enhanced MR images of the liver before curative ablation, and various baseline clinical characteristics were collected. Cox regression and random survival forests were used to construct models that incorporated radiomic features and/or clinical characteristics. The predictive performance of the different models was compared using the concordance index (C-index) and decision curves analysis (DCA). A cutoff derived from the combined model was used for risk categorization, and recurrence-free survival (RFS) was compared between groups using the Kaplan-Meier survival curve analysis. RESULTS: Twenty radiomic features and four clinical characteristics were identified and used for model construction. The radiomics model constructed by tumoral and peritumoral radiomic features had better predictive performance (C-index 0.698, 95% confidence interval [CI] 0.640–0.755) compared with the clinical model (C-index 0.614, 95% CI 0.499–0.695), while the combined model had the best predictive performance (C-index 0.706, 95% CI 0.638–0.763). A better net benefit was observed with the combined model compared with the other two models according to the DCA. Distinct RFS distributions were observed when patients were categorized based on the cutoff derived from the combined model (Log rank test, p = 0.007). CONCLUSION: The radiomics model which combined radiomic features extracted from gadoxetic acid disodium-enhanced MR images with clinical characteristics could predict HCC recurrence after curative ablation. Dove 2021-03-25 /pmc/articles/PMC8006953/ /pubmed/33790652 http://dx.doi.org/10.2147/CMAR.S300627 Text en © 2021 Zhang et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Zhang, Ling
Cai, Peiqiang
Hou, Jingyu
Luo, Ma
Li, Yonggang
Jiang, Xinhua
Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation
title Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation
title_full Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation
title_fullStr Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation
title_full_unstemmed Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation
title_short Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation
title_sort radiomics model based on gadoxetic acid disodium-enhanced mr imaging to predict hepatocellular carcinoma recurrence after curative ablation
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006953/
https://www.ncbi.nlm.nih.gov/pubmed/33790652
http://dx.doi.org/10.2147/CMAR.S300627
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