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Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data

BACKGROUND: Most existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data u...

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Autores principales: Zou, Yanzheng, Yue, Ming, Jia, Linna, Wang, Yifan, Chen, Hongbo, Zhang, Amei, Xia, Xueshan, Liu, Wei, Yu, Rongbin, Yang, Sheng, Huang, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10676612/
https://www.ncbi.nlm.nih.gov/pubmed/38007418
http://dx.doi.org/10.1186/s12885-023-11628-1
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author Zou, Yanzheng
Yue, Ming
Jia, Linna
Wang, Yifan
Chen, Hongbo
Zhang, Amei
Xia, Xueshan
Liu, Wei
Yu, Rongbin
Yang, Sheng
Huang, Peng
author_facet Zou, Yanzheng
Yue, Ming
Jia, Linna
Wang, Yifan
Chen, Hongbo
Zhang, Amei
Xia, Xueshan
Liu, Wei
Yu, Rongbin
Yang, Sheng
Huang, Peng
author_sort Zou, Yanzheng
collection PubMed
description BACKGROUND: Most existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data using a novel modeling strategy. The predictive performance of the longitudinal model was also compared with a baseline model. METHODS: A total of 400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals (DAA) were enrolled in the study. Patients were randomly divided into a training set (70%) and a validation set (30%). Informative features were extracted from the longitudinal variables and then put into the random survival forest (RSF) to develop the longitudinal model. A baseline model including the same variables was built for comparison. RESULTS: During a median follow-up time of approximately 5 years, 25 patients (8.9%) in the training set and 11 patients (9.2%) in the validation set developed HCC. The areas under the receiver-operating characteristics curves (AUROC) for the longitudinal model were 0.9507 (0.8838–0.9997), 0.8767 (0.6972,0.9918), and 0.8307 (0.6941,0.9993) for 1-, 2- and 3-year risk prediction, respectively. The brier scores of the longitudinal model were also relatively low for the 1-, 2- and 3-year risk prediction (0.0283, 0.0561, and 0.0501, respectively). In contrast, the baseline model only achieved mediocre AUROCs of around 0.6 (0.6113, 0.6213, and 0.6480, respectively). CONCLUSIONS: Our longitudinal model yielded accurate predictions of HCC risk in patients with HCV-relate cirrhosis, outperforming the baseline model. Our model can provide patients with valuable prognosis information and guide the intensity of surveillance in clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-11628-1.
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spelling pubmed-106766122023-11-25 Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data Zou, Yanzheng Yue, Ming Jia, Linna Wang, Yifan Chen, Hongbo Zhang, Amei Xia, Xueshan Liu, Wei Yu, Rongbin Yang, Sheng Huang, Peng BMC Cancer Research BACKGROUND: Most existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data using a novel modeling strategy. The predictive performance of the longitudinal model was also compared with a baseline model. METHODS: A total of 400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals (DAA) were enrolled in the study. Patients were randomly divided into a training set (70%) and a validation set (30%). Informative features were extracted from the longitudinal variables and then put into the random survival forest (RSF) to develop the longitudinal model. A baseline model including the same variables was built for comparison. RESULTS: During a median follow-up time of approximately 5 years, 25 patients (8.9%) in the training set and 11 patients (9.2%) in the validation set developed HCC. The areas under the receiver-operating characteristics curves (AUROC) for the longitudinal model were 0.9507 (0.8838–0.9997), 0.8767 (0.6972,0.9918), and 0.8307 (0.6941,0.9993) for 1-, 2- and 3-year risk prediction, respectively. The brier scores of the longitudinal model were also relatively low for the 1-, 2- and 3-year risk prediction (0.0283, 0.0561, and 0.0501, respectively). In contrast, the baseline model only achieved mediocre AUROCs of around 0.6 (0.6113, 0.6213, and 0.6480, respectively). CONCLUSIONS: Our longitudinal model yielded accurate predictions of HCC risk in patients with HCV-relate cirrhosis, outperforming the baseline model. Our model can provide patients with valuable prognosis information and guide the intensity of surveillance in clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-11628-1. BioMed Central 2023-11-25 /pmc/articles/PMC10676612/ /pubmed/38007418 http://dx.doi.org/10.1186/s12885-023-11628-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Zou, Yanzheng
Yue, Ming
Jia, Linna
Wang, Yifan
Chen, Hongbo
Zhang, Amei
Xia, Xueshan
Liu, Wei
Yu, Rongbin
Yang, Sheng
Huang, Peng
Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data
title Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data
title_full Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data
title_fullStr Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data
title_full_unstemmed Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data
title_short Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data
title_sort accurate prediction of hcc risk after svr in patients with hepatitis c cirrhosis based on longitudinal data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10676612/
https://www.ncbi.nlm.nih.gov/pubmed/38007418
http://dx.doi.org/10.1186/s12885-023-11628-1
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