Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study
Ming-Yu Wu,1,* Qian Qiao,1,* Ke Wang,2– 4 Gu-Wei Ji,2– 4,* Bing Cai,1 Xiang-Cheng Li2– 4 1Department of Hepatobiliary Surgery, Wuxi People’s Hospital, Wuxi, People’s Republic of China; 2Hepatobiliary Center, The First Affiliated Hospital of Nanjing M...
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doaj-602f4e323d6e41c0978bb398a0907c0a2020-11-25T03:16:33ZengDove Medical PressCancer Management and Research1179-13222020-05-01Volume 123503351253803Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional StudyWu MYQiao QWang KJi GWCai BLi XCMing-Yu Wu,1,* Qian Qiao,1,* Ke Wang,2– 4 Gu-Wei Ji,2– 4,* Bing Cai,1 Xiang-Cheng Li2– 4 1Department of Hepatobiliary Surgery, Wuxi People’s Hospital, Wuxi, People’s Republic of China; 2Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 3Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People’s Republic of China; 4NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiang-Cheng LiHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, People’s Republic of ChinaTel +86 18951999088Fax +86 2568136450Email drxcli@njmu.edu.cnBing CaiDepartment of Hepatobiliary Surgery, Wuxi People’s Hospital, 299 Qingyang Road, Wuxi 214023, People’s Republic of ChinaTel +86 13358112071Fax +86 51082700778Email wxphcb@163.comBackground: The ideal candidates for resection are patients with solitary hepatocellular carcinoma (HCC); however, postoperative recurrence rate remains high. We aimed to establish prognostic models to predict HCC recurrence based on readily accessible clinical parameters and multi-institutional databases.Patients and Methods: A total of 485 patients undergoing curative resection for solitary HCC were recruited from two independent institutions and the Cancer Imaging Archive database. We randomly divided the patients into training (n=323) and validation cohorts (n=162). Two models were developed: one using pre-operative and one using pre- and post-operative parameters. Performance of the models was compared with staging systems.Results: Using multivariable analysis, albumin-bilirubin grade, serum alpha-fetoprotein and tumor size were selected into the pre-operative model; albumin-bilirubin grade, serum alpha-fetoprotein, tumor size, microvascular invasion and cirrhosis were selected into the postoperative model. The two models exhibited better discriminative ability (concordance index: 0.673– 0.728) and lower prediction error (integrated Brier score: 0.169– 0.188) than currently used staging systems for predicting recurrence in both cohorts. Both models stratified patients into low- and high-risk subgroups of recurrence with distinct recurrence patterns.Conclusion: The two models with corresponding user-friendly calculators are useful tools to predict recurrence before and after resection that may facilitate individualized management of solitary HCC.Keywords: hepatocellular carcinoma, resection, recurrence, survival, modellinghttps://www.dovepress.com/development-and-validation-of-pre--and-post-operative-models-to-predic-peer-reviewed-article-CMARhepatocellular carcinomaresectionrecurrencesurvivalmodelling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wu MY Qiao Q Wang K Ji GW Cai B Li XC |
spellingShingle |
Wu MY Qiao Q Wang K Ji GW Cai B Li XC Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study Cancer Management and Research hepatocellular carcinoma resection recurrence survival modelling |
author_facet |
Wu MY Qiao Q Wang K Ji GW Cai B Li XC |
author_sort |
Wu MY |
title |
Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study |
title_short |
Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study |
title_full |
Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study |
title_fullStr |
Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study |
title_full_unstemmed |
Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study |
title_sort |
development and validation of pre- and post-operative models to predict recurrence after resection of solitary hepatocellular carcinoma: a multi-institutional study |
publisher |
Dove Medical Press |
series |
Cancer Management and Research |
issn |
1179-1322 |
publishDate |
2020-05-01 |
description |
Ming-Yu Wu,1,* Qian Qiao,1,* Ke Wang,2– 4 Gu-Wei Ji,2– 4,* Bing Cai,1 Xiang-Cheng Li2– 4 1Department of Hepatobiliary Surgery, Wuxi People’s Hospital, Wuxi, People’s Republic of China; 2Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 3Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People’s Republic of China; 4NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiang-Cheng LiHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, People’s Republic of ChinaTel +86 18951999088Fax +86 2568136450Email drxcli@njmu.edu.cnBing CaiDepartment of Hepatobiliary Surgery, Wuxi People’s Hospital, 299 Qingyang Road, Wuxi 214023, People’s Republic of ChinaTel +86 13358112071Fax +86 51082700778Email wxphcb@163.comBackground: The ideal candidates for resection are patients with solitary hepatocellular carcinoma (HCC); however, postoperative recurrence rate remains high. We aimed to establish prognostic models to predict HCC recurrence based on readily accessible clinical parameters and multi-institutional databases.Patients and Methods: A total of 485 patients undergoing curative resection for solitary HCC were recruited from two independent institutions and the Cancer Imaging Archive database. We randomly divided the patients into training (n=323) and validation cohorts (n=162). Two models were developed: one using pre-operative and one using pre- and post-operative parameters. Performance of the models was compared with staging systems.Results: Using multivariable analysis, albumin-bilirubin grade, serum alpha-fetoprotein and tumor size were selected into the pre-operative model; albumin-bilirubin grade, serum alpha-fetoprotein, tumor size, microvascular invasion and cirrhosis were selected into the postoperative model. The two models exhibited better discriminative ability (concordance index: 0.673– 0.728) and lower prediction error (integrated Brier score: 0.169– 0.188) than currently used staging systems for predicting recurrence in both cohorts. Both models stratified patients into low- and high-risk subgroups of recurrence with distinct recurrence patterns.Conclusion: The two models with corresponding user-friendly calculators are useful tools to predict recurrence before and after resection that may facilitate individualized management of solitary HCC.Keywords: hepatocellular carcinoma, resection, recurrence, survival, modelling |
topic |
hepatocellular carcinoma resection recurrence survival modelling |
url |
https://www.dovepress.com/development-and-validation-of-pre--and-post-operative-models-to-predic-peer-reviewed-article-CMAR |
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