Cargando…

Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma

BACKGROUND: Microvascular invasion (MVI) is an important prognostic factor affecting early recurrence and overall survival in hepatocellular carcinoma (HCC) patients after hepatectomy and liver transplantation, but it can be determined only in surgical specimens. Accurate preoperative prediction of...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Lin, Jin, Yue-Xinzi, Ji, Ya-Zhou, Mu, Yuan, Zhang, Shi-Chang, Pan, Shi-Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7167416/
https://www.ncbi.nlm.nih.gov/pubmed/32327913
http://dx.doi.org/10.3748/wjg.v26.i14.1647
_version_ 1783523568484614144
author Wang, Lin
Jin, Yue-Xinzi
Ji, Ya-Zhou
Mu, Yuan
Zhang, Shi-Chang
Pan, Shi-Yang
author_facet Wang, Lin
Jin, Yue-Xinzi
Ji, Ya-Zhou
Mu, Yuan
Zhang, Shi-Chang
Pan, Shi-Yang
author_sort Wang, Lin
collection PubMed
description BACKGROUND: Microvascular invasion (MVI) is an important prognostic factor affecting early recurrence and overall survival in hepatocellular carcinoma (HCC) patients after hepatectomy and liver transplantation, but it can be determined only in surgical specimens. Accurate preoperative prediction of MVI is conducive to clinical decisions. AIM: To develop and validate a preoperative prediction model for MVI in patients with HCC. METHODS: Data from 454 patients with HCC who underwent hepatectomy at the First Affiliated Hospital of Nanjing Medical University between May 2016 and October 2019 were retrospectively collected. Then, the patients were nonrandomly split into a training cohort and a validation cohort. Logistic regression analysis was used to identify variables significantly associated with MVI that were then included in the nomogram. We evaluated the discrimination and calibration ability of the nomogram by using R software. RESULTS: MVI was confirmed in 209 (46.0%) patients by a pathological examination. Multivariate logistic regression analysis identified four risk factors independently associated with MVI: Tumor size [odds ratio (OR) = 1.195; 95% confidence interval (CI): 1.107–1.290; P < 0.001], number of tumors (OR = 4.441; 95%CI: 2.112–9.341; P < 0.001), neutrophils (OR = 1.714; 95%CI: 1.036–2.836; P = 0.036), and serum α-fetoprotein (20–400 ng/mL, OR = 1.955; 95%CI: 1.055–3.624; P = 0.033; >400 ng/mL, OR = 3.476; 95%CI: 1.950–6.195; P < 0.001). The concordance index was 0.79 (95%CI: 0.74–0.84) and 0.81 (95%CI: 0.74–0.89) in the training and validation cohorts, respectively. The calibration curves showed good agreement between the predicted risk by the nomogram and real outcomes. CONCLUSION: We have developed and validated a preoperative prediction model for MVI in patients with HCC. The model could aid physicians in clinical treatment decision making.
format Online
Article
Text
id pubmed-7167416
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Baishideng Publishing Group Inc
record_format MEDLINE/PubMed
spelling pubmed-71674162020-04-23 Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma Wang, Lin Jin, Yue-Xinzi Ji, Ya-Zhou Mu, Yuan Zhang, Shi-Chang Pan, Shi-Yang World J Gastroenterol Retrospective Study BACKGROUND: Microvascular invasion (MVI) is an important prognostic factor affecting early recurrence and overall survival in hepatocellular carcinoma (HCC) patients after hepatectomy and liver transplantation, but it can be determined only in surgical specimens. Accurate preoperative prediction of MVI is conducive to clinical decisions. AIM: To develop and validate a preoperative prediction model for MVI in patients with HCC. METHODS: Data from 454 patients with HCC who underwent hepatectomy at the First Affiliated Hospital of Nanjing Medical University between May 2016 and October 2019 were retrospectively collected. Then, the patients were nonrandomly split into a training cohort and a validation cohort. Logistic regression analysis was used to identify variables significantly associated with MVI that were then included in the nomogram. We evaluated the discrimination and calibration ability of the nomogram by using R software. RESULTS: MVI was confirmed in 209 (46.0%) patients by a pathological examination. Multivariate logistic regression analysis identified four risk factors independently associated with MVI: Tumor size [odds ratio (OR) = 1.195; 95% confidence interval (CI): 1.107–1.290; P < 0.001], number of tumors (OR = 4.441; 95%CI: 2.112–9.341; P < 0.001), neutrophils (OR = 1.714; 95%CI: 1.036–2.836; P = 0.036), and serum α-fetoprotein (20–400 ng/mL, OR = 1.955; 95%CI: 1.055–3.624; P = 0.033; >400 ng/mL, OR = 3.476; 95%CI: 1.950–6.195; P < 0.001). The concordance index was 0.79 (95%CI: 0.74–0.84) and 0.81 (95%CI: 0.74–0.89) in the training and validation cohorts, respectively. The calibration curves showed good agreement between the predicted risk by the nomogram and real outcomes. CONCLUSION: We have developed and validated a preoperative prediction model for MVI in patients with HCC. The model could aid physicians in clinical treatment decision making. Baishideng Publishing Group Inc 2020-04-14 2020-04-14 /pmc/articles/PMC7167416/ /pubmed/32327913 http://dx.doi.org/10.3748/wjg.v26.i14.1647 Text en ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
spellingShingle Retrospective Study
Wang, Lin
Jin, Yue-Xinzi
Ji, Ya-Zhou
Mu, Yuan
Zhang, Shi-Chang
Pan, Shi-Yang
Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
title Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
title_full Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
title_fullStr Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
title_full_unstemmed Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
title_short Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
title_sort development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
topic Retrospective Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7167416/
https://www.ncbi.nlm.nih.gov/pubmed/32327913
http://dx.doi.org/10.3748/wjg.v26.i14.1647
work_keys_str_mv AT wanglin developmentandvalidationofapredictionmodelformicrovascularinvasioninhepatocellularcarcinoma
AT jinyuexinzi developmentandvalidationofapredictionmodelformicrovascularinvasioninhepatocellularcarcinoma
AT jiyazhou developmentandvalidationofapredictionmodelformicrovascularinvasioninhepatocellularcarcinoma
AT muyuan developmentandvalidationofapredictionmodelformicrovascularinvasioninhepatocellularcarcinoma
AT zhangshichang developmentandvalidationofapredictionmodelformicrovascularinvasioninhepatocellularcarcinoma
AT panshiyang developmentandvalidationofapredictionmodelformicrovascularinvasioninhepatocellularcarcinoma