Wang L, Jin YX, Ji YZ, Mu Y, Zhang SC, Pan SY. Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma. World J Gastroenterol 2020; 26(14): 1647-1659 [PMID: 32327913 DOI: 10.3748/wjg.v26.i14.1647]
Corresponding Author of This Article
Shi-Yang Pan, MD, PhD, Professor, Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing 210029, Jiangsu Province, China. sypan@njmu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
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. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Apr 14, 2020; 26(14): 1647-1659 Published online Apr 14, 2020. doi: 10.3748/wjg.v26.i14.1647
Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
Lin Wang, Yue-Xinzi Jin, Ya-Zhou Ji, Yuan Mu, Shi-Chang Zhang, Shi-Yang Pan
Lin Wang, Yue-Xinzi Jin, Ya-Zhou Ji, Yuan Mu, Shi-Chang Zhang, Shi-Yang Pan, Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
Lin Wang, Yue-Xinzi Jin, Ya-Zhou Ji, Yuan Mu, Shi-Chang Zhang, Shi-Yang Pan, National Key Clinical Department of Laboratory Medicine, Jiangsu Province Hospital, Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
Author contributions: Wang L and Pan SY designed the study and wrote the paper; Jin YXZ and Ji YZ collected the clinical data; Wang L, Mu Y, and Zhang SC contributed to data analysis and validation.
Supported bythe National Natural Science Foundation of China, No. 81672100; and the Key Laboratory for Laboratory Medicine of Jiangsu Province of China, No. ZDXKB2016005.
Institutional review board statement: This study was reviewed and approved by the Institutional Ethics Committee of the First Affiliated Hospital of Nanjing Medical University.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: We have no financial relationships to disclose.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Shi-Yang Pan, MD, PhD, Professor, Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing 210029, Jiangsu Province, China. sypan@njmu.edu.cn
Received: December 19, 2019 Peer-review started: December 19, 2019 First decision: January 19, 2020 Revised: March 14, 2020 Accepted: March 19, 2020 Article in press: March 19, 2020 Published online: April 14, 2020 Processing time: 117 Days and 2.8 Hours
ARTICLE HIGHLIGHTS
Research background
Microvascular invasion (MVI) is a definite risk factor of early recurrence and poor surgical outcomes of hepatocellular carcinoma (HCC). Accurate preoperative prediction of MVI is helpful for the choice of clinical treatment options and evaluation of postoperative efficacy.
Research motivation
Histologic examination of the surgical specimens is the only reliable method to diagnose MVI. There is an urgent need for an effective tool to predict MVI preoperatively.
Research objectives
This study aimed to construct a new prediction model, mainly based on routine laboratory parameters, to achieve more accurate prediction for MVI in patients with HCC before surgery.
Research methods
In this retrospective study, data from 454 patients with HCC who underwent hepatectomy were collected and nonrandomly split into a training cohort and a validation cohort. Univariate and multivariable logistic regression analyses were performed to identify variables significantly associated with MVI, and a new preoperative prediction model for MVI was established and further validated.
Research results
The incidence of MVI was 46.0% in patients with hepatectomy. Tumor size, number of tumors, neutrophils, and serum α-fetoprotein were identified as independent significant factors associated with MVI. A nomogram was established and showed good performance in the evaluation of discrimination and calibration.
Research conclusions
This prediction model could effectively predict MVI with good discrimination and calibration ability.
Research perspectives
Data from other centers are needed to further validate the clinical usability of this novel model.
