Copyright
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma: A retrospective study
Hai-Yang Nong, Yong-Yi Cen, Shan-Jin Lu, Rui-Sui Huang, Qiong Chen, Li-Feng Huang, Jian-Ning Huang, Xue Wei, Man-Rong Liu, Lin Li, Ke Ding
Hai-Yang Nong, Shan-Jin Lu, Rui-Sui Huang, Qiong Chen, Li-Feng Huang, Jian-Ning Huang, Xue Wei, Ke Ding, Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
Hai-Yang Nong, Yong-Yi Cen, Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
Hai-Yang Nong, Yong-Yi Cen, Guangxi Clinical Medical Research Center for Hepatobiliary Diseases, Affiliated Hospital of Youiiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
Man-Rong Liu, Department of Ultrasound, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
Lin Li, Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
Co-first authors: Hai-Yang Nong and Yong-Yi Cen.
Co-corresponding authors: Man-Rong Liu and Ke Ding.
Author contributions: Nong HY, Ding K, Liu MR, Cen YY and Lu SJ carried out the studies, participated in collecting data, and drafted the manuscript; Ding K, Liu MR, Nong HY and Cen YY performed the statistical analysis and participated in its design; Huang RS, Chen Q, Huang LF, Huang JN, Wei X and Li L helped to draft the manuscript; All authors read and approved the final manuscript.
Supported by the National Natural Science Foundation of China, No. 81560278; the Health Commission of Guangxi Zhuang Autonomous Region, No. Z20200953, No. G201903023, and No. Z-A20221157; and Scientific Research and Technology Development Project of Nanning, No. 20213122.
Institutional review board statement: Adhering to the ethical principles of the Declaration of Helsinki, a retrospective analysis of 97 patients with hepatocellular carcinoma confirmed by surgical pathology at our hospital from January 2019 to July 2022 was conducted, with approval from our Institutional Review Board and completion of all patients’ informed consent (approved number: 2015-02-28-1).
Informed consent statement: Informed consent was obtained from all patients.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Ke Ding, MD, Doctor, Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, No. 13 Dancun Road, Nanning 530031, Guangxi Zhuang Autonomous Region, China.
272480365@qq.com
Received: May 8, 2024
Revised: September 6, 2024
Accepted: November 7, 2024
Published online: January 15, 2025
Processing time: 218 Days and 8.1 Hours
BACKGROUND
Microvascular invasion (MVI) is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma (HCC) surgery. Currently, there is a paucity of preoperative evaluation approaches for MVI.
AIM
To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC.
METHODS
Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital. Patients were classified into two groups: MVI-positive (n = 57) and MVI-negative (n = 40), based on postoperative pathological results. The correlation between relevant radiological signs and MVI status was analyzed. MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features, which were combined with radiological signs to construct artificial neural network (ANN) models for MVI prediction. The predictive performance of the ANN models was evaluated using area under the curve, sensitivity, and specificity. ANN models with relatively high predictive performance were screened using the DeLong test, and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models’ stability.
RESULTS
The absence of a pseudocapsule, an incomplete pseudocapsule, and the presence of tumor blood vessels were identified as independent predictors of HCC MVI. The ANN model constructed using the dominant features of the combined group (pseudocapsule status + tumor blood vessels + arterial phase + venous phase) demonstrated the best predictive performance for MVI status and was found to be automated, highly operable, and very stable.
CONCLUSION
The ANN model constructed using the dominant features of the combined group can be recommended as a non-invasive method for preoperative prediction of HCC MVI status.
Core Tip: Whether artificial neural network (ANN) models mimicking human brain provide a high predictive value for hepatocellular carcinoma (HCC) microvascular invasion (MVI). The preoperative prediction ANN model constructed on multiparametric magnetic resonance imaging is acute and stable. ANN models have clinical values and benefits in non-invasively predicting MVI in HCC patients.
