Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Aug 27, 2024; 16(8): 2546-2554
Published online Aug 27, 2024. doi: 10.4240/wjgs.v16.i8.2546
Preoperative prediction of hepatocellular carcinoma microvascular invasion based on magnetic resonance imaging feature extraction artificial neural network
Jing-Yi Xu, Yu-Fan Yang, Zhong-Yue Huang, Xin-Ye Qian, Fan-Hua Meng
Jing-Yi Xu, Yu-Fan Yang, Xin-Ye Qian, Center of Hepatobiliary Pancreatic Disease, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
Zhong-Yue Huang, Department of Surgical, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
Fan-Hua Meng, Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai 200040, China
Co-corresponding authors: Xin-Ye Qian and Fan-Hua Meng.
Author contributions: Xu JY conceived the study; Xu JY, Yang YF, and Huang ZY collected and analyzed data; Xu JY and Qian XY prepared the first draft of the manuscript; Xu JY and Meng FH provided expert suggestions and revised the manuscript; and all authors contributed to the article and approved the submitted version. Qian XY and Meng FH, co-corresponding authors, have made equal contributions to this work. After discussion among all the authors, Qian XY and Meng FH were designated the corresponding authors for three main reasons. First, this study was a collaborative effort, and it was reasonable to designate co-corresponding authors. The author accurately reflects on the allocation of responsibilities and burdens related to the time and effort required to complete the research and final manuscript. Designating two co-corresponding authors ensures effective communication and management of post-submission matters, thereby improving the quality and reliability of the paper. Second, the co-corresponding authors of the research team possess diverse professional knowledge and skills in different fields, and their appointments best reflect this diversity. It also promotes the most comprehensive and in-depth exploration of research topics, ultimately enriching the readers’ understanding by providing various expert perspectives. Qian XY and Meng FH made substantial and equal contributions to the research process. The selection of these researchers as co-corresponding authors acknowledges and respects their equal contributions and demonstrates a spirit of collaboration and teamwork. We believe that designating Qian XY and Meng FH as co-corresponding authors is suitable for our manuscript as it accurately reflects the collaborative spirit, equal contribution, and diversity of our team.
Supported by the Tsinghua University Institute of Precision Medicine, No. 2022ZLA006.
Institutional review board statement: The studies involving human participants were reviewed and approved by the ethic committee of Beijing Tsinghua Changgung Hospital.
Informed consent statement: The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Conflict-of-interest statement: All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Data sharing statement: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.
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: Xin-Ye Qian, MD, Doctor, Center of Hepatobiliary Pancreatic Disease, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, No. 168 Litang Road, Changping District, Beijing 102218, China. m13716890662@163.com
Received: May 7, 2024
Revised: May 29, 2024
Accepted: June 27, 2024
Published online: August 27, 2024
Processing time: 101 Days and 3.7 Hours
Abstract
BACKGROUND

Hepatocellular carcinoma (HCC) recurrence is highly correlated with increased mortality. Microvascular invasion (MVI) is indicative of aggressive tumor biology in HCC.

AIM

To construct an artificial neural network (ANN) capable of accurately predicting MVI presence in HCC using magnetic resonance imaging.

METHODS

This study included 255 patients with HCC with tumors < 3 cm. Radiologists annotated the tumors on the T1-weighted plain MR images. Subsequently, a three-layer ANN was constructed using image features as inputs to predict MVI status in patients with HCC. Postoperative pathological examination is considered the gold standard for determining MVI. Receiver operating characteristic analysis was used to evaluate the effectiveness of the algorithm.

RESULTS

Using the bagging strategy to vote for 50 classifier classification results, a prediction model yielded an area under the curve (AUC) of 0.79. Moreover, correlation analysis revealed that alpha-fetoprotein values and tumor volume were not significantly correlated with the occurrence of MVI, whereas tumor sphericity was significantly correlated with MVI (P < 0.01).

CONCLUSION

Analysis of variable correlations regarding MVI in tumors with diameters < 3 cm should prioritize tumor sphericity. The ANN model demonstrated strong predictive MVI for patients with HCC (AUC = 0.79).

Keywords: Hepatocellular carcinoma; Microvascular invasion; Artificial neural network; Magnetic resonance imaging; Tumor sphericity; Area under the curve

Core Tip: Constructing an artificial neural network (ANN) using magnetic resonance imaging can accurately predict the presence of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) with tumors < 3 cm. Tumor sphericity has emerged as a significant factor associated with MVI occurrence, emphasizing its importance in MVI analysis. The strong predictive capability of the ANN model, with an area under the curve of 0.79, shows its potential to enhance MVI prediction accuracy and aid in the management of patients with HCC.