Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Jun 27, 2024; 16(6): 1571-1581
Published online Jun 27, 2024. doi: 10.4240/wjgs.v16.i6.1571
Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer
Kai-Feng Yang, Sheng-Jie Li, Jun Xu, Yong-Bin Zheng
Kai-Feng Yang, Yong-Bin Zheng, Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan 430030, Hubei Province, China
Sheng-Jie Li, Jun Xu, Department of Gastrointestinal Surgery, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People’s Hospital, Yichang 443008, Hubei Province, China
Author contributions: Zheng YB is responsible for the conceptualization and design of this project; Yang KF and Zheng YB are responsible for manuscript writing and monitoring the progress of the project; Li SJ and Xu J are responsible for data collection, analysis, and visualization; and all authors shall verify and submit the manuscript.
Institutional review board statement: The study was reviewed and approved by the Institutional review board of Yichang Central People’s Hospital (Approval No. 2023-089-01).
Informed consent statement: As the study only involved retrospective chart reviews, informed written consents were not required in accordance with institutional IRB policy.
Conflict-of-interest statement: The authors declare that they have no conflict of interest to disclose.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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: Yong-Bin Zheng, PhD, Doctor, Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, No. 100 Zhangzhidong Road, Wuhan 430660, Hubei Province, China. yongbinzheng@whu.edu.cn
Received: January 27, 2024
Revised: March 16, 2024
Accepted: April 25, 2024
Published online: June 27, 2024
Processing time: 154 Days and 20.1 Hours
Core Tip

Core Tip: Our predictive model for synchronous liver metastasis (SLM) in colorectal cancer (CRC) patients can screen reliable predictive variables based on clinical features. This is crucial for predicting SLM in CRC and improving patient prognosis. Imaging omics is a discipline that has developed in recent years. Based on advanced deep learning algorithms, extracting imaging features will have practical clinical value for constructing prediction models for SLM in CRC. This study combines imaging and deep learning to construct an early warning prediction model, to provide necessary auxiliary predictions for the occurrence of SLM and guide clinical decision-making.