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
Abstract
BACKGROUND

Synchronous liver metastasis (SLM) is a significant contributor to morbidity in colorectal cancer (CRC). There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.

AIM

To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix (GLCM) features collected from magnetic resonance imaging (MRI).

METHODS

Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023. Patients were randomly divided into a training and validation group (3:7). The clinical parameters and GLCM features extracted from MRI were included as candidate variables. The prediction model was constructed using a generalized linear regression model, random forest model (RFM), and artificial neural network model. Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.

RESULTS

Among the 392 patients, 48 had SLM (12.24%). We obtained fourteen GLCM imaging data for variable screening of SLM prediction models. Inverse difference, mean sum, sum entropy, sum variance, sum of squares, energy, and difference variance were listed as candidate variables, and the prediction efficiency (area under the curve) of the subsequent RFM in the training set and internal validation set was 0.917 [95% confidence interval (95%CI): 0.866-0.968] and 0.09 (95%CI: 0.858-0.960), respectively.

CONCLUSION

A predictive model combining GLCM image features with machine learning can predict SLM in CRC. This model can assist clinicians in making timely and personalized clinical decisions.

Keywords: Colorectal cancer; Synchronous liver metastasis; Gray-level co-occurrence matrix; Machine learning algorithm; Prediction model

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.