SYSTEMATIC REVIEW article
Front. Oncol.
Sec. Gastrointestinal Cancers: Colorectal Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1558915
Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Predicting Distant Metastasis of Colorectal Cancer: a Systematic Review and Meta-Analysis
Provisionally accepted
- 1Affiliated hospital of Shaoxing University,, Shaoxing, China
- 2Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
- 3Department of Radiology, Shaoxing People's Hospital, Shaoxing, Zhejiang Province, China
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Background: Colorectal cancer is the third most common malignant tumor with the third highest incidence rate.Distant metastasis is the main cause of death in colorectal cancer patients. Early detection and prognostic prediction of colorectal cancer has improved with the widespread use of artificial intelligence technologies.Purpose: The aim of this study was to comprehensively evaluate the accuracy and validity of AI-based imaging data for predicting distant metastasis in colorectal cancer patients.Methods ： A systematic literature search was conducted to find relevant studies published up to January, 2024, in different databases. The quality of articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The predictive value of AI-based imaging data for distant metastasis in colorectal cancer patients was assessed using pooled sensitivity, specificity. To explore the reasons for heterogeneity, subgroup analyses were performed using different covariates.Results：Seventeen studies were included in the systematic evaluation. The pooled sensitivity, specificity, and AUC of AI-based imaging data for predicting distant metastasis in colorectal cancer patients were 0.86, 0.82, and 0.91. Based on QUADAS-2, risk of bias was detected in patient selection, diagnostic tests to be evaluated, and gold standard. Based on the results of subgroup analyses, found that the duration of follow-up, site of metastasis , etc. had a significant impact on the heterogeneity.Conclusion：Imaging data images based on artificial intelligence algorithms have good diagnostic accuracy for predicting distant metastasis in colorectal cancer patients and have potential for clinical application.
Keywords: ：colorectal cancer, distant metastasis, CT, MR, ultrasound, artificial intelligence, deep learning, machine learning
Received: 11 Jan 2025; Accepted: 17 Apr 2025.
Copyright: © 2025 Chen, Xu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lujiao Chen, Department of Radiology, Shaoxing People's Hospital, Shaoxing, 312000, Zhejiang Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.