AUTHOR=Chen Lulin , Xu Fei , Chen Lujiao 

TITLE=Diagnostic accuracy of artificial intelligence based on imaging data for predicting distant metastasis of colorectal cancer: a systematic review and meta-analysis

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1558915

DOI=10.3389/fonc.2025.1558915

ISSN=2234-943X

ABSTRACT=BackgroundColorectal 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.PurposeThe 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.MethodsA 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.ResultsSeventeen 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.ConclusionImaging 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.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42024516063).