Artificial intelligence based on imaging data to predict rectal cancer recurrence: A meta-analysis

Abstract

Purpose: The purpose of this study was to evaluate the diagnostic performance of artificial intelligence based on imaging data to predict rectal cancer recurrence using a meta-analysis system.

Materials and methods: Medline, Embase, Cochrane Library, Web of Science, and other databases were searched for all articles on artificial intelligence prediction of rectal cancer recurrence based on imaging data published publicly from the establishment of the library to December 31, 2023. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis was performed by the software Revman 5.4 and Statistics data (Stata), and sensitivity analysis was used to detect potential sources of heterogeneity and test to assess the presence of publication bias. We evaluated how well imaging-based data can predict recurrence in patients with rectal cancer by analysing the pooled sensitivity, specificity, and area under the curve.

Results: Ten studies were included. The pooled sensitivity, specificity, and area under the curve of imaging-based data for recurrence in patients with rectal cancer were respectively 0.84 (95 % confidence interval [CI]: 0.74-0.91), 0.87 (95 % CI: 0.82-0.91) and 0.92 (95 % CI: 0.89-0.94). Based on QUADAS-2, the quality of the article is acceptable. We found the causes of heterogeneity through meta-regression: recurrence time predesign Lasso. Based on Deeks' funnel plot, no publication bias was detected.

Conclusion: Artificial intelligence based on imaging data has a high predictive ability for rectal cancer recurrence.

Keywords: AI; Meta-analysis; Radiographic; Rectal cancer; Recurrence.