# Artificial intelligence-based magnetic resonance imaging for preoperative staging of patients with endometrial cancer: a systematic review and meta-analysis

## TL;DR

This study reviews how artificial intelligence with MRI improves preoperative staging accuracy for endometrial cancer patients.

## Contribution

A meta-analysis of AI-based MRI performance for predicting myometrial and cervical invasion in endometrial cancer.

## Key findings

- AI-based MRI shows moderate accuracy in predicting deep myometrial invasion with an AUC of 0.83.
- AI-based MRI demonstrates higher accuracy for cervical stroma invasion with an AUC of 0.90.
- The study highlights the need for large-scale clinical trials to validate AI-based MRI utility.

## Abstract

To systematically collect all literature on the value of artificial intelligence (AI)-based magnetic resonance imaging (MRI) for preoperative prediction of myometrial invasion and cervical stroma invasion in patients with endometrial cancer and conduct a meta-analysis to provide the latest and most comprehensive synthesis of current research findings.

A systematic literature search was conducted in PubMed, Web of Science, Embase, and the Cochrane Library databases up to March 2025. The methodological quality of the included studies was assessed using the QUADAS-2 tool. Statistical analyses were primarily conducted using Stata15.0 and Review Manager 5.4.1 software. Outcomes included combined Sen, Spe, +LR, -LR, DOR, and their 95% CI. The SROC curve was plotted, and the AUC was calculated. The Deeks’ funnel plot was used to detect publication bias and assumed small-study effects.

Finally, 8 studies (including 13 cohorts) were included. The overall performance of AI-based MRI for the prediction of deep myometrial invasion showed a combined Sen, Spe, +LR, -LR, DOR, and AUC value of 0.80 (95% CI: 0.75-0.85), 0.81 (95% CI: 0.64-0.91), 4.2 (95% CI: 2.0-8.5), 0.24 (95% CI: 0.17-0.34), 17 (95% CI: 6-47), and 0.83 (95% CI: 0.80-0.86), respectively. The overall performance of AI-based MRI for the prediction of cervical stroma invasion showed a combined Sen, Spe, +LR, -LR, DOR, and AUC value of 0.78 (95% CI: 0.55-0.91), 0.86 (95% CI: 0.79-0.91), 5.6 (95% CI: 4.3-7.4), 0.25 (95% CI: 0.12-0.55), 22 (95% CI: 11-44), and 0.90 (95% CI: 0.87-0.92) respectively.

AI-based MRI can improve the accuracy of preoperative staging of patients with endometrial cancer to a certain extent. However, considering the limitations of this article, additional large-scale, prospective, multicenter clinical trials are necessary to further investigate the utility of AI-based MRI in the preoperative staging of endometrial cancer.