SYSTEMATIC REVIEW article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
This article is part of the Research TopicRecent Advancements in AI-Assisted Gynecologic Cancer DetectionView all 4 articles
Artificial intelligence-based magnetic resonance imaging for preoperative staging of patients with endometrial cancer: a systematic review and meta-analysis
Provisionally accepted
- Ningbo Medical Center Lihuili Hospital, Ningbo University, Bingbo, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: 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. Methods: 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. Results: 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. Conclusion: 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.
Keywords: artificial intelligence, Magnetic Resonance Imaging, endometrial cancer, Meta-analysis, Systematic review
Received: 31 Jul 2025; Accepted: 20 Nov 2025.
Copyright: © 2025 Zheng, Lin and Li. 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: Jinjing Zheng, zhengjinjing1223@163.com
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.