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Meta-Analysis
. 2025 Oct;35(10):6193-6206.
doi: 10.1007/s00330-025-11519-y. Epub 2025 Apr 12.

Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysis

Zhiqiang Bai  1 , Lumin Xu  1 , Zujun Ding  2 , Yi Cao  1 , Zepeng Wang  1 , Wenjie Yang  3 , Wei Xu  3 , Hang Li  4
Affiliations
Meta-Analysis

Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysis

Zhiqiang Bai et al. Eur Radiol. 2025 Oct.

Abstract

Objective: This meta-analysis aims to evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based artificial intelligence (AI) in the preoperative detection of lymph node metastasis (LNM) in patients with rectal cancer and to compare it with the diagnostic performance of radiologists.

Methods: A thorough literature search was conducted across PubMed, Embase, and Web of Science to identify relevant studies published up to September 2024. The selected studies focused on the diagnostic performance of MRI-based AI in detecting rectal cancer LNM. A bivariate random-effects model was employed to calculate pooled sensitivity and specificity, each reported with 95% confidence intervals (CIs). Study heterogeneity was assessed using the I2 statistic. Furthermore, the modified quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool was applied to assess the methodological quality of the selected studies.

Results: Seventeen studies were included in this meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) for MRI-based AI in detecting preoperative LNM in rectal cancer were 0.71 (95% CI: 0.66-0.74), 0.71 (95% CI: 0.67-0.75), and 0.77 (95% CI: 0.73-0.80), respectively. For radiologists, these values were 0.64 (95% CI: 0.49-0.77), 0.72 (95% CI: 0.62-0.80), and 0.74 (95% CI: 0.68-0.80). Both analyses showed no significant publication bias (p > 0.05).

Conclusions: MRI-based AI demonstrates diagnostic performance similar to that of radiologists. The high heterogeneity among studies limits the strength of these findings, and further research with external validation datasets is necessary to confirm the results and assess their practical clinical value.

Key points: Question How effective is MRI-based AI in detecting LNM in rectal cancer patients compared to traditional radiology methods? Findings The diagnostic performance of MRI-based AI is comparable to radiologists, with pooled sensitivity and specificity both at 0.71, indicating moderate accuracy. Clinical relevance Integrating MRI-based AI can enhance diagnostic efficiency in identifying LNM, especially in settings with limited access to skilled radiologists, but requires further validation.

Keywords: Artificial intelligence; Lymphatic metastasis; Magnetic resonance imaging; Meta-analysis; Rectal neoplasms.

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Conflict of interest statement

Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Hang Li. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: One of the authors has significant statistical expertise. Informed consent: Not applicable. This study does not involve human and animal participants. Ethical approval: Not applicable. This study does not involve human and animal participants. Study subjects or cohorts overlap: No overlapping study subjects or cohorts have been reported. Methodology: Retrospective Diagnostic or prognostic study Performed at one institution

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