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Meta-Analysis
. 2025 Mar 31;25(1):44.
doi: 10.1186/s40644-025-00863-3.

Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis

Chia-Fen Lee  1   2 , Joseph Lin  3   4   5 , Yu-Len Huang  6 , Shou-Tung Chen  3   4 , Chen-Te Chou  1 , Dar-Ren Chen  3   4 , Wen-Pei Wu  7   8   9
Affiliations
Meta-Analysis

Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis

Chia-Fen Lee et al. Cancer Imaging. .

Abstract

Background: To perform a systematic review and meta-analysis that assesses the diagnostic performance of deep learning algorithms applied to breast MRI for predicting axillary lymph nodes metastases in patients of breast cancer.

Methods: A systematic literature search in PubMed, MEDLINE, and Embase databases for articles published from January 2004 to February 2025. Inclusion criteria were: patients with breast cancer; deep learning using MRI images was applied to predict axillary lymph nodes metastases; sufficient data were present; original research articles. Quality Assessment of Diagnostic Accuracy Studies-AI and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. A summary receiver operating characteristic curve (SROC) was performed. R statistical software (version 4.4.0) was used for statistical analyses.

Results: A total of 10 studies were included. The pooled sensitivity and specificity were 0.76 (95% CI, 0.67-0.83) and 0.81 (95% CI, 0.74-0.87), respectively, with both measures having moderate between-study heterogeneity (I2 = 61% and 60%, respectively; p < 0.01). The SROC analysis yielded a weighted AUC of 0.788.

Conclusion: This meta-analysis demonstrates that deep learning algorithms applied to breast MRI offer promising diagnostic performance for predicting axillary lymph node metastases in breast cancer patients. Incorporating deep learning into clinical practice may enhance decision-making by providing a non-invasive method to more accurately predict lymph node involvement, potentially reducing unnecessary surgeries.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A flowchart of the literature review and study selection
Fig. 2
Fig. 2
Forest plots of pooled sensitivity (a) and specificity(b) for the deep learning model to detect axillary lymph node metastasis in breast cancer
Fig. 3
Fig. 3
Forest plots of the sensitivity (a) and specificity (b) for subgroup analysis according to the target of assessment (the primary tumor only or the axillary lymph nodes and the primary tumor)
Fig. 4
Fig. 4
Cross-hair plot of studies included in the meta-analysis
Fig. 5
Fig. 5
Summary receiver operating characteristic (SROC) curves based on the bivariate approach
See this image and copyright information in PMC

References

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