SynthesisFrontiers in oncology2026
The predictive value of multiple artificial intelligence models in axillary lymph node metastasis of breast cancer detected by ultrasound - a network meta-analysis.
Synthesis in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Objective: To compare the diagnostic performance of different AI models in predicting axillary lymph node metastasis in breast cancer detected by ultrasound. Method: A systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane Library, CNKI, and Wanfang Data from July 2015 to July 2025 for diagnostic studies on the prediction of axillary lymph node metastasis in breast cancer. Relevant diagnostic accuracy data were extracted and synthesized. A network meta-analysis was performed to compare the performance of different AI models. Results: This study finally included 9 studies involving 2,813 patients through strict inclusion and exclusion criteria. The predictive value of 11 artificial intelligence models for axillary lymph node metastasis was assessed. The methodological quality of the included studies was good, and there was no obvious publication bias. The pooled diagnostic odds ratio of all diagnostic methods was 10.92, showing statistically significant heterogeneity (I² = 92%, Conclusion: Boosting-based algorithm models showed a possible signal of favorable and balanced predictive value in ultrasound-based axillary lymph node metastasis, though substantial heterogeneity and inconsistency limit definitive conclusions. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD420251119253.
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