Application of CT and MRI images based on an artificial intelligence algorithm for predicting lymph node metastasis in breast cancer patients: a meta-analysis

Abstract

Abstract Background This study aimed to comprehensively evaluate the accuracy and effect of computed tomography (CT) and magnetic resonance imaging (MRI) based on artificial intelligence (AI) algorithms for predicting lymph node metastasis in breast cancer patients. Methods We systematically searched the PubMed, Embase and Cochrane Library databases for literature from inception to June 2023 using keywords that included ‘artificial intelligence’, ‘CT,’ ‘MRI’, ‘breast cancer’ and ‘lymph nodes’. Studies that met the inclusion criteria were screened and their data were extracted for analysis. The main outcome measures included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve (AUC). Results A total of 16 studies were included in the final meta-analysis, covering 4,764 breast cancer patients. Among them, 11 studies used the manual algorithm MRI to calculate breast cancer risk, which had a sensitivity of 0.85 (95% confidence interval [CI] 0.79–0.90; p < 0.001; I 2 = 75.3%), specificity of 0.81 (95% CI 0.66–0.83; p < 0.001; I 2 = 0%), a positive likelihood ratio of 4.6 (95% CI 4.0–4.8), a negative likelihood ratio of 0.18 (95% CI 0.13–0.26) and a diagnostic odds ratio of 25 (95% CI 17–38). Five studies used manual algorithm CT to calculate breast cancer risk, which had a sensitivity of 0.88 (95% CI 0.79–0.94; p < 0.001; I 2 = 87.0%), specificity of 0.80 (95% CI 0.69–0.88; p < 0.001; I 2 = 91.8%), a positive likelihood ratio of 4.4 (95% CI 2.7–7.0), a negative likelihood ratio of 0.15 (95% CI 0.08–0.27) and a diagnostic odds ratio of 30 (95% CI 12–72). For MRI and CT, the AUC after study pooling was 0.85 (95% CI 0.82–0.88) and 0.91 (95% CI 0.88–0.93), respectively. Conclusion Computed tomography and MRI images based on an AI algorithm have good diagnostic accuracy in predicting lymph node metastasis in breast cancer patients and have the potential for clinical application.

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