Diagnosis of axillary lymph node metastasis in breast cancer: a systematic review and meta-analysis of the literature on ultrasound and magnetic resonance imaging published from 2014 to 2025.

Background: Breast cancer remains one of the most prevalent and lethal malignancies among women worldwide. Axillary lymph node (ALN) metastasis is a critical prognostic factor guiding treatment, yet the current diagnostic approaches such as sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) are invasive and carry notable risks. Noninvasive imaging modalities including magnetic resonance imaging (MRI) and ultrasound (US) have shown value in ALN evaluation but exhibit limitations in accessibility, operator dependence, and diagnostic accuracy. Meta-analyses on this subject have largely focused on single modalities or included outdated data. This study thus aimed to provide an updated nondirect comparative assessment of conventional ultrasound (CUS), contrast-enhanced ultrasound (CEUS), and MRI in detecting ALN metastasis through a review of the relevant literature published between 2014 and 2025. Methods: Relevant studies on CUS, CEUS, and MRI published from January 2014 to September 2025 in the PubMed, Embase, and Web of Science databases were retrieved before September 5, 2025. Histopathological findings from SLNB or ALND served as the reference standard. Study quality and bias risk were assessed via the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A bivariate random-effects model was applied to estimate the pooled sensitivity, pooled specificity, diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (AUC) for meta-analysis. This study was registered with the PROSPERO (International Prospective Register of Systematic Reviews) (No. CRD42024589342). Results: A total of 41 studies comprising 53 datasets examining patients with breast cancer, comprising 17 CUS studies (n=5,603), 20 CEUS studies (n=2,907), and 16 MRI studies (n=2,232), that met the inclusion and exclusion criteria were included. Each study used SLNB or ALND as the histopathological reference standard. Among the 17 studies on CUS, the pooled sensitivity and specificity for detecting ALN metastasis were 0.69 (95% CI: 0.56-0.80) and 0.85 (95% CI: 0.77-0.90), respectively, while the AUC was 0.85 (95% CI: 0.82-0.88). Among the 20 studies on CEUS, the combined sensitivity and specificity for detecting ALN metastasis were 0.86 (95% CI: 0.79-0.91) and 0.88 (95% CI: 0.84-0.91), respectively, while the AUC was 0.93 (95% CI: 0.90-0.95). Among 16 studies on MRI, the pooled sensitivity and specificity for detecting ALN metastasis were 0.62 (95% CI: 0.53-0.71) and 0.85 (95% CI: 0.79-0.89), respectively, while the AUC was 0.82 (95% CI: 0.78-0.85). Additionally, QUADAS-2 assessment indicated a low overall risk of bias. Meta-regression analysis revealed that sample size, study design, publication year, and specific technical characteristics were significant sources of heterogeneity across CUS, CEUS, and MRI modalities, generally influencing specificity, although sample size also significantly affected sensitivity in CEUS, and sensitivity analysis confirmed the robustness of the result. Deeks' funnel plot showed no substantial publication bias except for in the CEUS studies (P>0.05). Conclusions: CUS, CEUS, and MRI possess distinct advantages in detecting ALN metastasis of breast cancer. However, in the selection of imaging diagnostic methods for clinical decision-making, factors such as cost and technical demand should be carefully considered.