Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging—a systematic review and meta-analysis
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Background
Prediction models for sentinel lymph node (SLN) status could potentially substitute surgical axillary staging in patients with early breast cancer. Several imaging modalities have been used with various feature extraction and selection approaches. This systematic review and meta-analysis aimed to evaluate prediction models for SLN status based on breast imaging in patients with early breast cancer to summarize the current evidence and to identify areas requiring additional research.
Methods
The systematic literature search strategy was based on the Population, Intervention, Comparison, and Outcome (PICO) framework: P: female patients with clinically node-negative invasive breast cancer scheduled to undergo primary surgery; I: breast imaging; C: upfront sentinel lymph node biopsy; and O: prediction model performance regarding SLN status. The search was conducted in the PubMed, Embase, Web of Science, Cochrane, and Cumulative Index to Nursing and Allied Health Literature databases in March 2024. The screening of records, data collection, and bias assessments were performed independently by two reviewers. The risk of bias was assessed via the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and the Prediction Model Study Risk of Bias Assessment Tool. A meta-analysis was performed using the random-effects model to assess performance and heterogeneity overall and in subgroups.
Results
The literature search resulted in the inclusion of 32 articles with 11,464 patients in total. Five imaging categories were included: ultrasound ( n = 8), magnetic resonance imaging (MRI) ( n = 17), mammography ( n = 1), positron emission tomography computed tomography ( n = 1), and multiple modalities ( n = 5). Four studies, assessed as having a high risk of bias, were excluded from the meta-analysis. The meta-analysis revealed heterogeneity in overall performance, except for MRI-based studies, with a pooled area under the curve of 0.85 (95% confidence interval 0.82–0.87). Meta-regression indicated that MRI and model calibration assessment upon validation contributed to heterogeneity.
Conclusions
This systematic review and meta-analysis revealed that prediction models using breast imaging—particularly MRI—could serve as a noninvasive alternative to surgical axillary staging in patients with early breast cancer. The results illustrate the heterogeneity between studies and the need for additional high-quality studies.
Systematic review registration
PROSPERO CRD42022301852, available at https://www.crd.york.ac.uk/PROSPERO
