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Comparison with other studies that used clinicopathological features for breast cancer lymph node classification.
| Study (Algorithm Type) | Total Patients | Mean AUC (95% CI) |
|---|---|---|
| This study (XGBoost) | 8381 | 0.76 (0.73–0.80) |
| Takada et al. [55] (ADTree) | 467 | 0.77 (0.69–0.86) |
| Zheng et al. [52] (without radiomics, neural network) | 1342 | 0.72 (0.63–0.82) |
| Dihge et al. [53] (neural network) | 800 | 0.74 (0.72–0.76) |
| Meng et al. [47] (non-sentinel lymph node prediction, Lasso regression) | 714 | 0.77 (0.69–0.86) |
