Related Experiment Video
Updated: Jun 22, 2026
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Validation of breast cancer nomograms for predicting the non-sentinel lymph node metastases after a positive sentinel
Objective:
In the study, our aim was to evaluate the predictability of four different nomograms on non-sentinel lymph node metastases (NSLNM) in breast cancer (BC) patients with positive sentinel lymph node (SLN) biopsy in a multi-center study.
Methods:
We identified 607 patients who had a positive SLN biopsy and completion axillary lymph node dissection (CALND) at seven different BC treatment centers in Turkey. The BC nomograms developed by the Memorial Sloan Kettering Cancer Center (MSKCC), Tenon Hospital, Cambridge University, and Stanford University were used to calculate the probability of NSLNM. Area under (AUC) Receiver Operating Characteristics Curve (ROC) was calculated for each nomogram and values greater than 0.70 were accepted as demonstrating good discrimination.
Results:
Two hundred and eighty-seven patients (287) of 607 patients (47.2%) had a positive axillary NSLNM. The AUC values were 0.705, 0.711, 0.730, and 0.582 for the MSKCC, Cambridge, Stanford, and Tenon models, respectively. On the multivariate analysis; overall metastasis size (OMS), lymphovascular invasion (LVI), and proportion of positive SLN to total SLN were found statistically significant. We created a formula to predict the NSLNM in our patient population and the AUC value of this formula was 0.8023.
Conclusions:
The MSKCC, Cambridge, and Stanford nomograms were good discriminators of NSLNM in SLN positive BC patients in this study. A newly created formula in this study needs to be validated in prospective studies in different patient populations. A nomogram to predict NSLNM in patients with positive SLN biopsy developed at one institution should be used with caution.
