A preoperative prediction model for sentinel lymph node status using artificial intelligence on mammographic images and clinicopathological variables in patients with clinically node-negative breast cancer

Abstract

Abstract Background: Cancer detection systems using artificial intelligence (AI) are a rapidly growing research area, in breast cancer. Sentinel lymph node biopsy (SLNB) is the recommended surgical axillary staging method in patients with clinically node-negative breast cancer, although approximately 75–80% have a negative sentinel lymph node (N0) status in the final pathology report. Previous prediction models for N0 status included variables only available postoperatively, thus defeating their purpose. Therefore, we aimed to investigate whether two AI systems, Transpara (Screenpoint Medical) and Laboratory for Individualized Breast Radiodensity Assessment (LIBRA), on mammographic images can be used to improve a previous prediction model for N0 status using only preoperatively available variables. To our knowledge, this is the first preoperative prediction model for N0 status combining AI detection on mammographic images with clinicopathological variables. Methods: This retrospective cohort study included 755 women with primary breast cancer treated at Lund University Hospital between 2009 and 2012. Mammographic images were analyzed using Transpara and LIBRA. Preoperative clinicopathological and radiological variables were used in a multivariable logistic regression analysis to predict N0 status with multiple imputation. The area under the receiver operating curve (AUC) was used to assess model performance and a nomogram was developed. The agreement between preoperative radiological and postoperative pathological tumor size was assessed using correlation. Results: We proposed a preoperative prediction model for N0 status using AI detection on mammographic images and clinicopathological variables, with an AUC of 0.695 (confidence interval: 0.653–0.736). Applying the model, SLNB could be putatively omitted in 23.8% of patients if a false-negative rate of 10% was accepted. The mean difference between radiologic and pathologic tumor size was 0.4 mm and the corresponding Pearson correlation coefficient 0.62. Conclusion: To our knowledge, the prediction model proposed in this manuscript is the first preoperative prediction model for N0 status using AI on mammographic images and routine preoperative patients and tumor characteristics. The correlation between tumor size measurements suggests that radiologic tumor size can replace pathologic size as a predictor of N0 status. Applying this model may enable safe omission of SLNB in 23.8% of patients.