• Caixing Yuan, Guolin Xu, Xiangmei Zhan, Min Xie, Mingcong Luo, Lilan She, and Yunjing Xue.
• Department of Radiology, Affiliated Hospital of Putian College, Putian, China.
• Medicine (Baltimore). 2023 Oct 20; 102 (42): e35672e35672.

AbstractSentinel lymph node (SLN) status is closely related to axillary lymph node metastasis in breast cancer. However, SLN biopsy has certain limitations due to invasiveness and diagnostic efficiency. This study aimed to develop a model to predict the risk of axillary SLN metastasis in early-stage breast cancer based on mammography, a noninvasive, cost-effective, and potential complementary way. Herein, 649 patients with early-stage breast cancer (cT1-T2) who received SLN biopsy were assigned to the training cohort (n = 487) and the validation cohort (n = 162). A prediction model based on specific characteristics of tumor mass in mammography was developed and validated with R software. The performance of model was evaluated by receiver operating characteristic curve, calibration plot, and decision curve analysis. Tumor margins, spicular structures, calcification, and tumor size were independent predictors of SLN metastasis (all P < .05). A nomogram showed a satisfactory performance with an AUC of 0.829 (95% CI = 0.792-0.865) in the training cohort and an AUC of 0.825 (95% CI = 0.763-0.888) in validation cohort. The consistency between model-predicted results and actual observations showed great Hosmer-Lemeshow goodness-of-fit (P = .104). Patients could benefit from clinical decisions guided by the present model within the threshold probabilities of 6% to 84%. The prediction model for axillary SLN metastasis showed satisfactory discrimination, calibration abilities, and wide clinical practicability. These findings suggest that our prediction model based on mammography characteristics is a reliable tool for predicting SLN metastasis in patients with early-stage breast cancer.Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.

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