Construction and validation of a nomogram prediction model for axillary lymph node metastasis of cT1 invasive breast cancer

Shuqi Wang; Dongmo Wang; Xin Wen; Xiangli Xu; Dongmei Liu; Jiawei Tian

doi:10.1097/CEJ.0000000000000860

Construction and validation of a nomogram prediction model for axillary lymph node metastasis of cT1 invasive breast cancer

Eur J Cancer Prev. 2023 Nov 21. doi: 10.1097/CEJ.0000000000000860. Online ahead of print.

Authors

Shuqi Wang¹, Dongmo Wang¹, Xin Wen², Xiangli Xu³, Dongmei Liu¹, Jiawei Tian¹

Affiliations

¹ Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang.
² The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai.
³ The second hospital of Harbin, Harbin, Heilongjiang, China.

PMID: 37997911
DOI: 10.1097/CEJ.0000000000000860

Abstract

Objective: Based on the ultrasonic characteristics of the breast mass and axillary lymph nodes as well as the clinicopathological information, a model was developed for predicting axillary lymph node metastasis in cT1 breast cancer, and relevant features associated with axillary lymph node metastasis were identified.

Methods: Our retrospective study included 808 patients with cT1 invasive breast cancer treated at the Second Affiliated Hospital and the Cancer Hospital Affiliated with Harbin Medical University from February 2012 to August 2021 (250 cases in the positive axillary lymph node group and 558 cases in the negative axillary lymph node group). We allocated 564 cases to the training set and 244 cases to the verification set. R software was used to compare clinicopathological data and ultrasonic features between the two groups. Based on the results of multivariate logistic regression analysis, a nomogram prediction model was developed and verified for axillary lymph node metastasis of cT1 breast cancer.

Results: Univariate and multivariate logistic regression analysis indicated that palpable lymph nodes (P = 0.003), tumor location (P = 0.010), marginal contour (P < 0.001), microcalcification (P = 0.010), surrounding tissue invasion (P = 0.046), ultrasonic detection of lymph nodes (P = 0.001), cortical thickness (P < 0.001) and E-cadherin (P < 0.001) are independently associated with axillary lymph node metastasis. Using these features, a nomogram was developed for axillary lymph node metastasis. The training set had an area under the curve of 0.869, while the validation set had an area under the curve of 0.820. Based on the calibration curve, the model predicted axillary lymph node metastases were in good agreement with reality (P > 0.05). Nomogram's net benefit was good based on decision curve analysis.

Conclusion: The nomogram developed in this study has a high negative predictive value for axillary lymph node metastasis in invasive cT1 breast c ancer. Patients with no axillary lymph node metastases can be accurately screened using this nomogram, potentially allowing this group of patients to avoid invasive surgery.