Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer
Authors Zong Q, Deng J, Ge W, Chen J, Xu D
Received 9 December 2019
Accepted for publication 26 February 2020
Published 18 March 2020 Volume 2020:12 Pages 2025—2035
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Chien-Feng Li
Qingqing Zong,1,* Jing Deng,1,* Wanli Ge,2,* Jie Chen,1 Di Xu3
1Department of Ultrasonography, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People’s Republic of China; 2Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People’s Republic of China; 3Department of Geriatric Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Di Xu
Department of Geriatric Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People’s Republic of China
Tel/Fax +86 25 8371 4511
Purpose: Axillary lymph node (ALN) involvement is an important prognostic factor of early invasive breast cancer. The objective of this study was to establish simple nomograms for predicting ALN involvement based on ultrasound (US) characteristics and evaluate the predictive value of US in the detection of ALN involvement.
Patients and Methods: A total of 1328 patients with cT1-2N0 breast cancer by physical exam were retrospectively analyzed. Univariate analysis was used for the comparison of variables, and multivariate analysis was performed by binary logistic regression analysis. The R software was used to establish simple nomograms based on the US characteristics alone. The receiver operating characteristic (ROC) curves of the prediction model and the verification group were drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of the prediction model. A calibration curve was plotted to assess the nomogram predictions vs the actual observations of the ALN metastasis rate and axillary tumor burden rate.
Results: The ALN metastasis rates of the training group and the validation group were 35.1% and 34.1%, respectively. Multivariate analysis showed that molecular subtype, lymphovascular invasion, mass descriptors (size, margin, microcalcification and blood flow signal) and LN descriptors (shape, cortical thickness and long-to-short ratio) were independent impact factors in early breast cancer. The AUC of ALN metastasis rate of prediction model based on US features was 0.802, the AUC of high tumor burden rate was 0.873, and the AUC of external validation group was 0.731 and 0.802, respectively. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual metastasis rate and the high tumor burden rate. The results showed that preoperative US had a sensitivity of 59.4% and a specificity of 88.9% for predicting the ALN metastasis rate.
Conclusion: The successfully established nomograms based on US characteristics to predict ALN metastasis rate and high axillary tumor burden rate in early breast cancer can achieve individual prediction. Compared with other nomogram predictions, it is more intuitive, and can help clinical decision-making; thus, it should be promoted. However, at this time US features alone are insufficient to replace sentinel lymph node biopsy.
Keywords: axillary lymph node involvement, early breast cancer, ultrasound, nomogram
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