Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer

Authors

  • Xiaomin Wang Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, Hunan, China; Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, Hunan, China; Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Changsha, Hunan, China https://orcid.org/0000-0001-5215-4050
  • Xiaoping Yi National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, Hunan, China; Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, Hunan, China; Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Changsha, Hunan, China; Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
  • Qian Zhang Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
  • Xiaoxiao Wang Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
  • Hanghao Zhang Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
  • Shuai Peng Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
  • Kuansong Wang Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Department of Pathology, School of Basic Medical Science, Central South University, Changsha, Hunan, China
  • Liqiu Liao Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China

DOI:

https://doi.org/10.17305/bb.2022.8564

Keywords:

Predictive model, axillary lymph node metastasis, breast cancer

Abstract

Models for predicting axillary lymph node metastasis (ALNM) in breast cancer patients are lacking. We aimed to develop an efficient model to accurately predict ALNM. Three hundred fifty-five breast cancer patients were recruited and randomly divided into the training and validation sets. Univariate and multivariate logistic regressions were applied to identify predictors of ALNM. We developed nomograms based on these variables to predict ALNM. The performance of the nomograms was tested using the receiver operating characteristic curve and calibration curve, and a decision curve analysis was performed to assess the clinical utility of the prediction models. The nomograms that included clinical N stage (cN), pathological grade (pathGrade), and hemoglobin accurately predicted ALNM in the training and validation sets (area under the curve [AUC] 0.80 and 0.80, respectively). We then explored the importance of the cN and pathGradesignatures used in the integrated model and developed new nomograms by removing the two variables. The results suggested that the combine-pathGrade nomogram also accurately predicted ALNM in the training and validation sets (AUC 0.78 and 0.78, respectively), but the combine-cN nomogram did not (AUC 0.64 and 0.60, in training and validation sets, respectively). We described a cN-based ALNM prediction model in breast cancer patients, presenting a novel efficient clinical decision nomogram for predicting ALNM.

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Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer

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Published

2023-01-13

How to Cite

1.
Wang X, Yi X, Zhang Q, Wang X, Zhang H, Peng S, Wang K, Liao L. Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer. Biomol Biomed [Internet]. 2023Jan.13 [cited 2023Feb.1];. Available from: https://www.bjbms.org/ojs/index.php/bjbms/article/view/8564

Issue

Section

Translational and Clinical Research