Nomogram models for stratified prediction of axillary lymph node metastasis in breast cancer patients (cN0)

作者全名："Gao, Xin; Luo, Wenpei; He, Lingyun; Yang, Lu"

作者地址："[Gao, Xin; Luo, Wenpei; Yang, Lu] Chongqing Med Univ, Affiliated Hosp 2, Dept Breast & Thyroid Surg, Chongqing, Peoples R China; [He, Lingyun] Chongqing Hlth Ctr Women & Children, Sci Res & Educ Sect, Chongqing, Peoples R China"

通信作者："Yang, L (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Breast & Thyroid Surg, Chongqing, Peoples R China."

来源：FRONTIERS IN ENDOCRINOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000853263900001

JCR分区：Q2

影响因子：5.2

年份：2022

卷号：13

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：breast cancer; axillary lymph node metastasis; predictor; nomogram model; stratified prediction

摘要："ObjectivesTo determine the predictors of axillary lymph node metastasis (ALNM), two nomogram models were constructed to accurately predict the status of axillary lymph nodes (ALNs), mainly high nodal tumour burden (HNTB, > 2 positive lymph nodes), low nodal tumour burden (LNTB, 1-2 positive lymph nodes) and negative ALNM (N0). Accordingly, more appropriate treatment strategies for breast cancer patients without clinical ALNM (cN0) could be selected. MethodsFrom 2010 to 2015, a total of 6314 patients with invasive breast cancer (cN0) were diagnosed in the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and internal validation groups at a ratio of 3:1. As the external validation group, data from 503 breast cancer patients (cN0) who underwent axillary lymph node dissection (ALND) at the Second Affiliated Hospital of Chongqing Medical University between January 2011 and December 2020 were collected. The predictive factors determined by univariate and multivariate logistic regression analyses were used to construct the nomograms. Receiver operating characteristic (ROC) curves and calibration plots were used to assess the prediction models' discrimination and calibration. ResultsUnivariate analysis and multivariate logistic regression analyses showed that tumour size, primary site, molecular subtype and grade were independent predictors of both ALNM and HNTB. Moreover, histologic type and age were independent predictors of ALNM and HNTB, respectively. Integrating these independent predictors, two nomograms were successfully developed to accurately predict the status of ALN. For nomogram 1 (prediction of ALNM), the areas under the receiver operating characteristic (ROC) curve in the training, internal validation and external validation groups were 0.715, 0.688 and 0.876, respectively. For nomogram 2 (prediction of HNTB), the areas under the ROC curve in the training, internal validation and external validation groups were 0.842, 0.823 and 0.862. The above results showed a satisfactory performance. ConclusionWe established two nomogram models to predict the status of ALNs (N0, 1-2 positive ALNs or >2 positive ALNs) for breast cancer patients (cN0). They were well verified in further internal and external groups. The nomograms can help doctors make more accurate treatment plans, and avoid unnecessary surgical trauma."

基金机构："Chongqing Natural Science Foundation; Science and Technology Bureau of Yuzhong District, Chongqing; Kuanren Talents Program of the second affiliated hospital of Chongqing Medical University; [cstc2020jcyj-msxmX2011]; [20200145]"

基金资助正文："Funding This study was supported by Chongqing Natural Science Foundation (grant no. cstc2020jcyj-msxmX2011), Science and Technology Bureau of Yuzhong District, Chongqing (grant no. 20200145), and Kuanren Talents Program of the second affiliated hospital of Chongqing Medical University."