Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes

This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1–2 positive SLNs were investigated. Univa...

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Veröffentlicht in:	Scientific reports Jg. 11; H. 1; S. 19972 - 11
Hauptverfasser:	Meng, Lei, Zheng, Ting, Wang, Yuanyuan, Li, Zhao, Xiao, Qi, He, Junfeng, Tan, Jinxiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 07.10.2021 Nature Publishing Group Nature Portfolio
Schlagworte:	631/67 631/67/1347 692/4028 692/4028/546 692/4028/67 Adult Aged Aged, 80 and over Area Under Curve Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - pathology China Decision Support Techniques ErbB-2 protein Female Humanities and Social Sciences Humans Logistic Models Lymph nodes Lymph Nodes - pathology Lymphatic Metastasis - diagnosis Lymphatic Metastasis - pathology Lymphatic system Metastases Metastasis Middle Aged multidisciplinary Patients Prediction models Reproducibility of Results Risk factors Science Science (multidisciplinary) Sentinel Lymph Node Biopsy Training Tumors China
ISSN:	2045-2322, 2045-2322
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Abstract	This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1–2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729–0.798) and 0.777 (95% CI 0.692–0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1–2 positive SLNs and make the final decisions regarding axillary lymph node dissection.
AbstractList	This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1–2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729–0.798) and 0.777 (95% CI 0.692–0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1–2 positive SLNs and make the final decisions regarding axillary lymph node dissection. This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1-2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729-0.798) and 0.777 (95% CI 0.692-0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1-2 positive SLNs and make the final decisions regarding axillary lymph node dissection.This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1-2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729-0.798) and 0.777 (95% CI 0.692-0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1-2 positive SLNs and make the final decisions regarding axillary lymph node dissection. Abstract This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1–2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729–0.798) and 0.777 (95% CI 0.692–0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1–2 positive SLNs and make the final decisions regarding axillary lymph node dissection.
ArticleNumber	19972
Author	Wang, Yuanyuan Meng, Lei Li, Zhao Xiao, Qi Tan, Jinxiang He, Junfeng Zheng, Ting
Author_xml	– sequence: 1 givenname: Lei surname: Meng fullname: Meng, Lei organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University – sequence: 2 givenname: Ting surname: Zheng fullname: Zheng, Ting organization: Chongqing Traditional Chinese Medicine Hospital – sequence: 3 givenname: Yuanyuan surname: Wang fullname: Wang, Yuanyuan organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University – sequence: 4 givenname: Zhao surname: Li fullname: Li, Zhao organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University – sequence: 5 givenname: Qi surname: Xiao fullname: Xiao, Qi organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University – sequence: 6 givenname: Junfeng surname: He fullname: He, Junfeng organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University – sequence: 7 givenname: Jinxiang surname: Tan fullname: Tan, Jinxiang email: tjx1202@163.com organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34620978$$D View this record in MEDLINE/PubMed
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Snippet	This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer... Abstract This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast...
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SubjectTerms	631/67 631/67/1347 692/4028 692/4028/546 692/4028/67 Adult Aged Aged, 80 and over Area Under Curve Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - pathology China Decision Support Techniques ErbB-2 protein Female Humanities and Social Sciences Humans Logistic Models Lymph nodes Lymph Nodes - pathology Lymphatic Metastasis - diagnosis Lymphatic Metastasis - pathology Lymphatic system Metastases Metastasis Middle Aged multidisciplinary Patients Prediction models Reproducibility of Results Risk factors Science Science (multidisciplinary) Sentinel Lymph Node Biopsy Training Tumors
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Title	Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes
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