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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Published in:Scientific reports Vol. 11; no. 1; pp. 19972 - 11
Main Authors: Meng, Lei, Zheng, Ting, Wang, Yuanyuan, Li, Zhao, Xiao, Qi, He, Junfeng, Tan, Jinxiang
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 07.10.2021
Nature Publishing Group
Nature Portfolio
Subjects:
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
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  givenname: Yuanyuan
  surname: Wang
  fullname: Wang, Yuanyuan
  organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University
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  givenname: Zhao
  surname: Li
  fullname: Li, Zhao
  organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University
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  givenname: Qi
  surname: Xiao
  fullname: Xiao, Qi
  organization: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University
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  surname: He
  fullname: He, Junfeng
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  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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SSID ssj0000529419
Score 2.457301
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...
SourceID doaj
pubmedcentral
proquest
pubmed
crossref
springer
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 19972
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
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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
URI https://link.springer.com/article/10.1038/s41598-021-99522-3
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Volume 11
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