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Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy

BACKGROUND: Sentinel lymph node (SLN) biopsy is feasible for breast cancer (BC) patients with clinically negative axillary lymph nodes; however, complications develop in some patients after surgery, although SLN metastasis is rarely found. Previous predictive models contained parameters that relied...

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Autores principales: Hu, Xi’E, Xue, Jingyi, Peng, Shujia, Yang, Ping, Yang, Zhenyu, Yang, Lin, Dong, Yanming, Yuan, Lijuan, Wang, Ting, Bao, Guoqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8107679/
https://www.ncbi.nlm.nih.gov/pubmed/33981613
http://dx.doi.org/10.3389/fonc.2021.665240
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author Hu, Xi’E
Xue, Jingyi
Peng, Shujia
Yang, Ping
Yang, Zhenyu
Yang, Lin
Dong, Yanming
Yuan, Lijuan
Wang, Ting
Bao, Guoqiang
author_facet Hu, Xi’E
Xue, Jingyi
Peng, Shujia
Yang, Ping
Yang, Zhenyu
Yang, Lin
Dong, Yanming
Yuan, Lijuan
Wang, Ting
Bao, Guoqiang
author_sort Hu, Xi’E
collection PubMed
description BACKGROUND: Sentinel lymph node (SLN) biopsy is feasible for breast cancer (BC) patients with clinically negative axillary lymph nodes; however, complications develop in some patients after surgery, although SLN metastasis is rarely found. Previous predictive models contained parameters that relied on postoperative data, thus limiting their application in the preoperative setting. Therefore, it is necessary to find a new model for preoperative risk prediction for SLN metastasis to help clinicians facilitate individualized clinical decisions. MATERIALS AND METHODS: BC patients who underwent SLN biopsy in two different institutions were included in the training and validation cohorts. Demographic characteristics, preoperative tumor pathological features, and ultrasound findings were evaluated. Multivariate logistic regression was used to develop the nomogram. The discrimination, accuracy, and clinical usefulness of the nomogram were assessed using Harrell’s C-statistic and ROC analysis, the calibration curve, and the decision curve analysis, respectively. RESULTS: A total of 624 patients who met the inclusion criteria were enrolled, including 444 in the training cohort and 180 in the validation cohort. Young age, high BMI, high Ki67, large tumor size, indistinct tumor margins, calcifications, and an aspect ratio ≥1 were independent predictive factors for SLN metastasis of BC. Incorporating these parameters, the nomogram achieved a robust predictive performance with a C-index and accuracy of 0.92 and 0.85, and 0.82 and 0.80 in the training and validation cohorts, respectively. The calibration curves also fit well, and the decision curve analysis revealed that the nomogram was clinically useful. CONCLUSIONS: We established a nomogram to preoperatively predict the risk of SLN metastasis in BC patients, providing a non-invasive approach in clinical practice and serving as a potential tool to identify BC patients who may omit unnecessary SLN biopsy.
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spelling pubmed-81076792021-05-11 Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy Hu, Xi’E Xue, Jingyi Peng, Shujia Yang, Ping Yang, Zhenyu Yang, Lin Dong, Yanming Yuan, Lijuan Wang, Ting Bao, Guoqiang Front Oncol Oncology BACKGROUND: Sentinel lymph node (SLN) biopsy is feasible for breast cancer (BC) patients with clinically negative axillary lymph nodes; however, complications develop in some patients after surgery, although SLN metastasis is rarely found. Previous predictive models contained parameters that relied on postoperative data, thus limiting their application in the preoperative setting. Therefore, it is necessary to find a new model for preoperative risk prediction for SLN metastasis to help clinicians facilitate individualized clinical decisions. MATERIALS AND METHODS: BC patients who underwent SLN biopsy in two different institutions were included in the training and validation cohorts. Demographic characteristics, preoperative tumor pathological features, and ultrasound findings were evaluated. Multivariate logistic regression was used to develop the nomogram. The discrimination, accuracy, and clinical usefulness of the nomogram were assessed using Harrell’s C-statistic and ROC analysis, the calibration curve, and the decision curve analysis, respectively. RESULTS: A total of 624 patients who met the inclusion criteria were enrolled, including 444 in the training cohort and 180 in the validation cohort. Young age, high BMI, high Ki67, large tumor size, indistinct tumor margins, calcifications, and an aspect ratio ≥1 were independent predictive factors for SLN metastasis of BC. Incorporating these parameters, the nomogram achieved a robust predictive performance with a C-index and accuracy of 0.92 and 0.85, and 0.82 and 0.80 in the training and validation cohorts, respectively. The calibration curves also fit well, and the decision curve analysis revealed that the nomogram was clinically useful. CONCLUSIONS: We established a nomogram to preoperatively predict the risk of SLN metastasis in BC patients, providing a non-invasive approach in clinical practice and serving as a potential tool to identify BC patients who may omit unnecessary SLN biopsy. Frontiers Media S.A. 2021-04-26 /pmc/articles/PMC8107679/ /pubmed/33981613 http://dx.doi.org/10.3389/fonc.2021.665240 Text en Copyright © 2021 Hu, Xue, Peng, Yang, Yang, Yang, Dong, Yuan, Wang and Bao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Hu, Xi’E
Xue, Jingyi
Peng, Shujia
Yang, Ping
Yang, Zhenyu
Yang, Lin
Dong, Yanming
Yuan, Lijuan
Wang, Ting
Bao, Guoqiang
Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy
title Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy
title_full Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy
title_fullStr Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy
title_full_unstemmed Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy
title_short Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy
title_sort preoperative nomogram for predicting sentinel lymph node metastasis risk in breast cancer: a potential application on omitting sentinel lymph node biopsy
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8107679/
https://www.ncbi.nlm.nih.gov/pubmed/33981613
http://dx.doi.org/10.3389/fonc.2021.665240
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