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Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram

BACKGROUND AND AIMS: Prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer is critical for surgical planning and evaluation of NAC efficacy. The purpose of this project was to assess the efficiency of a novel nomogram based on ultrasound and clinic...

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Autores principales: Cui, Hao, Zhao, Dantong, Han, Peng, Zhang, Xudong, Fan, Wei, Zuo, Xiaoxuan, Wang, Panting, Hu, Nana, Kong, Hanqing, Peng, Fuhui, Wang, Ying, Tian, Jiawei, Zhang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650158/
https://www.ncbi.nlm.nih.gov/pubmed/34888231
http://dx.doi.org/10.3389/fonc.2021.718531
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author Cui, Hao
Zhao, Dantong
Han, Peng
Zhang, Xudong
Fan, Wei
Zuo, Xiaoxuan
Wang, Panting
Hu, Nana
Kong, Hanqing
Peng, Fuhui
Wang, Ying
Tian, Jiawei
Zhang, Lei
author_facet Cui, Hao
Zhao, Dantong
Han, Peng
Zhang, Xudong
Fan, Wei
Zuo, Xiaoxuan
Wang, Panting
Hu, Nana
Kong, Hanqing
Peng, Fuhui
Wang, Ying
Tian, Jiawei
Zhang, Lei
author_sort Cui, Hao
collection PubMed
description BACKGROUND AND AIMS: Prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer is critical for surgical planning and evaluation of NAC efficacy. The purpose of this project was to assess the efficiency of a novel nomogram based on ultrasound and clinicopathological features for predicting pCR after NAC. METHODS: This retrospective study included 282 patients with advanced breast cancer treated with NAC from two centers. Patients received breast ultrasound before NAC and after two cycles of NAC; and the ultrasound, clinicopathological features and feature changes after two cycles of NAC were recorded. A multivariate logistic regression model was combined with bootstrapping screened for informative features associated with pCR. Then, we constructed two nomograms: an initial-baseline nomogram and a two-cycle response nomogram. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were analyzed. The C-index was used to evaluate predictive accuracy. RESULTS: Sixty (60/282, 21.28%) patients achieved pCR. Triple-negative breast cancer (TNBC) and HER2-amplified types were more likely to obtain pCR. Size shrinkage, posterior acoustic pattern, and elasticity score were identified as independent factors by multivariate logistic regression. In the validation cohort, the two-cycle response nomogram showed better discrimination than the initial-baseline nomogram, with the C-index reaching 0.79. The sensitivity, specificity, and NPV of the two-cycle response nomogram were 0.77, 0.77, and 0.92, respectively. CONCLUSION: The two-cycle response nomogram exhibited satisfactory efficiency, which means that the nomogram was a reliable method to predict pCR after NAC. Size shrinkage after two cycles of NAC was an important in dependent factor in predicting pCR.
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spelling pubmed-86501582021-12-08 Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram Cui, Hao Zhao, Dantong Han, Peng Zhang, Xudong Fan, Wei Zuo, Xiaoxuan Wang, Panting Hu, Nana Kong, Hanqing Peng, Fuhui Wang, Ying Tian, Jiawei Zhang, Lei Front Oncol Oncology BACKGROUND AND AIMS: Prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer is critical for surgical planning and evaluation of NAC efficacy. The purpose of this project was to assess the efficiency of a novel nomogram based on ultrasound and clinicopathological features for predicting pCR after NAC. METHODS: This retrospective study included 282 patients with advanced breast cancer treated with NAC from two centers. Patients received breast ultrasound before NAC and after two cycles of NAC; and the ultrasound, clinicopathological features and feature changes after two cycles of NAC were recorded. A multivariate logistic regression model was combined with bootstrapping screened for informative features associated with pCR. Then, we constructed two nomograms: an initial-baseline nomogram and a two-cycle response nomogram. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were analyzed. The C-index was used to evaluate predictive accuracy. RESULTS: Sixty (60/282, 21.28%) patients achieved pCR. Triple-negative breast cancer (TNBC) and HER2-amplified types were more likely to obtain pCR. Size shrinkage, posterior acoustic pattern, and elasticity score were identified as independent factors by multivariate logistic regression. In the validation cohort, the two-cycle response nomogram showed better discrimination than the initial-baseline nomogram, with the C-index reaching 0.79. The sensitivity, specificity, and NPV of the two-cycle response nomogram were 0.77, 0.77, and 0.92, respectively. CONCLUSION: The two-cycle response nomogram exhibited satisfactory efficiency, which means that the nomogram was a reliable method to predict pCR after NAC. Size shrinkage after two cycles of NAC was an important in dependent factor in predicting pCR. Frontiers Media S.A. 2021-11-23 /pmc/articles/PMC8650158/ /pubmed/34888231 http://dx.doi.org/10.3389/fonc.2021.718531 Text en Copyright © 2021 Cui, Zhao, Han, Zhang, Fan, Zuo, Wang, Hu, Kong, Peng, Wang, Tian and Zhang 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
Cui, Hao
Zhao, Dantong
Han, Peng
Zhang, Xudong
Fan, Wei
Zuo, Xiaoxuan
Wang, Panting
Hu, Nana
Kong, Hanqing
Peng, Fuhui
Wang, Ying
Tian, Jiawei
Zhang, Lei
Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram
title Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram
title_full Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram
title_fullStr Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram
title_full_unstemmed Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram
title_short Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram
title_sort predicting pathological complete response after neoadjuvant chemotherapy in advanced breast cancer by ultrasound and clinicopathological features using a nomogram
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650158/
https://www.ncbi.nlm.nih.gov/pubmed/34888231
http://dx.doi.org/10.3389/fonc.2021.718531
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