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Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors

BACKGROUND: Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide...

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Autores principales: Zhang, Jian, Xiao, Linhai, Pu, Shengyu, Liu, Yang, He, Jianjun, Wang, Ke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043913/
https://www.ncbi.nlm.nih.gov/pubmed/33095360
http://dx.doi.org/10.1245/s10434-020-09214-x
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author Zhang, Jian
Xiao, Linhai
Pu, Shengyu
Liu, Yang
He, Jianjun
Wang, Ke
author_facet Zhang, Jian
Xiao, Linhai
Pu, Shengyu
Liu, Yang
He, Jianjun
Wang, Ke
author_sort Zhang, Jian
collection PubMed
description BACKGROUND: Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice. OBJECTIVE: The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables. METHODS: Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value. RESULTS: The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646. CONCLUSION: We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery.
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spelling pubmed-80439132021-04-27 Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors Zhang, Jian Xiao, Linhai Pu, Shengyu Liu, Yang He, Jianjun Wang, Ke Ann Surg Oncol Breast Oncology BACKGROUND: Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice. OBJECTIVE: The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables. METHODS: Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value. RESULTS: The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646. CONCLUSION: We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery. Springer International Publishing 2020-10-23 2021 /pmc/articles/PMC8043913/ /pubmed/33095360 http://dx.doi.org/10.1245/s10434-020-09214-x Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Breast Oncology
Zhang, Jian
Xiao, Linhai
Pu, Shengyu
Liu, Yang
He, Jianjun
Wang, Ke
Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors
title Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors
title_full Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors
title_fullStr Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors
title_full_unstemmed Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors
title_short Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors
title_sort can we reliably identify the pathological outcomes of neoadjuvant chemotherapy in patients with breast cancer? development and validation of a logistic regression nomogram based on preoperative factors
topic Breast Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043913/
https://www.ncbi.nlm.nih.gov/pubmed/33095360
http://dx.doi.org/10.1245/s10434-020-09214-x
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