Cargando…

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy

Purpose: While a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomog...

Descripción completa

Detalles Bibliográficos
Autores principales: Lan, Ailin, Li, Han, Chen, Junru, Shen, Meiying, Jin, Yudi, Dai, Yuran, Jiang, Linshan, Dai, Xin, Peng, Yang, Liu, Shengchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965597/
https://www.ncbi.nlm.nih.gov/pubmed/36836483
http://dx.doi.org/10.3390/jpm13020249
_version_ 1784896804571054080
author Lan, Ailin
Li, Han
Chen, Junru
Shen, Meiying
Jin, Yudi
Dai, Yuran
Jiang, Linshan
Dai, Xin
Peng, Yang
Liu, Shengchun
author_facet Lan, Ailin
Li, Han
Chen, Junru
Shen, Meiying
Jin, Yudi
Dai, Yuran
Jiang, Linshan
Dai, Xin
Peng, Yang
Liu, Shengchun
author_sort Lan, Ailin
collection PubMed
description Purpose: While a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients. Methods: A retrospective analysis of 607 non-pCR BC patients was conducted (2012–2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, ac-curacy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan–Meier method. Results: Both pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all p < 0.05), showing good discrimination and calibration in both internal and external validation. We also assessed the performance of the two models in four subtypes, with the tri-ple-negative subtype showing the best prediction. Patients in the high-risk to high-risk subgroup have significantly poorer survival rates (p < 0.0001). Conclusion: Two robust and effective nomo-grams were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC.
format Online
Article
Text
id pubmed-9965597
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99655972023-02-26 Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy Lan, Ailin Li, Han Chen, Junru Shen, Meiying Jin, Yudi Dai, Yuran Jiang, Linshan Dai, Xin Peng, Yang Liu, Shengchun J Pers Med Article Purpose: While a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients. Methods: A retrospective analysis of 607 non-pCR BC patients was conducted (2012–2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, ac-curacy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan–Meier method. Results: Both pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all p < 0.05), showing good discrimination and calibration in both internal and external validation. We also assessed the performance of the two models in four subtypes, with the tri-ple-negative subtype showing the best prediction. Patients in the high-risk to high-risk subgroup have significantly poorer survival rates (p < 0.0001). Conclusion: Two robust and effective nomo-grams were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC. MDPI 2023-01-29 /pmc/articles/PMC9965597/ /pubmed/36836483 http://dx.doi.org/10.3390/jpm13020249 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lan, Ailin
Li, Han
Chen, Junru
Shen, Meiying
Jin, Yudi
Dai, Yuran
Jiang, Linshan
Dai, Xin
Peng, Yang
Liu, Shengchun
Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy
title Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy
title_full Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy
title_fullStr Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy
title_full_unstemmed Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy
title_short Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy
title_sort nomograms for predicting disease-free survival based on core needle biopsy and surgical specimens in female breast cancer patients with non-pathological complete response to neoadjuvant chemotherapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965597/
https://www.ncbi.nlm.nih.gov/pubmed/36836483
http://dx.doi.org/10.3390/jpm13020249
work_keys_str_mv AT lanailin nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT lihan nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT chenjunru nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT shenmeiying nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT jinyudi nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT daiyuran nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT jianglinshan nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT daixin nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT pengyang nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy
AT liushengchun nomogramsforpredictingdiseasefreesurvivalbasedoncoreneedlebiopsyandsurgicalspecimensinfemalebreastcancerpatientswithnonpathologicalcompleteresponsetoneoadjuvantchemotherapy