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MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC
PURPOSE: To identify MRI-based radiomics signature (Rad-score) as a biomarker of risk stratification for disease-free survival (DFS) in patients with HER2-positive invasive breast cancer treated with trastuzumab-based neoadjuvant chemotherapy (NAC) and establish a radiomics-clinicoradiologic-based n...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602910/ https://www.ncbi.nlm.nih.gov/pubmed/33149669 http://dx.doi.org/10.2147/CMAR.S271876 |
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author | Li, Qin Xiao, Qin Li, Jianwei Duan, Shaofeng Wang, He Gu, Yajia |
author_facet | Li, Qin Xiao, Qin Li, Jianwei Duan, Shaofeng Wang, He Gu, Yajia |
author_sort | Li, Qin |
collection | PubMed |
description | PURPOSE: To identify MRI-based radiomics signature (Rad-score) as a biomarker of risk stratification for disease-free survival (DFS) in patients with HER2-positive invasive breast cancer treated with trastuzumab-based neoadjuvant chemotherapy (NAC) and establish a radiomics-clinicoradiologic-based nomogram that combines Rad-score, MRI findings, and clinicopathological variables for DFS estimation. PATIENTS AND METHODS: A total of 127 patients were divided into a training set and testing set according to the ratio of 7:3. Radiomic features were extracted from multiphase CE-MRI (CE(m)). Rad-score was calculated using the LASSO (least absolute shrinkage and selection operator) regression analysis. The cutoff point of Rad-score to divide the patients into high- and low-risk groups was determined by receiver operating characteristic curve analysis. A Kaplan–Meier survival curves and the Log rank test were used to investigate the association of the Rad-score with DFS. Univariate and multivariate Cox proportional hazards model were used to determine the association of Rad-score, MRI features, and clinicopathological variables with DFS. A radiomics-clinicoradiologic-based nomogram combining the Rad-score, MRI features, and clinicopathological findings was plotted to validate the radiomic signatures for DFS estimation. RESULTS: The Rad-score stratified patients into high- and low-risk groups for DFS in the training set (P<0.0001) and was validated in the testing set (P=0.002). The radiomics-clinicoradiologic-based nomogram estimated DFS (training set: C-index=0.974, 95% confidence interval (CI)=0.954–0.994; testing set: C-index=0.917, 95% CI=0.842–0.991) better than the clinicoradiologic-based nomogram (training set: C-index=0.855, 95% CI=0.739–0.971; testing set: C-index=0.831, 95% CI=0.643–0.999). CONCLUSION: The Rad-score is an independent biomarker for the estimation of DFS in invasive HER2-positive breast cancer with NAC and the radiomics-clinicoradiologic-based nomogram improved individualized DFS estimation. |
format | Online Article Text |
id | pubmed-7602910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-76029102020-11-03 MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC Li, Qin Xiao, Qin Li, Jianwei Duan, Shaofeng Wang, He Gu, Yajia Cancer Manag Res Original Research PURPOSE: To identify MRI-based radiomics signature (Rad-score) as a biomarker of risk stratification for disease-free survival (DFS) in patients with HER2-positive invasive breast cancer treated with trastuzumab-based neoadjuvant chemotherapy (NAC) and establish a radiomics-clinicoradiologic-based nomogram that combines Rad-score, MRI findings, and clinicopathological variables for DFS estimation. PATIENTS AND METHODS: A total of 127 patients were divided into a training set and testing set according to the ratio of 7:3. Radiomic features were extracted from multiphase CE-MRI (CE(m)). Rad-score was calculated using the LASSO (least absolute shrinkage and selection operator) regression analysis. The cutoff point of Rad-score to divide the patients into high- and low-risk groups was determined by receiver operating characteristic curve analysis. A Kaplan–Meier survival curves and the Log rank test were used to investigate the association of the Rad-score with DFS. Univariate and multivariate Cox proportional hazards model were used to determine the association of Rad-score, MRI features, and clinicopathological variables with DFS. A radiomics-clinicoradiologic-based nomogram combining the Rad-score, MRI features, and clinicopathological findings was plotted to validate the radiomic signatures for DFS estimation. RESULTS: The Rad-score stratified patients into high- and low-risk groups for DFS in the training set (P<0.0001) and was validated in the testing set (P=0.002). The radiomics-clinicoradiologic-based nomogram estimated DFS (training set: C-index=0.974, 95% confidence interval (CI)=0.954–0.994; testing set: C-index=0.917, 95% CI=0.842–0.991) better than the clinicoradiologic-based nomogram (training set: C-index=0.855, 95% CI=0.739–0.971; testing set: C-index=0.831, 95% CI=0.643–0.999). CONCLUSION: The Rad-score is an independent biomarker for the estimation of DFS in invasive HER2-positive breast cancer with NAC and the radiomics-clinicoradiologic-based nomogram improved individualized DFS estimation. Dove 2020-10-27 /pmc/articles/PMC7602910/ /pubmed/33149669 http://dx.doi.org/10.2147/CMAR.S271876 Text en © 2020 Li et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Li, Qin Xiao, Qin Li, Jianwei Duan, Shaofeng Wang, He Gu, Yajia MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC |
title | MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC |
title_full | MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC |
title_fullStr | MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC |
title_full_unstemmed | MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC |
title_short | MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC |
title_sort | mri-based radiomic signature as a prognostic biomarker for her2-positive invasive breast cancer treated with nac |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602910/ https://www.ncbi.nlm.nih.gov/pubmed/33149669 http://dx.doi.org/10.2147/CMAR.S271876 |
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