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Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer

BACKGROUND: Accurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to pr...

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Autores principales: Xiong, Lang, Chen, Haolin, Tang, Xiaofeng, Chen, Biyun, Jiang, Xinhua, Liu, Lizhi, Feng, Yanqiu, Liu, Longzhong, Li, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117589/
https://www.ncbi.nlm.nih.gov/pubmed/33996546
http://dx.doi.org/10.3389/fonc.2021.621993
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author Xiong, Lang
Chen, Haolin
Tang, Xiaofeng
Chen, Biyun
Jiang, Xinhua
Liu, Lizhi
Feng, Yanqiu
Liu, Longzhong
Li, Li
author_facet Xiong, Lang
Chen, Haolin
Tang, Xiaofeng
Chen, Biyun
Jiang, Xinhua
Liu, Lizhi
Feng, Yanqiu
Liu, Longzhong
Li, Li
author_sort Xiong, Lang
collection PubMed
description BACKGROUND: Accurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to predict disease-free survival (DFS) in patients with invasive breast cancer and assess its additional value to the clinicopathological predictors for individualized DFS prediction. METHODS: We identified 620 patients with invasive breast cancer and randomly divided them into the training (n = 372) and validation (n = 248) cohorts. A radiomics signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in the training cohort and validated in the validation cohort. Univariate and multivariate Cox proportional hazards model and Kaplan–Meier survival analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. To evaluate the additional value of the radiomics signature for DFS prediction, a radiomics nomogram combining the radiomics signature and clinicopathological predictors was constructed and assessed in terms of discrimination, calibration, reclassification, and clinical usefulness. RESULTS: The radiomics signature was significantly associated with DFS, independent of the clinicopathological predictors. The radiomics nomogram performed better than the clinicopathological nomogram (C-index, 0.796 vs. 0.761) and provided better calibration and positive net reclassification improvement (0.147, P = 0.035) in the validation cohort. Decision curve analysis also demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: US radiomics signature is a potential imaging biomarker for risk stratification of DFS in invasive breast cancer, and US-based radiomics nomogram improved accuracy of DFS prediction.
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spelling pubmed-81175892021-05-14 Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer Xiong, Lang Chen, Haolin Tang, Xiaofeng Chen, Biyun Jiang, Xinhua Liu, Lizhi Feng, Yanqiu Liu, Longzhong Li, Li Front Oncol Oncology BACKGROUND: Accurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to predict disease-free survival (DFS) in patients with invasive breast cancer and assess its additional value to the clinicopathological predictors for individualized DFS prediction. METHODS: We identified 620 patients with invasive breast cancer and randomly divided them into the training (n = 372) and validation (n = 248) cohorts. A radiomics signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in the training cohort and validated in the validation cohort. Univariate and multivariate Cox proportional hazards model and Kaplan–Meier survival analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. To evaluate the additional value of the radiomics signature for DFS prediction, a radiomics nomogram combining the radiomics signature and clinicopathological predictors was constructed and assessed in terms of discrimination, calibration, reclassification, and clinical usefulness. RESULTS: The radiomics signature was significantly associated with DFS, independent of the clinicopathological predictors. The radiomics nomogram performed better than the clinicopathological nomogram (C-index, 0.796 vs. 0.761) and provided better calibration and positive net reclassification improvement (0.147, P = 0.035) in the validation cohort. Decision curve analysis also demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: US radiomics signature is a potential imaging biomarker for risk stratification of DFS in invasive breast cancer, and US-based radiomics nomogram improved accuracy of DFS prediction. Frontiers Media S.A. 2021-04-29 /pmc/articles/PMC8117589/ /pubmed/33996546 http://dx.doi.org/10.3389/fonc.2021.621993 Text en Copyright © 2021 Xiong, Chen, Tang, Chen, Jiang, Liu, Feng, Liu and Li 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
Xiong, Lang
Chen, Haolin
Tang, Xiaofeng
Chen, Biyun
Jiang, Xinhua
Liu, Lizhi
Feng, Yanqiu
Liu, Longzhong
Li, Li
Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer
title Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer
title_full Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer
title_fullStr Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer
title_full_unstemmed Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer
title_short Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer
title_sort ultrasound-based radiomics analysis for predicting disease-free survival of invasive breast cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117589/
https://www.ncbi.nlm.nih.gov/pubmed/33996546
http://dx.doi.org/10.3389/fonc.2021.621993
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