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Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma

INTRODUCTION: The molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperat...

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Autores principales: Wu, Jiangfeng, Ge, Lifang, Jin, Yun, Wang, Yunlai, Hu, Liyan, Xu, Dong, Wang, Zhengping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749858/
https://www.ncbi.nlm.nih.gov/pubmed/36530981
http://dx.doi.org/10.3389/fonc.2022.993466
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author Wu, Jiangfeng
Ge, Lifang
Jin, Yun
Wang, Yunlai
Hu, Liyan
Xu, Dong
Wang, Zhengping
author_facet Wu, Jiangfeng
Ge, Lifang
Jin, Yun
Wang, Yunlai
Hu, Liyan
Xu, Dong
Wang, Zhengping
author_sort Wu, Jiangfeng
collection PubMed
description INTRODUCTION: The molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC. METHODS: A total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis. RESULTS: In all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram. CONCLUSION: Both the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC.
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spelling pubmed-97498582022-12-15 Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma Wu, Jiangfeng Ge, Lifang Jin, Yun Wang, Yunlai Hu, Liyan Xu, Dong Wang, Zhengping Front Oncol Oncology INTRODUCTION: The molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC. METHODS: A total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis. RESULTS: In all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram. CONCLUSION: Both the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC. Frontiers Media S.A. 2022-11-28 /pmc/articles/PMC9749858/ /pubmed/36530981 http://dx.doi.org/10.3389/fonc.2022.993466 Text en Copyright © 2022 Wu, Ge, Jin, Wang, Hu, Xu and Wang 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
Wu, Jiangfeng
Ge, Lifang
Jin, Yun
Wang, Yunlai
Hu, Liyan
Xu, Dong
Wang, Zhengping
Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma
title Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma
title_full Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma
title_fullStr Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma
title_full_unstemmed Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma
title_short Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma
title_sort development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749858/
https://www.ncbi.nlm.nih.gov/pubmed/36530981
http://dx.doi.org/10.3389/fonc.2022.993466
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