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Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules

The objective of this research is to explore the value of whole-thyroid CT-based radiomics in predicting benign (noncancerous) and malignant thyroid nodules. The imaging and clinical data of 161 patients with thyroid nodules that were confirmed by pathology were retrospectively analyzed. The entire...

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Autores principales: Xu, Han, Wang, Ximing, Guan, Chaoqun, Tan, Ru, Yang, Qing, Zhang, Qi, Liu, Aie, Liu, Qingwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117640/
https://www.ncbi.nlm.nih.gov/pubmed/35600338
http://dx.doi.org/10.3389/fonc.2022.828259
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author Xu, Han
Wang, Ximing
Guan, Chaoqun
Tan, Ru
Yang, Qing
Zhang, Qi
Liu, Aie
Liu, Qingwei
author_facet Xu, Han
Wang, Ximing
Guan, Chaoqun
Tan, Ru
Yang, Qing
Zhang, Qi
Liu, Aie
Liu, Qingwei
author_sort Xu, Han
collection PubMed
description The objective of this research is to explore the value of whole-thyroid CT-based radiomics in predicting benign (noncancerous) and malignant thyroid nodules. The imaging and clinical data of 161 patients with thyroid nodules that were confirmed by pathology were retrospectively analyzed. The entire thyroid regions of interest (ROIs) were manually sketched for all 161 cases. After extracting CT radiomic features, the patients were divided into a training group (128 cases) and a test group (33 cases) according to the 4:1 ratio with stratified random sampling (fivefold cross validation). All the data were normalized by the maximum absolute value and screened using selection operator regression analysis and K best. The data generation model was trained by logistic regression. The effectiveness of the model in differentiating between benign and malignant thyroid nodules was validated by a receiver operating characteristic (ROC) curve. After data grouping, eigenvalue screening, and data training, the logistic regression model with the maximum absolute value normalized was constructed. For the training group, the area under the ROC curve (AUC) was 94.4% (95% confidence interval: 0.941–0.977); the sensitivity and specificity were 89.7% and 86.7%, respectively; and the diagnostic accuracy was 87.6%. For the test group, the AUC was 94.2% (95% confidence interval: 0.881–0.999); the sensitivity and specificity were 89.4% and 86.8%, respectively; and the diagnostic accuracy was 87.6%. The CT radiomic model of the entire thyroid gland is highly efficient in differentiating between benign and malignant thyroid nodules.
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spelling pubmed-91176402022-05-20 Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules Xu, Han Wang, Ximing Guan, Chaoqun Tan, Ru Yang, Qing Zhang, Qi Liu, Aie Liu, Qingwei Front Oncol Oncology The objective of this research is to explore the value of whole-thyroid CT-based radiomics in predicting benign (noncancerous) and malignant thyroid nodules. The imaging and clinical data of 161 patients with thyroid nodules that were confirmed by pathology were retrospectively analyzed. The entire thyroid regions of interest (ROIs) were manually sketched for all 161 cases. After extracting CT radiomic features, the patients were divided into a training group (128 cases) and a test group (33 cases) according to the 4:1 ratio with stratified random sampling (fivefold cross validation). All the data were normalized by the maximum absolute value and screened using selection operator regression analysis and K best. The data generation model was trained by logistic regression. The effectiveness of the model in differentiating between benign and malignant thyroid nodules was validated by a receiver operating characteristic (ROC) curve. After data grouping, eigenvalue screening, and data training, the logistic regression model with the maximum absolute value normalized was constructed. For the training group, the area under the ROC curve (AUC) was 94.4% (95% confidence interval: 0.941–0.977); the sensitivity and specificity were 89.7% and 86.7%, respectively; and the diagnostic accuracy was 87.6%. For the test group, the AUC was 94.2% (95% confidence interval: 0.881–0.999); the sensitivity and specificity were 89.4% and 86.8%, respectively; and the diagnostic accuracy was 87.6%. The CT radiomic model of the entire thyroid gland is highly efficient in differentiating between benign and malignant thyroid nodules. Frontiers Media S.A. 2022-05-05 /pmc/articles/PMC9117640/ /pubmed/35600338 http://dx.doi.org/10.3389/fonc.2022.828259 Text en Copyright © 2022 Xu, Wang, Guan, Tan, Yang, Zhang, Liu and Liu 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
Xu, Han
Wang, Ximing
Guan, Chaoqun
Tan, Ru
Yang, Qing
Zhang, Qi
Liu, Aie
Liu, Qingwei
Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules
title Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules
title_full Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules
title_fullStr Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules
title_full_unstemmed Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules
title_short Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules
title_sort value of whole-thyroid ct-based radiomics in predicting benign and malignant thyroid nodules
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117640/
https://www.ncbi.nlm.nih.gov/pubmed/35600338
http://dx.doi.org/10.3389/fonc.2022.828259
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