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Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto’s thyroiditis
BACKGROUND: Previous models for differentiating benign and malignant thyroid nodules(TN) have predominantly focused on the characteristics of the nodules themselves, without considering the specific features of the thyroid gland(TG) in patients with Hashimoto’s thyroiditis(HT). In this study, we ana...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627229/ https://www.ncbi.nlm.nih.gov/pubmed/37937055 http://dx.doi.org/10.3389/fendo.2023.1267886 |
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author | Fang, Mengyuan Lei, Mengjie Chen, Xuexue Cao, Hong Duan, Xingxing Yuan, Hongxia Guo, Lili |
author_facet | Fang, Mengyuan Lei, Mengjie Chen, Xuexue Cao, Hong Duan, Xingxing Yuan, Hongxia Guo, Lili |
author_sort | Fang, Mengyuan |
collection | PubMed |
description | BACKGROUND: Previous models for differentiating benign and malignant thyroid nodules(TN) have predominantly focused on the characteristics of the nodules themselves, without considering the specific features of the thyroid gland(TG) in patients with Hashimoto’s thyroiditis(HT). In this study, we analyzed the clinical and ultrasound radiomics(USR) features of TN in patients with HT and constructed a model for differentiating benign and malignant nodules specifically in this population. METHODS: We retrospectively collected clinical and ultrasound data from 227 patients with TN and concomitant HT(161 for training, 66 for testing). Two experienced sonographers delineated the TG and TN regions, and USR features were extracted using Python. Lasso regression and logistic analysis were employed to select relevant USR features and clinical data to construct the model for differentiating benign and malignant TN. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis(DCA). RESULTS: A total of 1,162 USR features were extracted from TN and the TG in the 227 patients with HT. Lasso regression identified 14 features, which were used to construct the TN score, TG score, and TN+TG score. Univariate analysis identified six clinical predictors: TI-RADS, echoic type, aspect ratio, boundary, calcification, and thyroid function. Multivariable analysis revealed that incorporating USR scores improved the performance of the model for differentiating benign and malignant TN in patients with HT. Specifically, the TN+TG score resulted in the highest increase in AUC(from 0.83 to 0.94) in the clinical prediction model. Calibration curves and DCA demonstrated higher accuracy and net benefit for the TN+TG+clinical model. CONCLUSION: USR features of both the TG and TN can be utilized for differentiating benign and malignant TN in patients with HT. These findings highlight the importance of considering the entire TG in the evaluation of TN in HT patients, providing valuable insights for clinical decision-making in this population. |
format | Online Article Text |
id | pubmed-10627229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106272292023-11-07 Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto’s thyroiditis Fang, Mengyuan Lei, Mengjie Chen, Xuexue Cao, Hong Duan, Xingxing Yuan, Hongxia Guo, Lili Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Previous models for differentiating benign and malignant thyroid nodules(TN) have predominantly focused on the characteristics of the nodules themselves, without considering the specific features of the thyroid gland(TG) in patients with Hashimoto’s thyroiditis(HT). In this study, we analyzed the clinical and ultrasound radiomics(USR) features of TN in patients with HT and constructed a model for differentiating benign and malignant nodules specifically in this population. METHODS: We retrospectively collected clinical and ultrasound data from 227 patients with TN and concomitant HT(161 for training, 66 for testing). Two experienced sonographers delineated the TG and TN regions, and USR features were extracted using Python. Lasso regression and logistic analysis were employed to select relevant USR features and clinical data to construct the model for differentiating benign and malignant TN. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis(DCA). RESULTS: A total of 1,162 USR features were extracted from TN and the TG in the 227 patients with HT. Lasso regression identified 14 features, which were used to construct the TN score, TG score, and TN+TG score. Univariate analysis identified six clinical predictors: TI-RADS, echoic type, aspect ratio, boundary, calcification, and thyroid function. Multivariable analysis revealed that incorporating USR scores improved the performance of the model for differentiating benign and malignant TN in patients with HT. Specifically, the TN+TG score resulted in the highest increase in AUC(from 0.83 to 0.94) in the clinical prediction model. Calibration curves and DCA demonstrated higher accuracy and net benefit for the TN+TG+clinical model. CONCLUSION: USR features of both the TG and TN can be utilized for differentiating benign and malignant TN in patients with HT. These findings highlight the importance of considering the entire TG in the evaluation of TN in HT patients, providing valuable insights for clinical decision-making in this population. Frontiers Media S.A. 2023-10-23 /pmc/articles/PMC10627229/ /pubmed/37937055 http://dx.doi.org/10.3389/fendo.2023.1267886 Text en Copyright © 2023 Fang, Lei, Chen, Cao, Duan, Yuan and Guo 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 | Endocrinology Fang, Mengyuan Lei, Mengjie Chen, Xuexue Cao, Hong Duan, Xingxing Yuan, Hongxia Guo, Lili Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto’s thyroiditis |
title | Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto’s thyroiditis |
title_full | Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto’s thyroiditis |
title_fullStr | Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto’s thyroiditis |
title_full_unstemmed | Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto’s thyroiditis |
title_short | Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto’s thyroiditis |
title_sort | radiomics-based ultrasound models for thyroid nodule differentiation in hashimoto’s thyroiditis |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627229/ https://www.ncbi.nlm.nih.gov/pubmed/37937055 http://dx.doi.org/10.3389/fendo.2023.1267886 |
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