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Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer

PURPOSE: To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). METHODS: This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n...

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Autores principales: Lu, Wen-Jie, Mao, Lin, Li, Jin, OuYang, Liang-Yan, Chen, Jia-Yao, Chen, Shi-Yan, Lin, Yun-Yong, Wu, Yi-Wen, Chen, Shao-Na, Qiu, Shao-Dong, Chen, Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482087/
https://www.ncbi.nlm.nih.gov/pubmed/37681026
http://dx.doi.org/10.3389/fonc.2023.1046951
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author Lu, Wen-Jie
Mao, Lin
Li, Jin
OuYang, Liang-Yan
Chen, Jia-Yao
Chen, Shi-Yan
Lin, Yun-Yong
Wu, Yi-Wen
Chen, Shao-Na
Qiu, Shao-Dong
Chen, Fei
author_facet Lu, Wen-Jie
Mao, Lin
Li, Jin
OuYang, Liang-Yan
Chen, Jia-Yao
Chen, Shi-Yan
Lin, Yun-Yong
Wu, Yi-Wen
Chen, Shao-Na
Qiu, Shao-Dong
Chen, Fei
author_sort Lu, Wen-Jie
collection PubMed
description PURPOSE: To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). METHODS: This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves. RESULTS: The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580–0.757] in the training cohort and 0.62 (95% CI, 0.467–0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727–0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662–0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05). CONCLUSIONS: The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.
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spelling pubmed-104820872023-09-07 Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer Lu, Wen-Jie Mao, Lin Li, Jin OuYang, Liang-Yan Chen, Jia-Yao Chen, Shi-Yan Lin, Yun-Yong Wu, Yi-Wen Chen, Shao-Na Qiu, Shao-Dong Chen, Fei Front Oncol Oncology PURPOSE: To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). METHODS: This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves. RESULTS: The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580–0.757] in the training cohort and 0.62 (95% CI, 0.467–0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727–0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662–0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05). CONCLUSIONS: The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC. Frontiers Media S.A. 2023-08-23 /pmc/articles/PMC10482087/ /pubmed/37681026 http://dx.doi.org/10.3389/fonc.2023.1046951 Text en Copyright © 2023 Lu, Mao, Li, OuYang, Chen, Chen, Lin, Wu, Chen, Qiu and Chen 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
Lu, Wen-Jie
Mao, Lin
Li, Jin
OuYang, Liang-Yan
Chen, Jia-Yao
Chen, Shi-Yan
Lin, Yun-Yong
Wu, Yi-Wen
Chen, Shao-Na
Qiu, Shao-Dong
Chen, Fei
Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
title Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
title_full Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
title_fullStr Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
title_full_unstemmed Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
title_short Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
title_sort three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482087/
https://www.ncbi.nlm.nih.gov/pubmed/37681026
http://dx.doi.org/10.3389/fonc.2023.1046951
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