Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1785102110915821568 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10482087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT luwenjie threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT maolin threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT lijin threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT ouyangliangyan threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT chenjiayao threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT chenshiyan threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT linyunyong threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT wuyiwen threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT chenshaona threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT qiushaodong threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer AT chenfei threedimensionalultrasoundbasedradiomicsnomogramforthepredictionofextrathyroidalextensionfeaturesinpapillarythyroidcancer |