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An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma

Purpose: This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). Patients and Methods: The prediction model was developed in 609 patients with clinicopatholog...

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Autores principales: Zhou, Shi-Chong, Liu, Tong-Tong, Zhou, Jin, Huang, Yun-Xia, Guo, Yi, Yu, Jin-Hua, Wang, Yuan-Yuan, Chang, Cai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498535/
https://www.ncbi.nlm.nih.gov/pubmed/33014810
http://dx.doi.org/10.3389/fonc.2020.01591
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author Zhou, Shi-Chong
Liu, Tong-Tong
Zhou, Jin
Huang, Yun-Xia
Guo, Yi
Yu, Jin-Hua
Wang, Yuan-Yuan
Chang, Cai
author_facet Zhou, Shi-Chong
Liu, Tong-Tong
Zhou, Jin
Huang, Yun-Xia
Guo, Yi
Yu, Jin-Hua
Wang, Yuan-Yuan
Chang, Cai
author_sort Zhou, Shi-Chong
collection PubMed
description Purpose: This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). Patients and Methods: The prediction model was developed in 609 patients with clinicopathologically confirmed unifocal PTC who received ultrasonography between Jan 2018 and June 2018. Radiomic features were extracted after the ultrasonography of PTC. Lasso regression model was used for data dimensionality reduction, feature selection, and radiomics signature building. The predicting model was established based on the multivariable logistic regression analysis in which the radiomics signature, ultrasonography-reported LN status, and independent clinicopathologic risk factors were incorporated, and finally a radiomics nomogram was established. The performance of the nomogram was assessed with respect to the discrimination and consistence. An independent validation was performed in 326 consecutive patients from July 2018 to Sep 2018. Results: The radiomics signature consisted of 23 selected features and was significantly associated with LN status in both primary and validation cohorts. The independent predictors in the radiomics nomogram included the radiomics signature, age, TG level, TPOAB level, and ultrasonography-reported LN status. The model showed good discrimination and consistence in both cohorts: C-index of 0.816 (95% CI, 0.808–0.824) in the primary cohort and 0.858 (95% CI, 0.849–0.867) in the validation cohort. The area under receiver operating curve was 0.858. In the validation cohort, the accuracy, sensitivity, specificity and AUC of this model were 0.812, 0.816, 0.810, and 0.858 (95% CI, 0.785–0.930), respectively. Decision curve analysis indicated the radiomics nomogram was clinically useful. Conclusion: This study presents a convenient, clinically useful ultrasound radiomics nomogram that can be used for the pre-operative individualized prediction of central LN metastasis in patients with PTC.
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spelling pubmed-74985352020-10-02 An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma Zhou, Shi-Chong Liu, Tong-Tong Zhou, Jin Huang, Yun-Xia Guo, Yi Yu, Jin-Hua Wang, Yuan-Yuan Chang, Cai Front Oncol Oncology Purpose: This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). Patients and Methods: The prediction model was developed in 609 patients with clinicopathologically confirmed unifocal PTC who received ultrasonography between Jan 2018 and June 2018. Radiomic features were extracted after the ultrasonography of PTC. Lasso regression model was used for data dimensionality reduction, feature selection, and radiomics signature building. The predicting model was established based on the multivariable logistic regression analysis in which the radiomics signature, ultrasonography-reported LN status, and independent clinicopathologic risk factors were incorporated, and finally a radiomics nomogram was established. The performance of the nomogram was assessed with respect to the discrimination and consistence. An independent validation was performed in 326 consecutive patients from July 2018 to Sep 2018. Results: The radiomics signature consisted of 23 selected features and was significantly associated with LN status in both primary and validation cohorts. The independent predictors in the radiomics nomogram included the radiomics signature, age, TG level, TPOAB level, and ultrasonography-reported LN status. The model showed good discrimination and consistence in both cohorts: C-index of 0.816 (95% CI, 0.808–0.824) in the primary cohort and 0.858 (95% CI, 0.849–0.867) in the validation cohort. The area under receiver operating curve was 0.858. In the validation cohort, the accuracy, sensitivity, specificity and AUC of this model were 0.812, 0.816, 0.810, and 0.858 (95% CI, 0.785–0.930), respectively. Decision curve analysis indicated the radiomics nomogram was clinically useful. Conclusion: This study presents a convenient, clinically useful ultrasound radiomics nomogram that can be used for the pre-operative individualized prediction of central LN metastasis in patients with PTC. Frontiers Media S.A. 2020-09-04 /pmc/articles/PMC7498535/ /pubmed/33014810 http://dx.doi.org/10.3389/fonc.2020.01591 Text en Copyright © 2020 Zhou, Liu, Zhou, Huang, Guo, Yu, Wang and Chang. http://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
Zhou, Shi-Chong
Liu, Tong-Tong
Zhou, Jin
Huang, Yun-Xia
Guo, Yi
Yu, Jin-Hua
Wang, Yuan-Yuan
Chang, Cai
An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma
title An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma
title_full An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma
title_fullStr An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma
title_full_unstemmed An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma
title_short An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma
title_sort ultrasound radiomics nomogram for preoperative prediction of central neck lymph node metastasis in papillary thyroid carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498535/
https://www.ncbi.nlm.nih.gov/pubmed/33014810
http://dx.doi.org/10.3389/fonc.2020.01591
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