| ISSN |
1949-8470 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Retrospective Study |
| Article Title |
Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Hao Wang, Xuan Wang, Yu-Sheng Du, You Wang, Zhuo-Jie Bai, Di Wu, Wu-Liang Tang, Han-Ling Zeng, Jing Tao and Jian He |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Science and Technology Development Fund of Nanjing Medical University |
No. NMUB20230037 |
| Youth Scientific Research Nurturing Fund of Jiangbei Campus of Zhongda Hospital Affiliated with Southeast University |
No. JB2024Q01 |
|
| Corresponding Author |
Jian He, Associate Professor, Chief Physician, MD, PhD, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Medicine School, Nanjing University, No. 321 Zhongshan Road, Nanjing 210008, Jiangsu Province, China. hjxueren@126.com |
| Key Words |
Papillary thyroid carcinoma; Thyroid nodules; Radiomics; Machine learning; Non-contrast computed tomography |
| Core Tip |
This study introduces a novel non-contrast computed tomography-based machine learning model integrating radiomics and clinical features with lobe segmentation for preoperative differentiation of benign and malignant thyroid nodules. Leveraging dual-center data and thyroid lobe segmentation, the extreme gradient boosting model demonstrated superior diagnostic accuracy and stability across diverse cohorts, outperforming traditional radiologist assessments. Key predictors, including radiomic score, age, and tumor size group, calcify and cystic, were showed through SHAP analysis, enhancing model interpretability. The approach offers a robust, non-invasive tool for personalized preoperative decision-making, with the potential to improve clinical management of thyroid nodules. |
| Publish Date |
2025-06-26 11:40 |
| Citation |
<p>Wang H, Wang X, Du YS, Wang Y, Bai ZJ, Wu D, Tang WL, Zeng HL, Tao J, He J. Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study. <i>World J Radiol</i> 2025; 17(6): 106682</p> |
| URL |
https://www.wjgnet.com/1949-8470/full/v17/i6/106682.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v17.i6.106682 |
