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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Jun 28, 2025; 17(6): 106682
Published online Jun 28, 2025. doi: 10.4329/wjr.v17.i6.106682
Published online Jun 28, 2025. doi: 10.4329/wjr.v17.i6.106682
Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study
Hao Wang, Yu-Sheng Du, You Wang, Zhuo-Jie Bai, Department of Radiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing 210031, Jiangsu Province, China
Xuan Wang, Di Wu, Wu-Liang Tang, Department of Radiology, Zhongda Hospital Southeast University (Jiangbei), Nanjing 210048, Jiangsu Province, China
Han-Ling Zeng, Jing Tao, Department of General Surgery, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing 210031, Jiangsu Province, China
Jian He, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Medicine School, Nanjing University, Nanjing 210008, Jiangsu Province, China
Co-first authors: Hao Wang and Xuan Wang.
Co-corresponding authors: Jing Tao and Jian He.
Author contributions: Wang H wrote the original manuscript, contributed software, and resources to the manuscript; Wang X reviewed and edited the manuscript; Wang H and Wang X wrote the manuscript, they contributed equally to this article, they are the co-first authors of this manuscript; Wang H, Du YS, and Tang WL organized the data; Wang Y and Wu D performed the validation; Wang H, Bai ZJ, and Zeng HL performed the methodology; Tao J supervised; He J performed project management; Tao J and He J contributed equally to this article, they are the co-corresponding authors of this manuscript; and all authors thoroughly reviewed and endorsed the final manuscript.
Supported by the Science and Technology Development Fund of Nanjing Medical University, No. NMUB20230037; and the Youth Scientific Research Nurturing Fund of Jiangbei Campus of Zhongda Hospital Affiliated with Southeast University, No. JB2024Q01.
Institutional review board statement: This study was approved by the Medical Ethics Committee of The Fourth Affiliated Hospital of Nanjing Medical University, approval No. 20240628-K077.
Informed consent statement: Due to the retrospective nature of the study, requirements for informed consent were waived.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data utilized and/or analyzed during the present study are available from the corresponding author upon reasonable request.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jian He, MD, PhD, Associate Professor, Chief Physician, 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.
Received: March 12, 2025
Revised: April 17, 2025
Accepted: May 21, 2025
Published online: June 28, 2025
Processing time: 106 Days and 20.8 Hours
Revised: April 17, 2025
Accepted: May 21, 2025
Published online: June 28, 2025
Processing time: 106 Days and 20.8 Hours
Core Tip
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.