ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1486920
Building Radiomics Models Based on ACR TI-RADS Combining Clinical Features for Discriminating Benign and Malignant Thyroid Nodules
Provisionally accepted
- The First people's Hospital of Xiaoshan District, Hangzhou, Jiangsu Province, China
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The aim of this study was to establish and validate a radiomics model combining the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and clinical features and to build a nomogram that could be utilized to enhance the diagnostic performance of malignant thyroid nodules.: From January 2019 to September 2022, 329 thyroid nodules from 323 patients who had been referred for surgery and had pathological evidence of them were gathered retrospectively and randomly allocated to training and test cohorts (8:2 ratio). A total of 107 radiomics features were extracted from the US images, and the radiomics score (Rad-score) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were created using logistic regression, including the clinic-ACR score (Clin+ACR), clinic-Rad score (Clin+Rad), ACR score-Rad score (ACR+Rad), and combined clinic-ACR score-Rad score (Clin+ACR+Rad). The diagnostic performance of different models was calculated and compared using the area under the receiver operating curve (AUC) and the corresponding sensitivity and specificity. Results: Eight radiomics features were independent signatures for predicting malignant TNs, with malignant TNs having higher Rad-scores in both cohorts (P < 0.05). The Clin+ACR+Rad model showed excellent diagnostic prediction ability in both the training (AUC = 0.958) and test datasets (AUC = 0.937), significantly outperforming other models including Rad-score (AUC = 0.890, 0.856), Clin+Rad (AUC = 0.895, 0.859), ACR+Rad (AUC = 0.943, 0.934), and Clin+ACR (AUC = 0.784, 0.785) (all P < 0.05). We further analyzed the performance of our integrated model (Clin+ACR+Rad) compared to the traditional ACR TI-RADS system at different probability thresholds. At the statistically optimal threshold of 0.386, the unnecessary biopsy rate decreased from 46.97% to 22.05% in the training cohort and from 45.83% to 21.05% in the test cohort. Conclusion:The current study offers preliminary support that the model of combined clinic-ACR score-radiomics score can be helpful for predicting malignancy in thyroid nodules by looking at a retrospective cohort of surgically treated thyroid nodules. The Clin-ACR-Rad nomogram may be a more practical instrument and more accurate prediction model for malignant thyroid nodules.
Keywords: Radiomics, ACR TI-RADS, thyroid nodules, nomogram, prediction
Received: 27 Aug 2024; Accepted: 03 Jul 2025.
Copyright: © 2025 Chen, Zhang, Chen and Lu. 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) or licensor 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.
* Correspondence: Jiajia Lu, The First people's Hospital of Xiaoshan District, Hangzhou, Jiangsu Province, China
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