ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1668347
This article is part of the Research TopicRadiomics and Artificial Intelligence in Oncology ImagingView all 18 articles
Optimizing C-TIRADS for Sub-Centimeter Thyroid Nodules Using Machine Learning–Derived Feature Importance
Provisionally accepted
- 1Cancer Hospital of Shantou University Medical College, 广东省, China
- 2The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
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Background: To optimize the diagnostic performance of the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) for sub-centimeter thyroid nodules by incorporating machine learning–derived feature importance. Methods: This retrospective study included 741 patients in a primary cohort and 421 patients in an external validation cohort. SHapley Additive exPlanations (SHAP) were used to quantify the diagnostic contribution of six ultrasound features based on an XGBoost model. A modified C-TIRADS scoring system was developed by assigning greater weight to the most contributive feature while retaining original weights for other features. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and decision curve analysis (DCA). Results: SHAP analysis identified vertical orientation as the most predictive feature for malignancy in sub-centimeter nodules. The modified scoring system significantly improved diagnostic performance in both the primary (AUC: 0.911 vs. 0.898, P < 0.001) and validation cohorts (AUC: 0.931 vs. 0.899, P < 0.001). NRI analysis further showed a substantial improvement in risk classifications, with NRI values of 0.406 in the primary and 0.471 in the validation cohort (both P < 0.001). DCA demonstrated greater net clinical benefit across wider threshold ranges in both cohorts. Additionally, malignancy rates exhibited a more rational stepwise increase from C-TIRADS 4A to 5, indicating improved risk stratification. Conclusion: The SHAP-guided modified C-TIRADS scoring system enhances diagnostic accuracy and risk stratification for sub-centimeter thyroid nodules and may facilitate improved clinical decision-making in this challenging subset.
Keywords: thyroid nodules, C-TIRADS, machine learning, Shap, Microcarcinoma
Received: 17 Jul 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Guo, Lin, Wang, Liao, Huang, Zhai and Chen. 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: Zhe Chen, Cancer Hospital of Shantou University Medical College, 广东省, China
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