ArticleFrontiers in endocrinology2025
Optimizing C-TIRADS for sub-centimeter thyroid nodules using machine learning-derived feature importance.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- The Importance of the Taller-than-Wide Feature and the Dimensions of Focal Thyroid Lesions in Assessing the Risk of Malignancy.Diagnostics (Basel, Switzerland) · 2026Article
- A multicenter, clinically interpretable prediction model for malignancy risk in C-TIRADS 3-4 thyroid nodules.Frontiers in oncology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.