SUN-326 The additive value of real-time elastography to thyroid ultrasound in detecting malignancy in nodules over 20 mm in diameter
Bibliographic record
Abstract
Abstract Disclosure: N. Angelopoulos: None. D.G. Goulis: None. S. Livadas: None. R. Paparodis: None. I. Androulakis: None. A. Boniakos: None. J.C. Jaume: None. I. Iakovou: None. Purpose. Ultrasonography (US) is the most accurate and cost-effective imaging method for identifying thyroid nodules. The difficulty in determining which nodules to sample for fine-needle aspiration (FNA) cytology has prompted the introduction of the Thyroid Imaging Reporting and Data Systems (TIRADS), which assesses the malignancy risk associated with thyroid nodules. Real-time elastography (RTE), coupled with strain ratio (SR) measurements, offers a means to evaluate the nodule stiffness and potentially discern their likelihood of being malignant. The present study aimed to investigate the efficacy of RTE and SR, combined with the TIRADS grading systems, in distinguishing between benign and malignant thyroid nodules. Methods. From 1094 patients with thyroid nodules referred for thyroid ultrasound at a University Hospital, those with thyroid nodules ≥20 mm in diameter were enrolled. Each nodule was categorized according to European (EU)- and American College of Radiology (ACR)-TIRADS systems, ranging from 2 to 5. Nodules’ semiquantitative SRs were evaluated together with RTE. The thyroid nodule diagnosis was documented by post-thyroidectomy histopathological examination and/or US-guided FNA according to the Bethesda classification of the examined smears.Results. The study involved 267 patients (mean age 60.3 ± 14.3 years; 46 males and 221 females) with 308 nodules categorized into EU-TIRADS categories 3, 4, and 5. Of these nodules, 22 proved malignant, and 286 benign. The elastography ratio exhibited high predictive performance in diagnosing thyroid malignancy (p<0.001) at a threshold value of >0.84 (sensitivity 90.9%, specificity 73.4%). In the 168 nodules with EU-TIRADS 3, this threshold had 100% sensitivity and 75.1% specificity in discriminating malignant thyroid nodules.Conclusion. Combining TIRADS with data derived from RTE reduces unnecessary FNAs and surgeries in patients with thyroid nodular disease. Presentation: Sunday, July 13, 2025
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".