Prospective Optimization of Malignancy Risk Prediction in Indeterminate Thyroid Nodules: Diagnostic Synergy of ACR TI-RADS and the 2023 Bethesda System
| dc.contributor.author | Aydin, Ozlem | |
| dc.contributor.author | Colakoglu, Bulent | |
| dc.contributor.author | Kayhan, Cavit Kerem | |
| dc.contributor.author | Gunver, Mehmet Guven | |
| dc.contributor.author | Simplicio, Mariana | |
| dc.contributor.author | Pinto Schmitt, Joana | |
| dc.contributor.author | Canberk, Sule | |
| dc.date.accessioned | 2026-11-05T11:06:25Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Background: Risk stratification of indeterminate thyroid nodules (Bethesda III-IV) remains difficult and often triggers unnecessary procedures. Ultrasound-based ACR TI-RADS and the 2023 Bethesda System are widely used, but the incremental value of combining them and the role of size thresholds needs prospective validation. Objective: The objective of this study was to prospectively compare the diagnostic performance of ACR TI-RADS and the 2023 Bethesda System, alone and in combination, for predicting malignancy in thyroid nodules, with dedicated analyses of indeterminate lesions (Bethesda categories III-IV), including subtypes of Bethesda III (nuclear atypia vs. other atypia), and the impact of nodule size. Methods: Histopathology was available for 131 nodules. Diagnostic metrics (sensitivity, specificity, PPV, NPV), ROC curves (DeLong comparison), and Youden indices were calculated for individual and combined thresholds; a 16 mm size cut-off was explored. Results: Malignancy was confirmed in 105/131 nodules (80.2%). Bethesda outperformed TI-RADS (AUC 0.87 vs. 0.69; DeLong p = 0.041). Malignancy rates rose with higher categories (e.g., TI-RADS 5: 93.6%; Bethesda category V: 100%; Bethesda category VI: 100%) and were markedly elevated in the histologically confirmed subset for Bethesda category III (32/41; 78.0%) and IV (6/8; 75.0%). The combined requirement of TI-RADS >= 4 and Bethesda >= 4 maximized specificity (96.2%) and PPV (98.4%) with a high Youden J (0.552), supporting a rule-in strategy in category IV of Bethesda. Size alone was a weak discriminator (AUC 0.66); within Bethesda III-IV nodules, malignancy did not differ significantly by the 16 mm threshold (p = 1.00). ROC using continuous tumor size yielded AUC = 0.66; the ROC-derived optimal cut-off was 16 mm. Applying this split produced sensitivity 0.80 and specificity 0.50. Conclusions: Integrating ACR TI-RADS with Bethesda cytology significantly improves specificity and PPV for indeterminate thyroid nodules, supporting a morphology-driven approach over traditional size-based thresholds. Incorporation of combined sonographic-cytologic criteria into management algorithms may reduce unnecessary interventions and optimize patient care. | |
| dc.description.issue | 1 | |
| dc.description.pages | 12 | |
| dc.description.volume | 7 | |
| dc.identifier.doi | 10.3390/endocrines7010012 | |
| dc.identifier.issn | 2673-396X | |
| dc.identifier.uri | https://doi.org/10.3390/endocrines7010012 | |
| dc.identifier.uri | https://openaccess.acibadem.edu.tr/handle/11443/11351 | |
| dc.identifier.wos | WOS:001725272000001 | |
| dc.language.iso | en | |
| dc.publisher | MDPI | |
| dc.relation.hasversion | 10.3390/endocrines7010012 | |
| dc.relation.ispartof | ENDOCRINES | |
| dc.subject | thyroid nodule | |
| dc.subject | Bethesda III-IV | |
| dc.subject | ACR TI-RADS | |
| dc.subject | ultrasound | |
| dc.subject | cytology | |
| dc.subject | malignancy risk | |
| dc.subject | diagnostic accuracy | |
| dc.subject | FNA | |
| dc.subject | fine needle aspiration | |
| dc.subject | Endocrinology & Metabolism | |
| dc.title | Prospective Optimization of Malignancy Risk Prediction in Indeterminate Thyroid Nodules: Diagnostic Synergy of ACR TI-RADS and the 2023 Bethesda System | |
| dc.type | Article | |
| oaire.citation.edition | Emerging Sources Citation Index (ESCI) | |
| oaire.citation.issue | 1 | |
| oaire.citation.volume | 7 | |
| person.identifier.rid | OLR-3091-2025 | |
| person.identifier.rid | AAM-8781-2020 | |
| person.identifier.rid | AAD-1784-2020 |
