Diagnostic Performance of ACR-TIRADS in Differentiating Benign From Malignant Thyroid Nodules in Patients Undergoing Fine-Needle Aspiration Biopsy: Comparative Study Based on Five International Guidelines for Management of Thyroid Nodules
Notice bibliographique
Résumé
Background: The purpose of this study was to retrospectively analyze the diagnostic performance of different international guidelines to detect benign from malignant nodules using fine-needle aspiration biopsy as a reference test. Methods: This study is a multi-institution, IRB-approved, retrospective study conducted from 2016 to 2020 that evaluated 200 consecutive biopsied thyroid nodules. The nodules were reclassified according to American College of Radiology Thyroid Imaging and Reporting Data System (ACR-TIRADS), Kwak-TIRADS (K-TIRADS), Korean Society of Thyroid Radiology (KSThR), European Thyroid Imaging and Reporting Data System (EU-TIRADS), and American Thyroid Association (ATA) guidelines. A Chi-squared test and receiver operating curve (ROC) with 95% confidence intervals and P-value < 0.05 were performed to calculate sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), false negative and unnecessary biopsy rate. The unnecessary biopsy rate was defined as the percentage of benign nodules among total biopsy-required nodules. Results: A total of 200 patients were included in this study. Patients aged from 23 to 74 years including 36 males and 164 females. The female/male ratio was 4.5:1. Female predominance was seen among most of the age groups. The cohort showed 26 (13%) malignant nodules and 174 (87%) benign nodules. A solid component was observed in the majority of malignant nodules (61.6%, P = 0.0376) and mixed component was observed in the majority of benign nodules (51.7%, P = 0.0376). There was no statistically significant difference in differentiating benign from malignant nodule with the echogenicity or orientation of the nodule. The statistically significant features of a benign nodule were spongiform appearance, no echogenic foci or comet tail and absence of peripheral halo (P < 0.03). The statistically significant features of a malignant nodule were a solid, peripheral halo, peripheral or punctate echogenic foci, microcalcification, and macrocalcification (P < 0.001). The ACR-TIRADS showed the highest specificity (40.23% (95% confidence interval (CI) 32 - 47)), PPV (18.75 (95% CI 0.12 - 0.26)), NPV (97.22 (95% CI 0.90 - 0.99)) and area under the curve (AUC) (0.6627 (95% CI 0.59 - 0.72)). This was closely followed by ATA which demonstrated the PPV of 17.39 (95% CI 0.11 - 0.24), NPV of 96.77 (95% CI 0.89 - 0.99) and AUC of 0.6340 (95% CI 0.57 - 0.69). The K-TIRADS has the highest sensitivity (96.15% (95% CI 80 - 99)). Lowest unnecessary biopsy rates were found with ACR-TIRADS (104 (52%) (P = 0.0013)) and KSThR guidelines (114 (57%) (P = 0.0059)) and highest with K-TIRADS (160 (80%) (P = 0.4482)). Conclusion: We found that diagnostic performance of ACR and ATA guidelines is higher and is a practical method for assessing thyroid nodules in routine practice. Both these guidelines can avoid unnecessary biopsies in a significant proportion of benign thyroid lesions. ACR-TIRADS is also very specific in identifying malignant lesions. The increased sensitivity of K-TIRADS is likely due to their lower size threshold. J Endocrinol Metab. 2021;11(3-4):69-75 doi: https://doi.org/10.14740/jem735
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».