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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".