Cancer Risk in Thyroid Nodules: An Analysis of Over 1000 Consecutive FNA Biopsies Performed in a Single Canadian Institution
Bibliographic record
Abstract
Objective: To determine the cancer risk in thyroid nodules using ACR TI-RADS. Methods: A retrospective analysis of all thyroid biopsies was performed over a 3-year period (2021 to 2023). Variables including gender, age, history of thyroid cancer or neck irradiation, nodule size and location, TR level, and sonographic features such as punctate echogenic foci (PEF), a very hypoechoic appearance, taller-than-wide shape, and suspected extrathyroidal extension were analyzed. Results: A total of 1140 nodules were assessed in 993 patients, including 740 females (74.5%) and 253 males (25.5%). The mean patient age was 57.1 ± 15.4 years. Variables significantly associated with nodule malignancy included (1) younger age, (2) a prior history of thyroid cancer or neck irradiation, (3) a higher TR level, (4) a taller-than-wide shape in nodules <1 cm, (5) PEF, (6) a very hypoechoic appearance, and (5) suspected extrathyroidal extension (p < 0.05). Gender, nodule location and size were not associated with a higher cancer risk (p > 0.05). Malignancy was found in 40.7% of TR5, 4.8% of TR4, 0.3% of TR3, and 0% of TR1 and 2 nodules. The odds ratios (ORs) for cancer were as follows: TR4 or 5, OR = 19; PEF, OR = 11; a very hypoechoic appearance, OR = 13.3; and suspected extrathyroidal extension, OR = 27.2 (p < 0.01). Conclusions: Higher TR levels, PEF, a very hypoechoic appearance, and suspected extrathyroidal extension are important features for predicting cancer risk. These findings affirm the effectiveness of ACR TI-RADS in nodule risk stratification.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".