Applying TI-RADS in Thyroid Sonography: Clearing Away the Fog
Bibliographic record
Abstract
Out of every 100 adults living in North America, 4 to 7 will have a palpable thyroid nodule and 19 to 67 will have a non-palpable nodule demonstrated by ultrasound (US). Despite a 2.4X increase in detection of thyroid cancer over the last 30 years, the 5-year mortality rate has been stable at around 2% since 2005. This could be due to many factors, but one apparent significant contributor is the over-diagnosis of malignant nodules, likely based on better detection rates from US imaging. It has been estimated that more than half of papillary thyroid cancer nodules (the most common type of thyroid cancer) would not have resulted in clinical symptoms during the life-time of the patient if left alone. With US now able to detect cysts as small as 1mm, and solid nodules in the range of 2-3 mm, physicians are left with a difficult decision in determining which nodules require a fine-needle aspiration (FNA) and which are better left alone. To aid with this difficult decision, standardized evidenced based guidelines were developed by various groups and organizations. Of these, thyroid image reporting and data system (TI-RADS) and American Thyroid Association (ATA) 2015 guidelines are the most commonly used in our institution (The Ottawa Hospital). While these efforts to create a standardized system are helpful in determining which nodules require FNA, the basis of these decisions has to come from high-quality imaging technique. However, a retrospective study at our institution found that there was large discrepancy in imaging technique used by technologists when imaging the thyroid. We suggest, at any point of thyroid imaging (pre, during and post FNA) a standardized technique be used in assessment. Providing all necessary imaging data will allow radiologists to produce a sufficiently detailed report, synthesize the findings, and ensure the best management plan possible for each patient. Future work by technologist associations may help establish more robust imaging standards for this patient population.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".