The Role of the ThyroSeq v3 Molecular Test in the Surgical Management of Thyroid Nodules in the Canadian Public Health Care Setting
Bibliographic record
Abstract
Background: Although the current gold standard for diagnosing thyroid nodule malignancy is ultrasound-guided fine-needle aspiration (FNA) cytology, about 20–25% of cytological evaluations are considered indeterminate for malignancy. This limitation has led to the emergence of next-generation sequencing panels, for example, ThyroSeq v3 (TSv3), which recognize highly diagnostic genetic mutations of common thyroid carcinomas in FNA samples and classify them as test-negative or test-positive, helping optimize treatment for indeterminate thyroid nodules (ITNs). Our goals were to evaluate the benign call rate (BCR) of TSv3 and assess its diagnostic performance and clinical utility while highlighting the points of consideration for a public Canadian institution. Methods: This is a single-center study conducted at the Royal Victoria Hospital (McGill University Health Centre) in Montreal, Canada, between January and February 2019. Patients were offered TSv3 following the McGill algorithm for ITN workup, a novel protocol developed at our institution to select only diagnostic surgery candidates to minimize waste of public resources, considering the single-payer health care system. Patient demographics, cytopathology results, TSv3 data, treatment plan, and final histopathology result were reviewed. Results: A total of 50 ITNs underwent TSv3 testing; molecular analysis yielded 20 (40%) “positive” results and 24 (48%) “negative” results. Six (12%) results were classified as “currently negative” or “negative but limited.” “Currently negative” results indicate a low-risk mutation that alone is insufficient for development of a malignant lesion. “Negative but limited” results indicate a sample that is nondiagnostic for malignancy due to low cell count. BCR was calculated as (“negative” and “currently negative”)/total, resulting in a BCR of 58%. Twenty-three (46%) patients were scheduled for surgery and 27 (54%) patients continued with surveillance. Ninety-one percent (20 of 22) of the resected target nodules were malignant on final pathology. Conclusions: TSv3 proved beneficial in classifying ITNs as positive or negative, avoiding surgery in the latter cases. We found a lower reduction rate in surgery and BCR than the previously published studies, which is attributable to the criteria of the McGill algorithm. In the Canadian public health care system, preventing unnecessary surgery represents significant cost savings for the provincial government while also improving patient quality of life.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".