The first Canadian experience with the Afirma® gene expression classifier test
Bibliographic record
Abstract
BACKGROUND: Thyroid nodules are common and often benign, although prove to be malignant upon surgical pathology in 5-15% of cases. When assessed with ultrasound-guided fine-needle aspiration (USFNA), 15-30% of the nodules yield an indeterminate result. The Afirma® gene expression classifier (AGEC) was developed to improve management of indeterminate thyroid nodules (ITNs) by classifying them as "benign" or "suspicious." Objectives were (1) to assess the performance of the AGEC in two Canadian academic medical centres (2), to search for inter-institutional variation and (3) to compare AGEC performance in Canadian versus American institutions. METHODS: We undertook a retrospective cohort study of patients with indeterminate cytopathology (Bethesda Class III or IV) as per USFNA who underwent AGEC testing. We reviewed patient demographics, cytopathological results, AGEC data and, if the patient underwent surgery, results from their final pathology. RESULTS: In total, we included 172 patients with Bethesda Class III or IV thyroid nodules underwent AGEC testing, 109 in Montreal, Quebec and 63 in St. John's, Newfoundland, in this study. Among the nodules sent for testing, 55% (60/109) in Montreal and 46% (29/63) in St. John's returned as "benign." None of these patients underwent surgery. On the other hand, 45% (49/109) nodules in Montreal and 54% (34/63) in St. John's were found to be "suspicious," for a total of 83 specimens. Seventy seven of these patients underwent surgery. Both in Montreal and St. John's, the final pathology yielded malignant thyroid disease in approximately 50% of the specimens categorized as "suspicious." Since 2013, no patient diagnosed with a benign nodule as per AGEC testing was found to harbor a malignant thyroid nodule on follow-up. CONCLUSIONS: Molecular analysis is increasingly used in the management of indeterminate thyroid nodules. This study highlights the experience of two Canadian centres with AGEC testing. We found inter-institutional variability in the rate of nodules returning as "benign," however we found similar rates of confirmed malignancy in nodules returning as "suspicious." According the literature, results for AGEC testing in two Canadian institutions align with results reported in American centres.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".