Utility of droplet digital polymerase chain reaction for TERT and BRAF mutational profiling of thyroid nodules
Bibliographic record
Abstract
BACKGROUND: Mutations involving BRAF and TERT are important predictors of disease severity in thyroid cancer, but molecular testing is limited by cost and lack of adequate tissue sample. This study aimed to assess the utility of BRAFV600E and TERT testing using droplet digital PCR (ddPCR) as a diagnostic and prognostic tool for thyroid fine needle aspirate biopsy (FNAB). METHODS: Patients with thyroid nodules were prospectively enrolled from March 2015 to September 2018. Pre-operative FNAB was collected for standard cytology and molecular testing. BRAFV600E and TERT levels were analyzed by ddPCR. Cytology (Bethesda system) and ddPCR results were correlated to surgical pathology. RESULTS: A total of 222 patients were enrolled, of which 124 received thyroid surgery. Pre-operative cytology alone with Bethesda ≥5 was 100% specific and 70% sensitive for malignancy on final surgical pathology. BRAFV600E positivity or TERT overexpression was 100% specific and 60.0% sensitive. Combining cytology (Bethesda ≥5) with BRAFV600E and TERT testing increased the sensitivity of a malignant diagnosis to 80.0%. High TERT levels and/or BRAFV600E was associated with aggressive or advanced stage pathology. CONCLUSIONS: Combining cytology with ddPCR analysis of BRAFV600E and TERT can improve the diagnostic accuracy of thyroid FNAB, and help predict aggressive pathology.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".