Young investigator challenge: Can the Ion AmpliSeq Cancer Hotspot Panel v2 be used for next‐generation sequencing of thyroid FNA samples?
Bibliographic record
Abstract
BACKGROUND: Fine-needle aspiration (FNA) cytology is accurate and cost-effective in the evaluation of thyroid nodules. Molecular techniques may contribute to risk stratification in indeterminate cases. Although next-generation sequencing (NGS) is a promising technique for the molecular testing of thyroid FNA specimens, thyroid-specific cancer gene panels are not commercially available. Conversely, the Ion AmpliSeq Cancer Hotspot Panel v2 (CHPv2), which includes the genes most frequently mutated in thyroid neoplasms, is commercially available and may represent an alternative to thyroid-specific panels. To the authors' knowledge to date, CHPv2 has performed well only on "ideal" cytological samples featuring abundant, high-quality DNA and satisfactory postsequencing metrics. The objective of the current study was to extend NGS to less-than-ideal samples, which represent a large percentage of routine clinical specimens. METHODS: A total of 37 thyroid smears were retrospectively analyzed using CHPv2, regardless of any preanalytical and postsequencing metric thresholds. Specifically, the authors evaluated the performance of CHPv2 on the BRAF, NRAS, HRAS, KRAS, and RET genes. Results were verified by pyrosequencing. RESULTS: Of the 37 thyroid FNA specimens, 34 (91.8%) were successfully processed. BRAF, NRAS, and RET somatic variants were detected in 22 of these 34 specimens (64.7%). NGS was found to have a high sensitivity (89.4%), specificity (85.7%), and accuracy (88.4%). CONCLUSIONS: CHPv2 is a valid option for the molecular evaluation of thyroid FNA specimens by NGS. It is interesting to note that this approach is accurate and effective even when applied to routine cytology samples that usually do not have optimal preanalytical and postsequencing requirements. Cancer Cytopathol 2016;124:776-84. © 2016 American Cancer Society.
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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 | 0.089 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".