Young investigator challenge: Can the Ion AmpliSeq Cancer Hotspot Panel v2 be used for next‐generation sequencing of thyroid FNA samples?
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it