Multiplatform Molecular Testing as an Adjunct to Fine Needle Aspiration in the Evaluation of Pediatric Thyroid Nodules
Bibliographic record
Abstract
CONTEXT: Molecular analysis of thyroid cytopathology derived from fine needle aspiration (FNA) improves diagnostic yield and informs presurgical treatment stratification of adults with thyroid nodules. Its use is limited in pediatrics, but it has the potential to guide clinical management for these patients. OBJECTIVE: To report on the results of oncogene sequencing and microRNA (miRNA)-based risk classification of thyroid FNA cytopathology in a pediatric cohort. DESIGN: Retrospective analysis of archived FNA samples collected from patients seen at Lurie Children's Hospital between October 2020 and March 2023; 39 cases were included in the study. SETTING: Referral center. PATIENTS: Thirty-seven patients ages 7 to 20 years (mean age 13.9 years). INTERVENTIONS: Interpace Diagnostics®' multiplatform testing including ThyGeNEXT® (oncogene panel) and ThyraMIR® (miRNA risk classifier) was performed on FNA samples. Molecular results were compared to final surgical pathology or repeat FNA results. MAIN OUTCOME MEASURES: Description of cohort molecular characteristics; test sensitivity and specificity. RESULTS: The most common oncogenic alterations among malignant cases were the BRAFV600E variant (38%), RET-PTC1 fusions (14%), and NRAS variants (14%). Nineteen of 21 malignant samples had positive (high-risk) miRNA expression profiles. Fifteen of 18 benign cases had negative (low-risk) miRNA profiles. Multiplatform molecular testing demonstrated 90.5% sensitivity and 88.9% specificity in detecting malignant pathology. CONCLUSION: This is the first study to report on the use of combined oncogene and miRNA analysis of FNA cytopathology in a pediatric cohort. Multiplatform molecular testing is a potentially useful adjunct to FNA as a presurgical diagnostic tool in the evaluation of pediatric thyroid nodules.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".