Prevalence and Spectrum of <i>DICER1</i> Mutations in Adult-onset Thyroid Nodules with Indeterminate Cytology
Bibliographic record
Abstract
CONTEXT: DICER1 mutations are found in multinodular goiter and differentiated thyroid carcinoma in children, and can be a manifestation of DICER1 syndrome, but the prevalence of DICER1 mutations and their significance in adult-onset thyroid nodules is unknown. OBJECTIVE: Determine (1) the prevalence of DICER1 hotspot mutations in thyroid nodules; (2) the frequency of a second DICER1 pathogenic variant in thyroid nodules with DICER1 hotspot mutations; (3) the prevalence of other thyroid cancer driver mutations in thyroid nodules with and without DICER1 hotspot mutations. METHODS: Population-based study of 14 993 consecutive fine needle aspiration biopsies of thyroid nodules evaluated by ThyroSeq v3. From 214 DICER1 hotspot-positive cases, we selected 61, matched to DICER1 hotspot-negative nodules. We performed full sequencing of all exons and exon-intron boundaries of DICER1. SETTING: Commercial and university-based laboratories in the United States and Canada. RESULTS: Among 14 993 thyroid nodules, 214 (1.4%) revealed a DICER1 hotspot mutation. A second pathogenic/likely pathogenic variant in DICER1 was found in 45/59 (76%) DICER1 hotspot-positive nodules studied while no other DICER1 variant was identified in the DICER1 hotspot-negative group by full DICER1 sequencing. Other alterations in thyroid-related genes were significantly more frequent in DICER1 hotspot-negative nodules (32/61) than in DICER1 hotspot--positive nodules (4/59) (P < .0001). CONCLUSION: DICER1 alterations occur in a proportion of adult thyroid nodules and appear mutually exclusive with alterations in other thyroid cancer-related genes. DICER1 hotspot mutations occur with a second hit in most cases and could suggest occult DICER1 syndrome in adults with thyroid nodules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".