Investigation of genetic sex‐specific molecular profile in well‐differentiated thyroid cancer: Is there a difference between females and males?
Bibliographic record
Abstract
Abstract Background Although more common in females, thyroid cancer is deemed to be more aggressive in males. The reasons for sex disparities in thyroid cancer are not well understood. We hypothesised that differences in molecular mutations between females and males contribute to this phenomenon. Methods Retrospective multicentre multinational study of thyroid nodules that underwent preoperative molecular profiling between 2015 and 2022. The clinical characteristics and mutational profiles of tumours in female and male patients were compared. Collected data included demographics, cytology results, surgical pathology, and molecular alterations. Results A total of 738 patients were included of which 571 (77.4%) were females. The extrathyroidal extension was more common in malignancies in males (chi‐squared, p = 0.028). The rate of point mutations and gene fusions were similar in both sex groups (p > 0.05 for all mutations). Patients with nodules with BRAFV600E mutations were significantly younger than BRAF wild‐type nodule patients (t‐test, p = 0.0001). Conversely, patients with TERT promoter mutations were significantly older than patients with wild‐type TERT (t‐test, p < 0.0001). For patients harbouring both BRAFV600E and TERT mutations, the difference in age at presentation was significantly different in females (t‐test, p = 0.009) but not in males (t‐test, p = 0.433). Among females, patients with BRAFV600E and TERT mutations were significantly older than their wild‐type or single‐mutation counterpart (t‐test, p = 0.003). Conclusion The absolute rate of molecular mutations was similar in females and males. We found that extrathyroidal extension was more common in males. Moreover, BRAFV600E and TERT mutations occur at a younger age in males than in females. These two findings are factors that may explain the tendency of more aggressive disease in males.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".