Clinical and Histopathological Features of Thyroid Cancer with TERT Promoter Molecular Alterations in Isolation Versus with Concurrent Molecular Alterations: A Multicenter Retrospective Study
Bibliographic record
Abstract
Background/Objectives: Molecular testing of thyroid nodules enables the detection of genetic alterations, which can help assess the risk of malignancy and tumor behavior. While telomerase reverse transcriptase (TERTp) mutations are known to be associated with aggressive disease, their exact prognostic significance when occurring alone or with other molecular alterations remains underreported. Methods: This study examined patients with thyroid cancer treated at two tertiary care hospitals from 2017 to 2024. We compared tumor behavior in patients with TERTp molecular alterations occurring alone and with concurrent molecular alterations. Aggressive histologic subtypes were defined as tall-cell, hobnail, and columnar variants of papillary carcinoma, as well as poorly differentiated and anaplastic carcinoma. High-risk disease was defined according to the 2015 ATA guidelines as gross extrathyroidal extension, lymph node metastasis >3 cm, postoperative elevated serum thyroglobulin, distant metastases, and/or positive resection margins. Statistical analysis was performed to assess differences between groups. Results: 30 patients with TERTp-positive thyroid malignancies were included. TERTp/BRAF V600E was the most prevalent mutation combination (n = 13, 43.3%), followed by TERTp alone (n = 8, 26.7%) and TERTp/RAS (n = 7, 23.4%). TERTp/EIF1AX/GNAS and TERTp/EIF1AX/PIK3CA were the least common combinations (n = 1, 3.3% each). Nodules with TERTp and concurrent mutations were significantly more likely to be classified as high-risk (p = 0.006) and were more frequently associated with aggressive histologic subtypes (p = 0.003) compared to those with TERTp mutations alone, which tended to exhibit more benign behavior. Conclusions: Thyroid carcinomas harboring both TERTp and concurrent molecular alterations are associated with more aggressive features and a higher likelihood of being classified as high-risk. In contrast, TERTp mutations occurring alone do not confer an elevated risk.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".