SAT523 Molecular Profile of Local vs. Regional Aggressive Thyroid Cancer: A Multicenter Study
Bibliographic record
Abstract
Abstract Disclosure: I. Tessler: None. N. Gecel: None. R. Payne: None. G. Avior: None. A. Eran: None. Background: Genetic testing for the diagnosis of thyroid cancer has rapidly evolved in recent years. While commonly applied for diagnosis, its role in predicting genotype-phenotype correlation and guiding management is emerging. Here we evaluate differences in the molecular profile of local vs. regional aggressive disease. Methods: We performed a retrospective multicenter study of patients who underwent molecular profiling for DTC between 2018-2021, dividing them into three groups: low-risk, locally aggressive, and regional aggressive. We analyzed the patients' basic characteristics, disease aggressive features (extranodal extension, perineural invasion), lymphovascular invasion, and extrathyroidal extension), and the mutation distribution according to disease aggressiveness. Genetic variants were stratified by risk levels according to the 2015 ATA guidelines. Results: The study included a total of 652 patients, 414 in the low-risk group, 73 in the locally aggressive group, and 165 in the regional aggressive group. The regional aggressive group had the lowest age at diagnosis (mean age of 44.64±13.78 years vs. 52.52±14.53 and 53.58±14.93 for the low-risk and locally-aggressive, respectively, p>0.001), the highest rate of mutation-positive nodules (86.1% vs. 6.6% and 67.1%), and the highest rate of aggressive mutation (76.4% vs. 17.6 and 39.7). On the contrary, RAS mutations were more common in the low-risk (31.4% vs. 19.2% and 6.1% in the local- and regional aggressiveness groups). Conclusion: This study showed that local and regional aggressive thyroid cancer has a distinct molecular profile, with a higher prevalence of high-risk mutations in the regional group. These findings may enhance the use of genetic testing in predicting disease aggressiveness and guiding management in thyroid cancer. Presentation Date: Saturday, June 17, 2023
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".