Molecular alteration patterns predict tumor behavior in papillary thyroid carcinoma independent of tumor size: insights from an international multicenter retrospective study
Bibliographic record
Abstract
BACKGROUND: Molecular testing is a well-established tool that assists in the management of thyroid nodules and allows classification in distinct molecular alteration patterns: BRAF-like, RAS-like and non-BRAF-non-RAS (NBNR). Yet classical TNM classification and ATA guidelines currently rely on tumor size for risk stratification. In this study, we compared tumor behavior according to molecular alteration patterns versus tumor size. METHODS: Retrospective multicenter multinational study of thyroid nodules that underwent preoperative molecular profiling with ThyGenX/ThyGeNEXT or ThyroSeq V3 between 2015 and 2022. Clinical characteristics, including demographics, cytology results, tumor size, surgical pathology, and molecular alterations, were analyzed. RESULTS: The study included 718 patients who underwent surgery for papillary thyroid cancer, with a majority of 556 (77.4%) being female. The distribution of molecular alteration patterns was as follows: BRAF-like in 227 (31.6%), RAS-like in 171 (23.8%), NBNR in 59 (8.2%), BRAF/RAS overlap 8 (1.1%) and no detectable mutation in 224 (31.2%) cases. The median tumor size was 15 mm (IQR 10-24). Extrathyroidal extension (ETE) was observed in 6.2% of cases with gross ETE and 5.6% with minimal ETE. Notably, nodules with BRAF-like molecular alterations were more likely to exhibit ETE compared to those with RAS-like or NBNR alterations (P < 0.001). There was no significant correlation between ETE and median tumor size (P > 0.05). CONCLUSION: Molecular testing of thyroid nodules provides a more accurate prediction of tumor behavior compared to tumor size alone. These findings suggest that future staging systems could benefit from incorporating molecular alteration patterns into their algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".