Association Between BRAF V600E Allele Frequency and Aggressive Behavior in Papillary Thyroid Microcarcinoma
Bibliographic record
Abstract
Background/Objectives: Papillary thyroid microcarcinoma (MPTC), a subset of papillary thyroid carcinoma (PTC), is increasingly detected with advanced imaging. While most MPTCs are indolent, some exhibit aggressive behavior, complicating clinical management. The BRAF V600E mutation, common in PTC, is linked to aggressive features, and its allele frequency (AF) may serve as a biomarker for tumor aggressiveness. This study explored the association between BRAF V600E AF and aggressive histopathological features in MPTC. Methods: Data from 1 January 2016 to 23 December 2023 were retrieved from two McGill University teaching hospitals. Inclusion criteria comprised patients aged ≥ 18 years with thyroid nodules ≤ 1 cm, documented BRAF V600E mutation and AF results, and available surgical pathology reports. Tumor aggressiveness was defined as the presence of lymph node metastasis, aggressive histological subtype (tall cell, hobnail, columnar, solid/trabecular or diffuse sclerosing), extra thyroidal extension, or extensive lymphovascular extension. Associations were explored using t-tests. Results: Among 1564 records, 34 met the inclusion criteria and were included in analyses. The mean BRAF V600E AF was significantly higher in aggressive tumors (23.58) compared to non-aggressive tumors (13.73) (95% CI: −18.53 to −1.16, p = 0.03). Although not statistically significant, trends were observed for higher BRAF V600E AF in tumors with lymph node metastasis (mean AF: 25.4) compared to those without (mean AF: 16.67, p = 0.08). No significant difference was noted in BRAF V600E AF by histological subtype (mean AF for aggressive: 19.57; non-aggressive: 19.15, p = 0.94). Conclusions: Elevated BRAF V600E AF is associated with aggressive behavior in MPTC, highlighting its potential as a biomarker to inform treatment strategies. Larger studies are warranted to validate these findings and enhance clinical management of MPTC patients.
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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 | 0.000 |
| 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".