Molecular Mutations and Clinical Behavior in Bethesda III and IV Thyroid Nodules: A Comparative Study
Bibliographic record
Abstract
Background: Thyroid cancer is the most common endocrine malignancy, and accurate diagnosis is crucial for effective management. Fine needle aspiration cytology, guided by the Bethesda System for Reporting Thyroid Cytopathology, categorizes thyroid nodules into six categories, with Bethesda III and IV representing indeterminate diagnoses that pose significant challenges for clinical decision-making. Understanding the molecular profiles of these categories may enhance diagnostic accuracy and guide treatment strategies. Methods: This study retrospectively analyzed data from 217 patients with Bethesda III and IV thyroid nodules who underwent ThyroSeq v3 molecular testing followed by thyroid surgery at McGill University teaching hospitals. The analysis focused on the presence of specific molecular mutations, copy number alterations (CNAs), and gene expression profiles (GEPs) within these nodules. The relationship between these molecular findings and the clinico-pathological features of the patients was also examined. Results: This study identified notable differences in the molecular landscape of Bethesda III and IV thyroid nodules. Bethesda IV nodules exhibited a higher prevalence of CNAs and distinct GEPs compared to Bethesda III nodules. Interestingly, the BRAFV600E mutation was found exclusively in Bethesda III nodules, which correlated with more aggressive malignant behavior. These findings underscore the potential of molecular profiling to differentiate between the clinical behaviors of these indeterminate nodule categories. Conclusions: Molecular profiling, including the assessment of CNAs, GEPs, and specific mutations like BRAFV600E, provides valuable insights into the nature of Bethesda III and IV thyroid nodules. The distinct molecular characteristics observed between these categories suggest that such profiling could be instrumental in improving diagnostic accuracy and tailoring treatment approaches, ultimately enhancing patient outcomes in thyroid cancer management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".