Molecular mutations as a possible factor for determining extent of thyroid surgery
Bibliographic record
Abstract
BACKGROUND: Molecular testing of thyroid nodules is a diagnostic tool used to better understand the nature of thyroid nodules. The aim of this study is to better comprehend the relationship between specific mutations and aggressive behavior of the tumour as demonstrated on postoperative pathological analysis. METHODS: A retrospective chart review of 103 cases was performed. Included were patients who had undergone molecular testing using a panel that tests for 9 mutations (ThyGenX®) and were found to have malignant tumours. The following gene alterations were found pre-operatively in the nodules: BRAF V600E (n = 32), BRAF K601E (n = 4), NRAS (n = 11), HRAS (n = 4), KRAS (n = 3), RET/PTC1 rearrangement (n = 1), TERT promoter (n = 2), PAX8-PPARγ rearrangement (n = 1), and 45 cases where no mutation was detected. Aggressive behavior was defined by extra-thyroidal extension (ETE), lymph node metastasis (LN+), and the following variants of papillary thyroid carcinoma: tall cell, solid, diffuse sclerosing, columnar cell and hobnail. Chi-squared testing was performed to compare groups. RESULTS: The group with BRAF V600E, RET/PTC1 rearrangement, and TERT promoter mutations was associated with ETE 37.1%, and LN+ 45.7% of the time compared to 4.3 and 13.0% in the group with other mutations, and 4.4 and 4.4% in the group with no mutations (p-value 0.02, p-value < 0.001, p-value 0.006). In addition, the BRAF V600E, RET/PTC1 rearrangement, and TERT mutations group demonstrated tall cell variants (17.1%), columnar cell variants (5.7%), and hobnail variants (3%). The other mutations group demonstrated columnar cell variants (4.3%), and the no mutations group demonstrated solid variants (2.2%). CONCLUSIONS: In this study, BRAF V600E, RET/PTC1 rearrangement, and TERT mutations were associated with aggressive behaving thyroid malignancies as defined above. Molecular testing may be a useful method to anticipate aggressive tumour types and therefore assist in planning the extent and timing of surgery.
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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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".