Prognostic Indicators of EIF1AX-Mutated Thyroid Tumor Malignancy and Cancer Aggressiveness
Bibliographic record
Abstract
The risk of malignancy (ROM) of EIF1AX-mutated thyroid nodules has been theorized to be contingent on the position of the mutation within the gene and the presence of co-existing mutations. However, due to EIF1AX’s low mutation frequency, sample sizes currently reported in the literature are too diminutive to appraise the clinical utility of molecular diagnostic testing. The objective of this study was to elucidate prognostic indicators of EIF1AX-mutated thyroid tumors and cancer aggressiveness by examining a large cohort of cytologically indeterminate thyroid nodules (CITNs) that underwent molecular testing and subsequent surgical resection. This is a multicenter study involving 764 subtotal and total thyroidectomy patients that underwent preoperative molecular testing at two quaternary care hospitals. A five-year retrospective review was performed on the 42 charts of patients that opted for surgery following a positive EIF1AX mutation on ThyroseqV3 results from January 2018 to May 2022. Patient demographics, cytopathology results, molecular testing results, and postoperative histopathology were reviewed. Of the 42 surgically resected nodules that harbored an EIF1AX mutation, 16 (38.1%) were benign, six (14.3%) were non-invasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTPs) or well-differentiated thyroid neoplasms of uncertain malignant potential (WDT-UMPs), and 20 (47.6%) were malignant. An isolated EIF1AX mutation conferred a ROM of 47.6%, whereas the ROM for nodules with at least one additional molecular alteration was 72.7%. The ROM increased to 100% for nodules with at least one additional molecular alteration and the A113_splice site mutation. Six malignant nodules were aggressive, with five having variegated components of poorly differentiated thyroid carcinoma (PDTC). EIF1AX-mutated thyroid nodules are more susceptible to malignancy in the presence of the A113_splice site mutation and when co-mutated with RAS and/or TP53. This deleterious amalgam is associated with aggressive disease and renders these nodules PDTC. A preoperative molecular test finding of an EIF1AX mutation can be a useful tool for thyroid specialists to optimize clinical 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 | 0.001 | 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) | 0.001 | 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".