Malignancy Risk, Molecular Mutations, and Surgical Outcomes of Thyroid Nodules Classified as Atypia of Undetermined Significance in the Bethesda System: A Comprehensive Analysis
Bibliographic record
Abstract
OBJECTIVES: Thyroid nodules classified as atypia of undetermined significance (AUS) within the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) present a diagnostic challenge, with a risk of malignancy (ROM) of 5% to 50%. In 2017, TBSRTC introduced AUS subcategories to enhance ROM assessment. This study explores the correlation between AUS subclassification, molecular mutations, and surgical outcomes. METHODS: Retrospective analysis was performed of 114 AUS cases with molecular profiling by ThyroSeqV3 and surgical follow-up. AUS subcategories as defined by TBSRTC included: AUS-Architectural, AUS-Nuclear, AUS-Nuclear and Architectural, and AUS-Hürthle cell. Pathology diagnoses were categorised as benign, malignant, or borderline, including noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). RESULTS: Of the 114 nodules, 32.5% were AUS-Architectural, 28.9% AUS-Nuclear and Architectural, 18.4% AUS-Nuclear, 19.3% AUS-Hürthle cell, and 0.9% AUS-Not Otherwise Specified. Papillary carcinoma, predominantly follicular variant, was the most common diagnosis (47.4%), followed by benign lesions (34.2%) and NIFTP (9.6%). RAS family mutations were the most prevalent molecular alteration (34.2%) followed by DICER1, EIF1AX, EXH1 mutations, CNA and GEP (29.8%). THADA fusions, PTEN, TSHR and BRAFK601E mutations were identified in 10.5% of cases, while high-risk mutations such as BRAF V600E, TERT, and TP53 were found in 8.8% of cases. AUS subcategories demonstrated distinct molecular profiles and were linked to varying surgical outcomes. CONCLUSIONS: AUS subcategorization is associated with specific molecular profiles and surgical outcomes, supporting the subclassification of AUS cases per TBSRTC guidelines for improved risk stratification and clinical management. Further prospective studies with larger cohorts are necessary for validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".