Rate of malignancy for thyroid nodules with AUS/FLUS cytopathology in a tertiary care center – a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Thyroid nodules are stratified through fine-needle aspiration (FNA) and are often categorized using The Bethesda System for Reporting Thyroid Cytopathology, which estimates the risk of malignancy for six cytopathological categories. The atypia of undetermined significance (AUS) and follicular lesion of undetermined significance (FLUS) categories have varying malignancy rates reported in the literature which can range from 6 to 72.9%. Due to this heterogeneity, we assessed the malignancy rate and effectiveness of repeat FNA (rFNA) for AUS/FLUS thyroid cytopathology at our institution. METHODS: Electronic health records of patients with AUS/FLUS thyroid cytopathology on FNA at our center since the implementation of the Bethesda System on May 1, 2014-December 31, 2019 were retrospectively reviewed. Patient demographics, treatment pathway, and pathology results were collected. The treatment pathway of the nodules, the rFNA results, and the malignant histopathology results were reported. Malignancy rates were calculated as an upper and lower limit estimate. RESULTS: This study described 182 AUS/FLUS thyroid nodules from 177 patients. In total, 24 thyroid nodules were deemed malignant upon histopathology, yielding a final malignancy rate of 13.2-25.3%. All of the malignancies were variants of papillary thyroid carcinoma. The malignancy rate of the nodules which underwent resection without rFNA (21.5%) was lower than the malignancy rate of the nodules which underwent resection after rFNA (43.8%). 45.5% of the rFNA results were re-classified into more definitive categories. CONCLUSION: The malignancy rate of AUS/FLUS thyroid cytopathology at our center is in line with the risk of malignancy stated by the 2017 Bethesda System. However, our malignancy rate is lower than some other Canadian centers and approximately half of our rFNAs were re-classified, highlighting the importance of establishing center-specific malignancy and rFNA re-classification rates to guide treatment decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".