Factors affecting thyroid nodule fine needle aspiration non‐diagnostic rates: a retrospective association study of 1975 thyroid biopsies
Bibliographic record
Abstract
BACKGROUND: Thyroid nodules are common in clinical practice, and it is important to distinguish benign nodules, the vast majority, from malignant ones. Non-diagnostic (ND) samples have the potential to delay or mis-diagnose or lead to unnecessary surgeries, and it is important to examine what factors influence the ND rate. Prior literature has suggested that the impact of bedside cytology on ND rate is dependent on the initial adequacy rate, whereby higher ND rates benefit most from bedside cytology. We aim to compare the impact of bedside adequacy review between specialist groups who perform high volume thyroid biopsies with low initial ND rates. METHODS: We reviewed the cytopathology results of 1975 thyroid nodule FNAs performed between January 1, 2017 to December 31, 2017 in a multi-centre Canadian city, and the corresponding histopathology reports of 340 resected nodules. Descriptive variables were used to describe the data along with chi-squared testing and univariate logistic regression. RESULTS: The FNA biopsies were performed by three different speciality groups, which differed by procedural volume: radiology performed the most at 1171, pathology performed 655 and surgery performed 103. We could not define the operator for 45 of the nodules. The ND rate was lowest in the speciality groups with highest procedural volume, 3.4 % in pathology and 8.3 % in radiology, compared to 37.9 % in surgery (p < 0.001). Completion of bedside cytology rapid onsite evaluation (ROSE) significantly reduced the ND rate from 16.7 to 4.2 % for all samples (p < 0.001). When ROSE was compared with non-ROSE within a high procedural group (radiology), it further reduced the ND rate from 12.5 to 5.1 % (p < 0.001). Of the 340 resected nodules, 10.7 % (18) were in the ND category, of which 28 % (5/18) of these were found to be malignant (4 papillary carcinoma and 1 lymphoma). CONCLUSIONS: The results from this study demonstrate that thyroid FNAs performed with bedside ROSE can significantly reduce the ND rate compared with non-ROSE, even in experienced groups with low initial ND rates. It is therefore imperative that care providers managing patients with thyroid nodules ensure that thyroid FNAs are referred to specialized individuals/groups who do high volume, and ideally with the use of bedside ROSE, whether provided by a cytotechnologist or a pathologist.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".