The diagnostic performance of neck ultrasound in follow-up of advanced stage differentiated thyroid cancer
Bibliographic record
Abstract
BACKGROUND: Differentiated thyroid cancer (DTC) requires long-term follow-up due to the risk of delayed recurrence. Follow-up surveillance involves serial neck ultrasound (US) and thyroglobulin (Tg); however, the optimal frequency and diagnostic performance of neck US outside of specialized thyroid cancer centres in higher risk patients is not well defined. We sought to evaluate the diagnostic performance of US and serial Tg in advanced stage DTC. METHODS: We retrospectively reviewed our thyroid cancer database for patients with stage III and IV DTC from 2006 to 2018, total thyroidectomy, and at least 2 years follow-up to assess recurrence rates. Those with hemi-thyroidectomy or anti-Tg antibodies were excluded. Diagnostic performance of US and Tg were assessed using a composite reference standard of follow-up imaging and pathology. All relevant US were reviewed by a blinded expert radiologist for uniformity. RESULTS: Of 136 included patients (91 females, mean age 58.9), 26 (19%) had recurrence of DTC over median follow-up of 6.6 years (IQR 5.3-9.3). The sensitivity and specificity of US in diagnosing cervical recurrence were 73.3% (95% CI 0.51-0.96) and 68.3% (95% CI 0.60-0.77) based on historical reports, respectively, and 80% (95% CI 0.60-1.00) and 87.8% (95% CI 0.82-0.93) based on blinded expert review, respectively. Tg had a sensitivity of 95.5% (95% CI 0.89-1.0) and specificity of 96.2% (95% CI 0.92-0.99) in detecting cervical recurrence or distant metastases. False positive US findings on historical US and subsequent review occurred in 38 (28%) and 15 (11%) patients, respectively, while 5 (3.6%) had false positive Tg results. CONCLUSION: Serial Tg has better sensitivity and specificity than US for detecting recurrence of advanced stage DTC. Furthermore, re-interpretation of abnormal findings using structured US reporting with a subspecialized reader may improve diagnostic performance of US and improve its utility in clinical care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".