Quantification of I-131 thyroid remnant uptake in patients with thyroid cancer
Bibliographic record
Abstract
Radioiodine ablation is commonly performed after thyroidectomy for well-differentiated thyroid cancer (DTC).This study aimed to quantify thyroid remnant uptake in standardized uptake values (SUV) and evaluate its correlation with post-therapy Thyroglobulin (Tg) levels across different risk groups.We retrospectively quantified SUV uptake with attenuation, scatter, and resolution recovery corrections on post-therapy SPECT/CT in thyroid cancer patients referred to our centre between 2015 and 2017.Thyroid remnant was segmented with a maximum SUV of 0.5 as the threshold and total thyroid remnant uptake (SUV total ) was obtained.Patients were stratified into low-intermediate, high-intermediate, and high-risk groups based on clinical risk and therapeutic dose.The primary outcome was the correlation between SUV total and post-therapy Tg levels.The cohort consisted of 174 adults (age: 50.7±16.0yr, F:M=110:64).Moderate correlations were found between SUV total and Tg levels in low-intermediate and high-intermediate groups (Spearman's ρ=0.65,P<0.001; ρ=0.61,P<0.001, respectively).No significant correlation was found in the high-risk group (ρ=0.12,P=0.33).Stimulated Tg levels increased (median Tg: 4, 7, and 13 pmol/L) and thyroid remnant uptake decreased (median SUV total : 272, 51, and 33) across the low-intermediate, high-intermediate, and high risk groups.In conclusion, this study shows good correlations between the thyroid remnant uptake and thyroglobulin in subgroups of patients with low-intermediate and high-intermediate risk DTC.The rationales for lack of significant correlation in the high-risk group DTC were discussed.Thyroid uptake quantification may serve as a feasible substitute for Tg measurements in post-ablation follow-up, offering potential for predicting disease recurrence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".