The Role of Metabolic Parameters of 18F-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (18F-FDG PET-CT) Imaging in Predicting Progression-Free Survival of Radioiodine-Refractory Differentiated Thyroid Cancer: A Single-Center Stud
Bibliographic record
Abstract
INTRODUCTION: Most cases of thyroid cancer are differentiated thyroid cancers, which typically have a high survival rate due to the effectiveness of radioactive iodine (RAI) therapy. However, a subset of these cancers, known as radioactive iodine-refractory differentiated thyroid cancer (RR-DTC), is resistant to RAI and is associated with lower survival rates, necessitating alternative therapeutic approaches. As RR-DTC develops, there is an increase in glucose utilization and metabolic activity of the tumor. The technique of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) is well-known for assessing the metabolic activity of tumors, and in this case, the RR-DTC. This study explores the relationship between 18F-FDG PET/CT imaging and associated metabolic parameters of RR-DTC to progression-free survival (PFS). METHODS: A retrospective analysis was performed on 22 patients diagnosed with RR-DTC who underwent 18F-FDG PET-CT imaging between 2010 and 2021. Metabolic PET parameters, including total lesion volume (TLV), total lesion glycolysis (TLG), maximum standardized uptake value (SUVmax), and the biomarker thyroglobulin (Tg), along with thyroglobulin doubling time (TgDT), were extracted and analyzed for potential associations with PFS. Means and standard deviations (SD) were reported for continuous variables, and percentages for categorical variables. Student's t-test and Fisher's exact test were used to compare imaging parameters and biomarker variables between patients with and without disease progression. Progression-free survival (PFS) was evaluated using univariate and multivariate Cox proportional hazards models, and the Kaplan-Meier method with log-rank test was used to assess the impact of various variables on PFS. All statistical analyses were performed using SPSS software version 28.1.1, with a two-sided significance level set at P < 0.05. RESULTS: The patients' ages ranged from 38 to 83 years, 15 out of 22 (68%) were male, and 13 out of 22 (59.1%) exhibited distant metastases. The follow-up period varied from 21 to 452 months; the median follow-up was 32 months, and the mean follow-up was 116 months. Of the 22 patients, 11 (50%) demonstrated disease progression, with a mean time-to-progression of 74 months. The mean SUVmax and TLV were higher in patients with metastatic disease compared to those with localized disease in surgical beds and regional lymph nodes (p-values of 0.045 and 0.01, respectively). Univariate Cox analysis revealed that SUVmax > 10 had a hazard ratio (HR) of 4.97 (CI: 1.39-17.8, p-value = 0.014), TLV > 5 had an HR of 11.6 (CI: 2.51-53.4, p-value = 0.002), Tg > 10 had an HR of 5.70 (CI: 1.44-22.6, p-value = 0.013), and TgDT ≤ 100 days had an HR of 17.9 (CI: 1.89-161.8, p-value = 0.01), all correlated with worse PFS. Multivariate Cox analysis demonstrated that TgDT ≤ 100 days with an HR 63.9 (CI: 9.33- 743, p-value=0.02) was the sole predictor of reduced PFS. Kaplan-Meier analysis showed that SUVmax >10, TLG >10, Tg > 10, and TgDT ≤ 100 days corresponded to worse PFS, and TgDT ≥300 days corresponded to best PFS. CONCLUSION: In this data set, the metabolic parameters obtained from PET-CT imaging are predictive for PFS in RR-DTC patients when used with other imaging and biomarkers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".