# 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 Study

## TL;DR

This study shows that PET-CT metabolic parameters can help predict how long patients with a specific type of thyroid cancer will remain free of disease progression.

## Contribution

The study identifies TgDT as the strongest predictor of progression-free survival in RR-DTC patients using PET-CT metabolic parameters.

## Key findings

- TgDT ≤ 100 days was the sole predictor of reduced progression-free survival in multivariate analysis.
- Higher SUVmax and TLV were associated with worse progression-free survival in patients with metastatic disease.
- Kaplan-Meier analysis confirmed that elevated metabolic parameters and Tg levels correlated with worse outcomes.

## 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.