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Dynamic Whole-body [F]FES PET/CT Increases Lesion Visibility in Patients with Metastatic Breast Cancer

Abstract

Background: Correct classification of estrogen receptor (ER) status is essential for prognosis and treatment planning in patients with breast cancer (BC). Therefore, it is recommended to sample tumor tissue from an accessible metastasis. However, ER expression can show intra- and intertumoral heterogeneity. 16α-[F]fluoroestradiol ([F]FES) Positron Emission Tomography/Computed Tomography (PET/CT) allows noninvasive whole-body (WB) identification of ER distribution and is usually performed as a single static image 60 min after radiotracer injection. Using dynamic whole-body (D-WB) PET imaging, we examine [F]FES kinetics and explore whether Patlak parametric images ( ) are quantitative and improve lesion visibility.

Results: This prospective study included eight patients with metastatic ER-positive BC scanned using a D-WB PET acquisition protocol. The kinetics of [F]FES were best characterized by the irreversible two-tissue compartment model in tumor lesions and in the majority of organ tissues. values from Patlak parametric images correlated with values from the full kinetic analysis, r = 0.77, and with the semiquantitative mean standardized uptake value (SUV), r = 0.91. Furthermore, parametric images had the highest target-to-background ratio (TBR) in 162/164 metastatic lesions and the highest contrast-to-noise ratio (CNR) in 99/164 lesions compared to conventional SUV images. TBR was 2.45 (95% confidence interval (CI): 2.25-2.68) and CNR 1.17 (95% CI: 1.08-1.26) times higher in images compared to SUV images. These quantitative differences were seen as reduced background activity in the images.

Conclusion: [F]FES uptake is best described by an irreversible two-tissue compartment model. D-WB [F]FES PET/CT scans can be used for direct reconstruction of parametric images, with superior lesion visibility and values comparable to values found from full kinetic analyses. This may aid correct ER classification and treatment decisions. Trial registration ClinicalTrials.gov: NCT04150731, https://clinicaltrials.gov/study/NCT04150731.

Citing Articles

Prediction of the axillary lymph-node metastatic burden of breast cancer by F-FDG PET/CT-based radiomics.

Li Y, Han D, Shen C BMC Cancer. 2024; 24(1):704.

PMID: 38849770 PMC: 11161959. DOI: 10.1186/s12885-024-12476-3.

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