Background: [⁶⁸Ga]Ga-FAPI-46 is a novel positron emission tomography (PET) ligand that targets fibroblast activation protein (FAP) expression as FAP inhibitor (FAPI) and could already show promising results in several tumor entities. It could be demonstrated that an increased FAP expression correlates with tumor aggressivity in urothelial carcinoma (UC). Given the limited value of [¹⁸F]FDG in UC, [⁶⁸Ga]Ga-FAPI-46 could add diagnostic information in staging and response assessment in UC. We present the first data of [⁶⁸Ga]Ga-FAPI-46 PET imaging in a pilot cohort of UC patients evaluating uptake characteristics in metastases and primary tumors. Methods: Fifteen patients with UC prior to or after local treatment underwent [⁶⁸Ga]Ga-FAPI-46 PET/CT imaging for detection of metastatic spread. We compared the biodistribution in non-affected organs and tumor uptake of UC lesions by standard uptake value measurements (SUV_mean and SUV_max). Additionally, metastatic sites on PET were compared to its morphological correlate on contrast-enhanced computed tomography (CT). Results: Overall, 64 tumor sites were detected on PET and/or CT. The highest uptake intensity was noted at the primary site (SUV_max 20.8 (range, 8.1–27.8)) followed by lymph node metastases (SUV_max 10.6 (range, 4.7–29.1)). In 4/15 (26.7%) patients there were [⁶⁸Ga]Ga-FAPI-46-positive lesions that were missed on standard routine CT imaging. On the other hand, 2/15 patients had suspicious prominent bipulmonary nodules as well as pelvic lymph nodes previously rated as suspicious for metastatic spread on CT, but without increased FAPI expression; here histopathology excluded malignancy. Conclusion: [⁶⁸Ga]Ga-FAPI-46 PET shows distinctly elevated uptake in UC lesions. Therefore, the tracer has potential as a promising new biomarker in metastatic UC patients, as [⁶⁸Ga]Ga-FAPI-46 PET might improve detection of metastatic sites compared to CT alone. These findings highly emphasize larger studies investigating FAPI imaging in UC patients.

In renal cell carcinoma (RCC), accurate imaging methods are required for treatment planning and response assessment to therapy. In addition, there is an urgent need for new therapeutic options, especially in metastatic RCC. One way to combine diagnostics and therapy in a so-called theranostic approach is the use of radioligands directed against surface antigens. For instance, radioligands against prostate-specific membrane antigen (PSMA) have already been successfully used for diagnosis and radionuclide therapy of metastatic prostate cancer. Recent studies have demonstrated that PSMA is expressed not only in prostate cancer but also in the neovasculature of several solid tumors, which has raised hopes to use PSMA-guided theranostic approaches in other tumor entities, too. However, data on PSMA expression in different histopathological subtypes of RCC are sparse. Because a better understanding of PSMA expression in RCC is critical to assess which patients would benefit most from theranostic approaches using PSMA-targeted ligands, we investigated the expression pattern of PSMA in different subtypes of RCC on protein level. Immunohistochemical staining for PSMA was performed on formalin-fixed, paraffin-embedded archival material of major different histological subtypes of RCC (clear cell RCC (ccRCC)), papillary RCC (pRCC) and chromophobe RCC (cpRCC). The extent and intensity of PSMA staining were scored semi-quantitatively and correlated with the histological RCC subtypes. Group comparisons were calculated with the Kruskal–Wallis test. In all cases, immunoreactivity was detected only in the tumor-associated vessels and not in tumor cells. Staining intensity was the strongest in ccRCC, followed by cpRCC and pRCC. ccRCC showed the most diffuse staining pattern, followed by cpRCC and pRCC. Our results provide a rationale for PSMA-targeted theranostic approaches in ccRCC and cpRCC.

In dynamic brain positron emission tomography (PET) studies, acquiring a time series of images, typically lasting more than an hour, is necessary to derive pharmacokinetic parameters. Analytically, these parameters are estimated by establishing kinetic models such as compartment models that consist of sets of ordinary differential equations (ODE), and by fitting the sparse time-activity curve (TAC) of the tracer. Yet, these models are simplified approximations of highly complex underlying processes, and sufficient samples of TAC are required throughout the entire acquisition, which is not only impractical but also hindered by patient involuntary motion and intrinsic noise. Therefore, recovering samples in missing timeframes is often required, which, in practice, is achieved by interpolation or extrapolation. Here, we introduce a novel deep-learning-based method that utilizes neural ODE (N-ODE) to predict TAC in the extended timeframes by mimicking analytical method in a data-driven manner. By training N-ODE to solve and fit sets of ODE such that the solution replicates the observed TAC, the N-ODE converges to the functional shapes that best describe the underlying pharmacokinetic processes. We customized N-ODE to predict the full-dynamic images (12 frames, 60min), hence pharmacokinetic parameters, given limited early-frame images (7 to 9 frames, 20 to 30min). For proof of concept, the proposed N-ODE was applied to simulated and clinical 18F-PI-2620 brain PET. We demonstrated that the proposed N-ODE delivered promising performance, indicated by bias, variance, and mean absolute error as well as pharmacokinetic parameters such as rate constants, standardized uptake value ratio (SUVr), and binding potential (BPND).