Diagnostic Performance of FAPI PET/CT vs. 18F-FDG PET/CT in Evaluation of Liver Tumors: A Systematic Review and Meta-analysis.

Objectives: Primary liver tumors constitute one of the most common tumors. These are aggressive tumors with poor survival. Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT), most commonly used functional imaging, shows limited tracer retention and poor tumor to background ratios (TBR). Novel 68Ga-fibroblast-activation-protein inhibitor (FAPI) PET/CT has shown better tracer uptake and detection efficacy in liver tumors. However, most of the available literature is limited to single center studies with limited number of patients. So, we tried to review and analyze the head-to-head comparison of 18F-FDG PET/CT and 68Ga-FAPI PET/CT in evaluation of liver tumors. Methods: Literature available on head to head comparison of diagnostic accuracy of 18F-FDG PET/CT and 68Ga-FAPI PET/CT was searched in databases like PubMed, SCOPUS, EMBASE and Google Scholar for published original studies till April 2023. The relevant studies were selected and assessed using the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies-2 checklist. A random-effect model was used for calculating pooled sensitivity and specificity. They were represented with 95% confidence intervals (95% CI) and demonstrated in Forest plots. I-square statistic was used to assess heterogeneity in the studies. Results: Pooled sensitivity and specificity of FAPI PET/CT and 18F-FDG PET/CT for detection of primary liver tumors was 94.3% (95% CI: 90.6-96.8%); 89.3% (95% CI: 71.8-97.7%) and 56.1% (95% CI: 49.7-62.5%); 96.4% (95% CI: 81.7-99.9%) respectively. Pooled sensitivity for detection of extrahepatic metastatic disease was 92.2% (range: 88.1-100%; 95% CI: 87.8-95.4%) and 72.4% (range: 69.8-76.5; 95% CI: 65.9-78.2%) respectively. Also, the maximum standardized uptake value (SUVmax) and TBR were higher for FAPI PET/CT than 18F-FDG PET/CT in the included studies. Conclusion: Overall, FAPI PET/CT showed higher sensitivity for detection of liver tumors with better SUVmax and TBR than 18F-FDG PET/CT.

An Uncommon Case of Pediatric Nasopharyngeal Carcinoma with Bone Metastases and Enchondromas Evaluated Using 18F-FDG PET/CT.

Nowadays, the utility of positron emission tomography/computed tomography (PET/CT) is well established in nasopharyngeal carcinoma (NPC). The incidence of NPC in the West population, especially in children, is very low. We present the first Italian case of a pediatric patient with NPC followed up with 18F-fluorodeoxyglucose (18F-FDG) PET/CT scan in addition to the standard follow-up imaging methods, including CT and magnetic resonance imaging. The 18F-FDG PET/CT scan was helpful in discriminating between metastatic and benign osseous lesions, thereby helping clinicians to determine the most appropriate therapeutic regimen. These findings support the clinical utility of 18F-FDG PET/CT in the diagnostic work-up of pediatric patients with NPC.

CT, MRI, and PET/CT imaging features of thoracic spine epithelioid hemangioma: a retrospective observational study.

Introduction: Epithelioid hemangioma (EH) is an intermediate locally aggressive tumor that consists of epithelioid cells and endothelial cell differentiation, which can occur at any age, but is most common between the ages of 30 and 40 years. EH in the thoracic spine is rare, and accurate diagnosis is critical to treatment planning. Our aim was to explore the imaging and clinical data of thoracic spine EH to improve the understanding of this rare disease. Methods: From January 1, 2018 to June 30, 2023, a database of thoracic spine masses was retrospectively reviewed. Five patients with histologically proven thoracic spine EH and complete imaging available were identified and analyzed. Computed tomography (CT) and magnetic resonance imaging (MRI) findings were evaluated separately by two radiologists with more than 10 years of experience. Positron emission tomography (PET)/CT was conducted by two nuclear medicine diagnostic technologists with at least 5 years of experience. Results: The patients included three male and two female patients aged 23 to 56 years (mean age was 38.4 ± 14.3 years). All patients underwent CT, MRI, and 18F-FDG PET/CT examination before treatment. Four patients were limited to one vertebral involvement, only one patient had multiple vertebral involvement, and all tumors involved the accessories, including one involving the posterior ribs. The maximum diameter of the tumor ranged from 2.7 to 4.3. Conclusions: CT, MRI, and 18F-FDG PET/CT findings of thoracic spine EH have certain characteristics, and understanding these imaging findings will help to obtain accurate diagnosis before surgery.

Comparison of digital and analog [68Ga]Ga-PSMA-11 PET/CT for detecting post-prostatectomy biochemical recurrence in prostate cancer patients: a prospective study.

Digital positron emission tomography/computed tomography (PET/CT) has shown enhanced sensitivity and spatial resolution compared with analog PET/CT. The present study compared the diagnostic performance of digital and analog PET/CT with [68Ga]Ga-PSMA-11 in prostate cancer patients who experienced biochemical recurrence (BCR) after prostatectomy. Forty prostate cancer patients who experienced BCR, defined as serum prostate-specific antigen (PSA) concentrations exceeding 0.2 ng/mL after prostatectomy, were prospectively recruited. These patients were stratified into three groups based on their serum PSA levels. [68Ga]Ga-PSMA-11 was injected into each patient, and images were acquired using both analog and digital PET/CT scanners. Analog and digital PET/CT showed comparable lesion detection rate (71.8% vs. 74.4%), sensitivity (85.0% vs. 90.0%), and positive predictive value (PPV, 100.0% vs. 100.0%). However, digital PET/CT detected more lesions (139 vs. 111) and had higher maximum standardized uptake values (SUVmax, 14.3 vs. 10.3) and higher kappa index (0.657 vs. 0.502) than analog PET/CT, regardless of serum PSA levels. On both analog and digital PET/CT, lesion detection rates and interrater agreement increased with increasing serum PSA levels. Compared with analog PET/CT, digital PET/CT detected more lesions with a higher SUVmax and better interrater agreement in prostate cancer patients who experienced BCR after prostatectomy.

Can FDG-PET Imaging Identify Cardiac Sarcoidosis Disease Phenotypes?

PURPOSE OF REVIEW: Despite the scarcity of data, most guidelines have advocated for the treatment of cardiac sarcoidosis with corticosteroids. However, there is heterogeneity in disease presentation and response to treatment, which can make treatment challenging. The ability to identify disease phenotypes to allow for tailored therapy is therefore highly desirable. This review will seek to outline the disease phenotypes of cardiac sarcoidosis and the role that FDG-PET imaging can play in identifying these phenotypes to optimize disease diagnosis and treatment management. RECENT FINDINGS: FDG PET can identify cardiac sarcoidosis and is being increasingly used to monitor therapeutic response to immunosuppressive therapy, to follow treatment response after discontinuation of corticosteroid therapy, and to evaluate for disease relapse. Modern quantitative techniques using FDG PET imaging may allow for even better phenotypic disease characterization and the ability to track the response to immunosuppression more accurately. FDG PET currently plays an important role in cardiac sarcoidosis diagnosis. However, it also affords us the opportunity to offer insights into cardiac sarcoidosis disease phenotypes to better understand the underlying disease process and in the future may allows us to tailor therapies accordingly.

The mitochondrial translocator protein (TSPO) in Alzheimer's disease: Therapeutic and immunomodulatory functions.

The translocator protein (TSPO) has been widely investigated as a PET-imaging biomarker of neuroinflammation and, more recently, as a therapeutic target for the treatment of neurodegenerative disease. TSPO ligands have been shown to exert neuroprotective effects in vivo and in vitro models of Alzheimer's disease (AD), by reducing toxic beta amyloid peptides, and attenuating brain atrophy. Recent transcriptomic and proteomic analyses, and the generation of TSPO-KO mice, have enabled new insights into the mechanistic function of TSPO in AD. Using a multi-omics approach in both TSPO-KO- and TSPO ligand-treated mice, we have demonstrated a key role for TSPO in microglial respiratory metabolism and phagocytosis in AD. In this review, we discuss emerging evidence for therapeutic and immunomodulatory functions of TSPO in AD, and new tools for studying TSPO in the brain.

Pilot study of humanized glypican-3-targeted zirconium-89 immuno-positron emission tomography for hepatocellular carcinoma.

Purpose: Glypican-3 (GPC3)-targeted radioisotope immuno-positron emission tomography (immunoPET) may lead to earlier and more accurate diagnosis of hepatocellular carcinoma (HCC), thus facilitating curative treatment, decreasing early recurrence, and enhancing patient survival. We previously demonstrated reliable HCC detection using a zirconium-89-labeled murine anti-GPC3 antibody (89Zr-αGPC3M) for immunoPET. This study evaluated the efficacy of the humanized antibody successor (αGPC3H) to further clinical translation of a GPC3-based theranostic for HCC. Methods: In vitro αGPC3 binding to HepG2 cells was assessed by flow cytometry. In vivo 89Zr-αGPC3H and 89Zr-αGPC3M tumor uptake was evaluated by PET/CT and biodistribution studies in an orthotopic xenograft mouse model of HCC. Results: αGPC3H maintained binding to GPC3 in vitro and 89Zr-αGPC3H immunoPET identified liver tumors in vivo. PET/CT and biodistribution analyses demonstrated high 89Zr-αGPC3H tumor uptake and tumor-to-liver ratios, with no difference between groups. Conclusion: Humanized αGPC3 successfully targeted GPC3 in vitro and in vivo. 89Zr-αGPC3H immunoPET had comparable tumor detection to 89Zr-αGPC3M, with highly specific tumor uptake, making it a promising strategy to improve HCC detection.

Advances in Clinical Imaging of Vascular Inflammation: A State-of-the-Art Review.

Vascular inflammation is a major contributor to cardiovascular disease, particularly atherosclerotic disease, and early detection of vascular inflammation may be key to the ultimate reduction of residual cardiovascular morbidity and mortality. This review paper discusses the progress toward the clinical utility of noninvasive imaging techniques for assessing vascular inflammation, with a focus on coronary atherosclerosis. A discussion of multiple modalities is included: computed tomography (CT) imaging (the major focus of the review), cardiac magnetic resonance, ultrasound, and positron emission tomography imaging. The review covers recent progress in new technologies such as the novel CT biomarkers of coronary inflammation (eg, the perivascular fat attenuation index), new inflammation-specific tracers for positron emission tomography-CT imaging, and others. The strengths and limitations of each modality are explored, highlighting the potential for multi-modality imaging and the use of artificial intelligence image interpretation to improve both diagnostic and prognostic potential for common conditions such as coronary artery disease.

Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET.

BACKGROUND: Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) - a locally adaptive image filter - allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering. METHODS: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( [ 18 F ] FDG, [ 18 F ] L-DOPA, [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the "optimal" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs. RESULTS: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUV max values of the unfiltered images with a low mean ± SD difference of δ SUV max CNN , STD = (-3.9 ± 5.2)% and δ SUV max BF , STD = (-4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUV max values in the vast majority of cases with an overall average difference of δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed. CONCLUSIONS: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

Evaluation of ComBat harmonization for reducing across-tracer biases in regional amyloid PET analyses.

Background: Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-ß uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods: One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Intraclass correlation coefficient (ICC) and mean standardized absolute error (MsAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results: In the head-to-head tracer comparison, the best ICC and MsAE were achieved after harmonizing with ComBat with no covariates for the global summary SUVR. ComBat with no covariates also performed the best in harmonizing regional SUVRs. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FPB-to-PiB proportions. Conclusions: ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.

PET Imaging in Chimeric Antigen Receptor T-Cell Trafficking.

The evolving field of chimeric antigen receptor (CAR) T-cell therapy, though promising, necessitates more comprehensive imaging methods to enhance therapeutic effectiveness and track cell trafficking in patients and ex vivo. This review examines the application of PET imaging in CAR T-cell trafficking and optimizing their therapeutic impact. The application of PET imaging using various radiotracers is promising in providing evaluation of CAR T-cell interaction within the host, thereby facilitating strategies for improved patient outcomes. As this technology progresses, further innovative strategies to streamline assessments of immunotherapeutic effectiveness are anticipated.

Spatial extent as a sensitive amyloid-PET metric in preclinical Alzheimer's disease.

INTRODUCTION: Spatial extent-based measures of how far amyloid beta (Aß) has spread throughout the neocortex may be more sensitive than traditional Aß-positron emission tomography (PET) measures of Aß level for detecting early Aß deposits in preclinical Alzheimer's disease (AD) and improve understanding of Aß's association with tau proliferation and cognitive decline. METHODS: Pittsburgh Compound-B (PIB)-PET scans from 261 cognitively unimpaired older adults from the Harvard Aging Brain Study were used to measure Aß level (LVL; neocortical PIB DVR) and spatial extent (EXT), calculated as the proportion of the neocortex that is PIB+. RESULTS: EXT enabled earlier detection of Aß deposits longitudinally confirmed to reach a traditional LVL-based threshold for Aß+ within 5 years. EXT improved prediction of cognitive decline (Preclinical Alzheimer Cognitive Composite) and tau proliferation (flortaucipir-PET) over LVL. DISCUSSION: These findings indicate EXT may be more sensitive to Aß's role in preclinical AD than level and improve targeting of individuals for AD prevention trials. HIGHLIGHTS: Aß spatial extent (EXT) was measured as the percentage of the neocortex with elevated Pittsburgh Compound-B. Aß EXT improved detection of Aß below traditional PET thresholds. Early regional Aß deposits were spatially heterogeneous. Cognition and tau were more closely tied to Aß EXT than Aß level. Neocortical tau onset aligned with reaching widespread neocortical Aß.

[68Ga]Ga­PSMA­617 PET-based radiomics model to identify candidates for active surveillance amongst patients with GGG 1-2 prostate cancer at biopsy.

PURPOSE: To develop a radiomics-based model using [68Ga]Ga-PSMA PET/CT to predict postoperative adverse pathology (AP) in patients with biopsy Gleason Grade Group (GGG) 1-2 prostate cancer (PCa), assisting in the selection of patients for active surveillance (AS). METHODS: A total of 75 men with biopsy GGG 1-2 PCa who underwent radical prostatectomy (RP) were enrolled. The patients were randomly divided into a training group (70%) and a testing group (30%). Radiomics features of entire prostate were extracted from the [68Ga]Ga-PSMA PET scans and selected using the minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression model. Logistic regression analyses were conducted to construct the prediction models. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were employed to evaluate the diagnostic value, clinical utility, and predictive accuracy of the models, respectively. RESULTS: Among the 75 patients, 30 had AP confirmed by RP. The clinical model showed an area under the curve (AUC) of 0.821 (0.695-0.947) in the training set and 0.795 (0.603-0.987) in the testing set. The radiomics model achieved AUC values of 0.830 (0.720-0.941) in the training set and 0.829 (0.624-1.000) in the testing set. The combined model, which incorporated the Radiomics score (Radscore) and free prostate-specific antigen (FPSA)/total prostate-specific antigen (TPSA), demonstrated higher diagnostic efficacy than both the clinical and radiomics models, with AUC values of 0.875 (0.780-0.970) in the training set and 0.872 (0.678-1.000) in the testing set. DCA showed that the net benefits of the combined model and radiomics model exceeded those of the clinical model. CONCLUSION: The combined model shows potential in stratifying men with biopsy GGG 1-2 PCa based on the presence of AP at final pathology and outperforms models based solely on clinical or radiomics features. It may be expected to aid urologists in better selecting suitable patients for AS.

Correlations between plasma markers and brain Aß deposition across the AD continuum: Evidence from SILCODE.

BACKGROUND: Previous studies have found that Alzheimer's disease (AD)-related plasma markers are associated with amyloid beta (Aß) deposition, but the change of this association in different Aß pathological stages remains unclear. METHODS: Data were obtained from the SILCODE. According to the standardized uptake value ratio (SUVR) and Aß stage classification, correlation analysis was performed among plasma biomarkers, and voxel/SUVR values in the regions of interest (ROI) and clinical scale information, respectively. Mediation analysis was used to study the possible pathways. RESULTS: The proportion of cognitively normal (CN) and subjective cognitive decline (SCD) was the highest in stages A0 to 1, while in stages A2 to 4, the proportion of mild cognitive impairment (MCI) and AD increased. Plasma phosphorylated tau (p-tau)181 and glial fibrillary acidic protein (GFAP) levels were significantly lower in stage A0 compared to the later phases. Two pathways demonstrated fully mediated effects: positron emission tomography (PET) SUVR-plasma p-tau181-Mini-Mental State Examination (MMSE) and PET SUVR-plasma GFAP-MMSE. DISCUSSION: This study demonstrated the role of plasma biomarkers in the early stage of AD, especially in SCD, from both the clinical diagnosis and Aß stage dimensions. HIGHLIGHTS: Plasma ptau181 and GFAP level serve as indicators of early Alzheimer's disease and the pathologic Aß staging classification. A possible ceiling effect of GFAP was observed in the mid-to-late stages of the AD course. This study confirms the role of AD plasma markers in promoting Aß deposition at an early stage, particularly in females with subjective cognitive decline(SCD). The overlapping brain regions of plasma p-tau181, GFAP, and neurofilament light for Aß deposition in the brain in early AD were distributed across various regions, including the posterior cingulate gyrus, rectus gyrus, and inferior temporal gyrus.

Proton spot dose estimation based on positron activity distributions with neural network.

BACKGROUND: Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning-based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy. PURPOSE: This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U-Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice. METHODS: Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one-dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics. RESULTS: The U-Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5 mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5 mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3 mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases. CONCLUSIONS: Both the U-Net and the Transformer have certain advantages in the proton dose estimation task. The U-Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose-guided proton therapy.

The value of 68Ga-PSMA PET/CT in the diagnosis of intracapsular prostate cancer with a poor prognosis.

OBJECTIVE: To evaluate the diagnostic value of 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) for intracapsular prostate cancer with a poor prognosis (PPC) and no extracapsular invasion or distant metastasis. METHODS: The PET/CT images and clinical data of 221 patients were retrospectively analyzed. These patients all had clear pathological results. The maximum standard uptake value (SUVmax) of the main lesions was measured at the postprocessing workstation and was tested for correlation with the pathological score. The diagnostic accuracy was calculated using the receiver operating characteristic (ROC) curve, and the best diagnostic threshold was calculated. The correlation between SUVmax and the International Society of Urological Pathology Grade Group (GG) was also analyzed. RESULTS: The pathological results of the 221 patients were 48 benign lesions and 173 malignant lesions, including 81 PPC. Low-, intermediate-, and high-risk prostate cancers made up 21.97% (38/173), 54.33% (94/173), and 23.70% (41/173) of the malignant lesions, respectively. SUVmax and GG were positively correlated (r = 0.54, P < 0.01). The best SUVmax thresholds for 68Ga-PSMA PET/CT for the diagnosis of intracapsular PC and PPC were 7.95 and 13.94, respectively; the specificities were 0.83 and 0.85, the negative predictive values were 0.55 and 0.87, and the areas under the ROC curves were 0.88 and 0.88, respectively. CONCLUSION: 68Ga-PSMA PET/CT has high specificity and NPV in the diagnosis of intracapsular PPC, but the sensitivity for the diagnosis of intracapsular low-risk PC is low, which may cause some cases to be undetected.

Dual threshold input receiver FPGA-only signal digitization method for time-of-flight positron emission tomography.

As silicon photomultiplier (SiPM)-based time-of-flight (TOF) positron emission tomography (PET) becomes popular, the need for sophisticated PET data acquisition (DAQ) systems is increasing. One promising solution to this challenge is the adoption of a field-programmable gate array (FPGA)-only signal digitization method. In this paper, we propose a new approach to efficiently implement an FPGA-only digitizer. We configured the input/output (IO) port of the FPGA to function as a dual-threshold voltage comparator through the use of simple passive circuitry and heterogeneous IO standards. This configuration overcomes the limitations of existing methods by allowing different threshold voltages for adjacent IO pins, effectively reducing routing complexity and lowering manufacturing costs. An FPGA-only digitizer was implemented by integrating the dual-threshold voltage comparator and FPGA-based time-to-digital converter. By combining the dual-threshold time-over-threshold (TOT) method and curve fitting, precise energy information could be obtained. The performance of the FPGA-only digitizer was assessed using a detector setup comprising a 3 × 3 × 20 mm3 LYSO scintillation crystal and a single pixel SiPM. Using the configured evaluation setup, an energy resolution of 12.5% and a time resolution of 146 ± 9 ps were achieved for a 20 mm scintillation crystal. The dual-threshold TOT implemented using the proposed method showed consistent linearity across an energy range of 100 keV to 600 keV. The proposed method is well-suited for the development of cost-effective DAQ systems in highly integrated TOF PET systems.

Combining poly-epitope MoonTags and labeled nanobodies for signal amplification in cell-specific PET imaging in vivo.

Immunorecognition provides an excellent basis for targeted imaging techniques covering a wide range from basic research to diagnostics and from single cells to whole organisms. Fluorescence- or radioisotope-labeled antibodies, antibody fragments or nanobodies enable a direct signal readout upon binding and allow for versatile imaging from microscopy to whole-body imaging. However, as the signal intensity directly correlates with the number of labeled antibodies bound to their epitopes (1:1 binding), sensitivity for low-expressing epitopes can be limiting for visualization. For the first time, we developed poly-epitope tags with multiple copies (1 to 7) of a short peptide epitope, specifically the MoonTag, that are recognized by a labeled nanobody and aimed at signal amplification in microscopy and cell-specific PET imaging. In transiently transfected HeLa cells or stably transduced A4573 cells we characterized complex formation and in vitro signal amplification. Indeed, using fluorescently and radioactively labeled nanobodies we found an approximately linear signal amplification with increasing numbers of epitope copies in vitro. To test the poly-epitope approach in vivo, A4573 tumor cells were injected subcutaneously into the shoulder of NSG mice, with A4573 tumor cells expressing a poly-epitope of 7 MoonTags on one side and WT cells on the other side. Using a [68Ga]-labeled NODAGA-conjugated MoonTag nanobody, we performed PET/CT imaging at day 8-9 after tumor implantation. Specific binding of a [68Ga]-labeled NODAGA-conjugated MoonTag nanobody was observed in 7xMoonTag tumors (1.7 ± 0.5%ID/mL) by PET imaging, showing significantly higher radiotracer accumulation compared to the WT tumors (1.1 ± 0.3%ID/mL; p < 0.01). Ex vivo gamma counter measurements confirmed significantly higher uptake in 7xMoonTag tumors compared to WT tumors (p < 0.001). In addition, MoonTag nanobody binding was detected by autoradiography which was spatially matched with histological analysis of the tumor tissues. In conclusion, we expect nanobody-based poly-epitope tag strategies to be widely applicable for multimodal imaging techniques given the advantageous properties of nanobodies and their amenability to genetic and chemical engineering.

Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68 DOTATATE PET/CT.

To evaluate a convolutional neural network's performance (nnU-Net) in the assessment of vascular contours, calcification and PET tracer activity using Ga-68 DOTATATE PET/CT. Patients who underwent Ga-68 DOTATATE PET/CT imaging over a 12-month period for neuroendocrine investigation were included. Manual cardiac and aortic segmentations were performed by an experienced observer. Scans were randomly allocated in ratio 64:16:20 for training, validation and testing of the nnU-Net model. PET tracer uptake and calcium scoring were compared between segmentation methods and different observers. 116 patients (53.5% female) with a median age of 64.5 years (range 23-79) were included. There were strong, positive correlations between all segmentations (mostly r > 0.98). There were no significant differences between manual and AI segmentation of SUVmean for global cardiac (mean ± SD 0.71 ± 0.22 vs. 0.71 ± 0.22; mean diff 0.001 ± 0.008, p > 0.05), ascending aorta (mean ± SD 0.44 ± 0.14 vs. 0.44 ± 0.14; mean diff 0.002 ± 0.01, p > 0.05), aortic arch (mean ± SD 0.44 ± 0.10 vs. 0.43 ± 0.10; mean diff 0.008 ± 0.16, p > 0.05) and descending aorta (mean ± SD < 0.001; 0.58 ± 0.12 vs. 0.57 ± 0.12; mean diff 0.01 ± 0.03, p > 0.05) contours. There was excellent agreement between the majority of manual and AI segmentation measures (r ≥ 0.80) and in all vascular contour calcium scores. Compared with the manual segmentation approach, the CNN required a significantly lower workflow time. AI segmentation of vascular contours using nnU-Net resulted in very similar measures of PET tracer uptake and vascular calcification when compared to an experienced observer and significantly reduced workflow time.