In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events.

Objective. In-beam positron emission tomography (PET) is a promising technology for real-time monitoring of proton therapy. Random coincidences between prompt radiation events and positron annihilation photon pairs can deteriorate imaging quality during beam-on operation. This study aimed to improve the PET image quality by filtering out the prompt radiation events.Approach. We investigated a prompt radiation event filtering method based on the accelerator radio frequency phase and assessed its performance using various prompt gamma energy thresholds. An in-beam PET prototype was used to acquire the data when the 70 MeV proton beam irradiated a water phantom and a mouse. The signal-to-background ratio (SBR) indicator was utilized to evaluate the quality of the PET reconstruction image.Main results. The selection of the prompt gamma energy threshold will affect the quality of the reconstructed image. Using the optimal energy threshold of 580 keV can obtain a SBR of 1.6 times for the water phantom radiation experiment and 2.0 times for the mouse radiation experiment compared to those without background removal, respectively.Significance. Our results show that using this optimal threshold can reduce the prompt radiation events, enhancing the SBR of the reconstructed image. This advancement contributes to more accurate real-time range verification in subsequent steps.

Avoid non-diagnostic EUS-FNA: a DNN model as a possible gatekeeper to distinguish pancreatic lesions prone to inconclusive biopsy.

OBJECTIVE: This work aimed to explore the utility of CT radiomics with machine learning for distinguishing the pancreatic lesions prone to non-diagnostic ultrasound-guided fine-needle aspiration (EUS-FNA). METHODS: 498 patients with pancreatic EUS-FNA were retrospectively reviewed [Development cohort: 147 pancreatic ductal adenocarcinoma (PDAC); Validation cohort: 37 PDAC]. Pancreatic lesions not PDAC were also tested exploratively. Radiomics extracted from contrast-enhanced CT was integrated with deep neural networks (DNN) after dimension reduction. The receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were performed for model evaluation. And, the explainability of the DNN model was analyzed by integrated gradients. RESULTS: The DNN model was effective in distinguishing PDAC lesions prone to non-diagnostic EUS-FNA (Development cohort: AUC = 0.821, 95% CI: 0.742-0.900; Validation cohort: AUC = 0.745, 95% CI: 0.534-0.956). In all cohorts, the DNN model showed better utility than the logistic model based on traditional lesion characteristics with NRI >0 (p < 0.05). And, the DNN model had net benefits of 21.6% at the risk threshold of 0.60 in the validation cohort. As for the model explainability, gray-level co-occurrence matrix (GLCM) features contributed the most averagely and the first-order features were the most important in the sum attribution. CONCLUSION: The CT radiomics-based DNN model can be a useful auxiliary tool for distinguishing the pancreatic lesions prone to nondiagnostic EUS-FNA and provide alerts for endoscopists preoperatively to reduce unnecessary EUS-FNA. ADVANCES IN KNOWLEDGE: This is the first investigation into the utility of CT radiomics-based machine learning in avoiding non-diagnostic EUS-FNA for patients with pancreatic masses and providing potential pre-operative assistance for endoscopists.

Is the radiomics-clinical combined model helpful in distinguishing between pancreatic cancer and mass-forming pancreatitis?

PURPOSE: To develop CT-based radiomics models for distinguishing between resectable PDAC and mass-forming pancreatitis (MFP) and to provide a non-invasive tool for cases of equivocal imaging findings with EUS-FNA needed. METHODS: A total of 201 patients with resectable PDAC and 54 patients with MFP were included. Development cohort: patients without preoperative EUS-FNA (175 PDAC cases, 38 MFP cases); validation cohort: patients with EUS-FNA (26 PDAC cases, 16 MFP cases). Two radiomic signatures (LASSOscore, PCAscore) were developed based on the LASSO model and principal component analysis. LASSOCli and PCACli prediction models were established by combining clinical features with CT radiomic features. ROC analysis and decision curve analysis (DCA) were performed to evaluate the utility of the model versus EUS-FNA in the validation cohort. RESULTS: In the validation cohort, the radiomic signatures (LASSOscore, PCAscore) were both effective in distinguishing between resectable PDAC and MFP (AUCLASSO = 0.743, 95% CI: 0.590-0.896; AUCPCA = 0.788, 95% CI: 0.639-0.938) and improved the diagnostic accuracy of the baseline onlyCli model (AUConlyCli = 0.760, 95% CI: 0.614-0.960) after combination with variables including age, CA19-9, and the double-duct sign (AUCPCACli = 0.880, 95% CI: 0.776-0.983; AUCLASSOCli = 0.825, 95% CI: 0.694-0.955). The PCACli model showed comparable performance to FNA (AUCFNA = 0.810, 95% CI: 0.685-0.935). In DCA, the net benefit of the PCACli model was superior to that of EUS-FNA, avoiding biopsies in 70 per 1000 patients at a risk threshold of 35%. CONCLUSIONS: The PCACli model showed comparable performance with EUS-FNA in discriminating resectable PDAC from MFP.

Incremental prognostic value of ADC histogram analysis in patients with high-risk prostate cancer receiving adjuvant hormonal therapy after radical prostatectomy.

Purpose: To investigate the incremental prognostic value of preoperative apparent diffusion coefficient (ADC) histogram analysis in patients with high-risk prostate cancer (PCa) who received adjuvant hormonal therapy (AHT) after radical prostatectomy (RP). Methods: Sixty-two PCa patients in line with the criteria were enrolled in this study. The 10th, 50th, and 90th percentiles of ADC (ADC10, ADC50, ADC90), the mean value of ADC (ADCmean), kurtosis, and skewness were obtained from the whole-lesion ADC histogram. The Kaplan-Meier method and Cox regression analysis were used to analyze the relationship between biochemical recurrence-free survival (BCR-fs) and ADC parameters and other clinicopathological factors. Prognostic models were constructed with and without ADC parameters. Results: The median follow-up time was 53.4 months (range, 41.1-79.3 months). BCR was found in 19 (30.6%) patients. Kaplan-Meier curves showed that lower ADCmean, ADC10, ADC50, and ADC90 and higher kurtosis could predict poorer BCR-fs (all p<0.05). After adjusting for clinical parameters, ADC50 and kurtosis remained independent prognostic factors for BCR-fs (HR: 0.172, 95% CI: 0.055-0.541, p=0.003; HR: 7.058, 95% CI: 2.288-21.773, p=0.001, respectively). By adding ADC parameters to the clinical model, the C index and diagnostic accuracy for the 24- and 36-month BCR-fs were improved. Conclusion: ADC histogram analysis has incremental prognostic value in patients with high-risk PCa who received AHT after RP. Combining ADC50, kurtosis and clinical parameters can improve the accuracy of BCR-fs prediction.

Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease.

OBJECTIVES: To investigate whether volumetric visceral adipose tissue (VAT) features extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approach are effective in differentiating Crohn's disease (CD) and ulcerative colitis (UC). METHODS: This retrospective study enrolled 316 patients (mean age, 36.25 ± 13.58 [standard deviation]; 219 men) with confirmed diagnosis of CD and UC who underwent CT enterography between 2012 and 2021. Volumetric VAT was semi-automatically segmented on the arterial phase images. Radiomics analysis was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. We developed a 3D-CNN model using VAT imaging data from the training cohort. Clinical covariates including age, sex, modified body mass index, and disease duration that impact VAT were added to the machine learning model for adjustment. The model's performance was evaluated on the testing cohort separating from the model's development process by its discrimination and clinical utility. RESULTS: Volumetric VAT radiomics analysis with LASSO had the highest AUC value of 0.717 (95% CI, 0.614-0.820), though difference of diagnostic performance among the 3D-CNN model (AUC = 0.693; 95% CI, 0.587-0.798) and radiomics analysis with PCA (AUC = 0.662; 95% CI, 0.548-0.776) and LASSO have not reached statistical significance (all p > 0.05). The radiomics score was higher in UC than in CD on the testing cohort (mean ± SD, UC 0.29 ± 1.05 versus CD -0.60 ± 1.25; p < 0.001). The LASSO model with adjustment of clinical covariates reached an AUC of 0.775 (95%CI, 0.683-0.868). CONCLUSION: The developed volumetric VAT-based radiomics and 3D-CNN models provided comparable and effective performance for the characterization of CD from UC. KEY POINTS: • High-output feature data extracted from volumetric visceral adipose tissue on CT enterography had an effective diagnostic performance for differentiating Crohn's disease from ulcerative colitis. • With adjustment of clinical covariates that cause difference in volumetric visceral adipose tissue, adjusted clinical machine learning model reached stronger performance when distinguishing Crohn's disease patients from ulcerative colitis patients.

DWI-based radiomic signature: potential role for individualized adjuvant chemotherapy in intrahepatic cholangiocarcinoma after partial hepatectomy.

OBJECTIVES: To develop a diffusion-weighted imaging (DWI) based radiomic signature for predicting early recurrence (ER) (i.e., recurrence within 1 year after surgery), and to explore the potential value for individualized adjuvant chemotherapy. METHODS: A total of 124 patients with intrahepatic cholangiocarcinoma (ICC) were randomly divided into the training (n = 87) and the validation set (n = 37). Radiomic signature was built using radiomic features extracted from DWI with random forest. An integrated radiomic nomogram was constructed with multivariate logistic regression analysis to demonstrate the incremental value of the radiomic signature beyond clinicopathological-radiographic factors. A clinicopathological-radiographic (CPR) model was constructed as a reference. RESULTS: The radiomic signature showed a comparable discrimination performance for predicting ER to CPR model in the validation set (AUC, 0.753 vs. 0.621, p = 0.274). Integrating the radiomic signature with clinicopathological-radiographic factors further improved prediction performance compared with CPR model, with an AUC of 0.821 (95%CI 0.684-0.959) in the validation set (p = 0.01). The radiomic signature succeeded to stratify patients into distinct survival outcomes according to their risk index of ER, and remained an independent prognostic factor in multivariable analysis (disease-free survival (DFS), p < 0.0001; overall survival (OS), p = 0.029). Furthermore, adjuvant chemotherapy improved prognosis in high-risk patients defined by the radiomic signature (DFS, p = 0.029; OS, p = 0.088) and defined by the nomogram (DFS, p = 0.031; OS, p = 0.023), whereas poor chemotherapy efficacy was detected in low-risk patients. CONCLUSIONS: The preoperative DWI-based radiomic signature could improve prognostic prediction and help to identify ICC patients who may benefit from postoperative adjuvant chemotherapy.

Multiparametric MRI-Based Radiomic Signature for Preoperative Evaluation of Overall Survival in Intrahepatic Cholangiocarcinoma After Partial Hepatectomy.

BACKGROUND: The clinical outcomes of patients with intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy remain suboptimal. Identifying patients with poor outcomes before surgery is urgently required. PURPOSE: To develop a multiparametric magnetic resonance imaging (MRI)-based radiomic signature to evaluate overall survival (OS) preoperatively and to investigate its incremental value for disease stratification. STUDY TYPE: Retrospective. SUBJECTS: One hundred and sixty-three patients with pathologically defined ICC, divided into training (N = 115) and validation sets (N = 48). SEQUENCE: Three-dimensional T1-weighted gradient-echo sequence with and without contrast agent, T2-weighted fast spin-echo sequence, and diffusion-weighted imaging with single-shot echo-planar sequence at 1.5 T or 3.0 T. ASSESSMENT: OS was defined as the time from the date of surgery to death or last contact. The radiomic signature was built based on the least absolute shrinkage and selection operator regression model. A clinicopathologic-radiographic (CPR) model and a combined model integrating radiomic signature with CPR factors were developed with multivariable Cox regression models. STATISTICAL TESTS: Harrell's concordance index (C-index) was used to compare the discrimination of different models. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to quantify the improvement of prognostic accuracy after adding radiomic signature. RESULTS: The high-risk patients of death defined by the radiomic signature showed significantly lower OS compared with low-risk patients in validation set (3-year OS 17.1% vs. 56.4%, P < 0.001). Integrating radiomic signature into tumor, node, and metastasis (TNM) staging system significantly improved the prognostic accuracy compared with TNM stage alone (validation set C-index 0.745 vs. 0.649, P = 0.039, NRI improvement 39.9%-43.8%, IDI improvement 16.1%-19.4%). The radiomic signature showed no significant difference of C-index with postoperative CPR model (validation set, 0.698 vs. 0.674, P = 0.752). Incorporating the radiomic signature into CPR model significantly improved prognostic accuracy (NRI improvement 32.5%-34.3%, IDI improvement 8.1%-12.9%). DATA CONCLUSION: Multiparametric MRI-based radiomic signature is a potential biomarker for preoperative prognostic evaluation of ICC patients. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

CT-Based Radiomics Can Predict the Efficacy of Anlotinib in Advanced Non-Small-Cell Lung Cancer.

Anlotinib is a small-molecule RTK inhibitor that has achieved certain results in further-line treatment, but many patients do not respond to this drug and lack effective methods for identification. Although radiomics has been widely used in lung cancer, very few studies have been conducted in the field of antiangiogenic drugs. This study aims to develop a new model to predict the efficacy of patients receiving anlotinib by combining pretreatment computed tomography (CT) radiomic characters with clinical characters, in order to assist precision medicine of pulmonary cancer. 254 patients from seven institutions were involved in the study. Lesions were selected according to the RECIST 1.1 criteria, and the corresponding radiomic features were obtained. We constructed prediction models based on clinical, NCE-CT, and CE-CT radiomic features, respectively, and evaluated the prediction performance of the models for training sets, internal validation sets, and external validation sets. In the RAD score only model, the area under curve(AUC) of the NCE-CT cohort was 0.740 (95% CI: 0.622, 0.857) for the training set, 0.711 (95% CI: 0.480, 0.942) for the internal validation set, and 0.633(95% CI: 0.479, 0.787) for the external validation set, while that of the CE-CT cohort was 0.815 (95% CI: 0.705, 0.926) for the training set, 0.771 (95% CI: 0.539, 1.000) for the internal validation set, and 0.701 (95% CI: 0.489, 0.913) for the external validation set. In the RAD score-combined model, the AUC of the NCE-CT cohort was 0.796 (95% CI: 0.691, 0.901) for the training set, 0.579 (95% CI: 0.309, 0.848) for the internal validation set, and 0.590 (95% CI: 0.427, 0.753) for the external validation set, while that of the CE-CT cohort was 0.902 (95% CI: 0.828, 0.977) for the training set, 0.865 (95% CI: 0.696, 1.000) for the internal validation set, and 0.837 (95% CI: 0.682, 0.992) for the external validation set. In conclusion, radiomics has accurate predictions for the efficacy of anlotinib. CE-CT-based radiomic models have the best predictive potential in predicting the efficacy of anlotinib, and model predictions become better when they are combined with clinical characteristics.

Computed tomography-based multiple body composition parameters predict outcomes in Crohn's disease.

BACKGROUND: The efficacy of computed tomography-based multiple body composition parameters in assessing disease behavior and prognosis has not been comprehensively evaluated in Crohn's disease. This study aimed to assess the association of body composition parameters with disease behavior and outcomes in Crohn's disease and to compare the efficacies of indexes derived from body and lumbar spinal heights in body composition analysis. RESULTS: One hundred twenty-two patients with confirmed Crohn's disease diagnoses and abdominal computed tomography scans were retrospectively included in this study. Skeletal muscle, visceral, and subcutaneous fat indexes were calculated by dividing each type of tissue area by height2 and lumbar spinal height2. Parameters reflecting the distribution of adiposity were also assessed. Principal component analysis was used to deal with parameters with multicollinearity. Patients were grouped according to their disease behavior (inflammatory vs. structuring/penetrating) and outcomes. Adverse outcome included need for intestinal surgery or anti-TNF therapy. Predictors of disease course from multiple parameters were evaluated using multivariate analysis. Indexes derived from body and lumbar spinal heights were strongly correlated (r, 0.934-0.995; p < 0.001). Low skeletal muscle-related parameters were significantly associated with complicated disease behavior in multivariate analysis (p = 0.048). Complicated disease behavior (p < 0.001) and adipose tissue parameters-related first principal component (p = 0.029) were independent biomarkers for predicting adverse outcomes. CONCLUSIONS: Skeletal muscle and adipose tissue principle component were associated with complicated Crohn's disease behavior and adverse outcome, respectively. Indexes derived from body and lumbar spinal heights have similar efficacies in body composition analysis.

New Digital Plug and Imaging Sensor for a Proton Therapy Monitoring System Based on Positron Emission Tomography.

One of the most challenging areas of sensor development for nuclear medicine is the design of proton therapy monitoring systems. Sensors are operated in a high detection rate regime in beam-on conditions. We realized a prototype of a monitoring system for proton therapy based on the technique of positron emission tomography. We used the Plug and Imaging (P&I) technology in this application. This sensing system includes LYSO/silicon photomultiplier (SiPM) detection elements, fast digital multi voltage threshold (MVT) readout electronics and dedicated image reconstruction algorithms. In this paper, we show that the P&I sensor system has a uniform response and is controllable in the experimental conditions of the proton therapy room. The prototype of PET monitoring device based on the P&I sensor system has an intrinsic experimental spatial resolution of approximately 3 mm (FWHM), obtained operating the prototype both during the beam irradiation and right after it. The count-rate performance of the P&I sensor approaches 5 Mcps and allows the collection of relevant statistics for the nuclide analysis. The measurement of both the half life and the relative abundance of the positron emitters generated in the target volume through irradiation of 10 10 protons in approximately 15 s is performed with 0.5% and 5 % accuracy, respectively.

Tmbim1 is a multivesicular body regulator that protects against non-alcoholic fatty liver disease in mice and monkeys by targeting the lysosomal degradation of Tlr4.

Non-alcoholic steatohepatitis (NASH) is an increasingly prevalent liver pathology that can progress from non-alcoholic fatty liver disease (NAFLD), and it is a leading cause of cirrhosis and hepatocellular carcinoma. There is currently no pharmacological therapy for NASH. Defective lysosome-mediated protein degradation is a key process that underlies steatohepatitis and a well-recognized drug target in a variety of diseases; however, whether it can serve as a therapeutic target for NAFLD and NASH remains unknown. Here we report that transmembrane BAX inhibitor motif-containing 1 (TMBIM1) is an effective suppressor of steatohepatitis and a previously unknown regulator of the multivesicular body (MVB)-lysosomal pathway. Tmbim1 expression in hepatocytes substantially inhibited high-fat diet-induced insulin resistance, hepatic steatosis and inflammation in mice. Mechanistically, Tmbim1 promoted the lysosomal degradation of toll-like receptor 4 by cooperating with the ESCRT endosomal sorting complex to facilitate MVB formation, and the ubiquitination of Tmbim1 by the E3 ubiquitin ligase Nedd4l was required for this process. We also found that overexpression of Tmbim1 in the liver effectively inhibited a severe form of NAFLD in mice and NASH progression in monkeys. Taken together, these findings could lead to the development of promising strategies to treat NASH by targeting MVB regulators to properly orchestrate the lysosome-mediated protein degradation of key mediators of the disease.

Targeting CASP8 and FADD-like apoptosis regulator ameliorates nonalcoholic steatohepatitis in mice and nonhuman primates.

Nonalcoholic steatohepatitis (NASH) is a progressive disease that is often accompanied by metabolic syndrome and poses a high risk of severe liver damage. However, no effective pharmacological treatment is currently available for NASH. Here we report that CASP8 and FADD-like apoptosis regulator (CFLAR) is a key suppressor of steatohepatitis and its metabolic disorders. We provide mechanistic evidence that CFLAR directly targets the kinase MAP3K5 (also known as ASK1) and interrupts its N-terminus-mediated dimerization, thereby blocking signaling involving ASK1 and the kinase MAPK8 (also known as JNK1). Furthermore, we identified a small peptide segment in CFLAR that effectively attenuates the progression of steatohepatitis and metabolic disorders in both mice and monkeys by disrupting the N-terminus-mediated dimerization of ASK1 when the peptide is expressed from an injected adenovirus-associated virus 8-based vector. Taken together, these findings establish CFLAR as a key suppressor of steatohepatitis and indicate that the development of CFLAR-peptide-mimicking drugs and the screening of small-molecular inhibitors that specifically block ASK1 dimerization are new and feasible approaches for NASH treatment.

Loss of 11ßHSD1 enhances glycolysis, facilitates intrahepatic metastasis, and indicates poor prognosis in hepatocellular carcinoma.

11beta-hydroxysteroid dehydrogenase type 1 (11ßHSD1), converting glucocorticoids from hormonally inactive cortisone to active cortisol, plays an essential role in glucose homeostasis. Accumulating evidence suggests that enhanced glycolytic activity is closely associated with postoperative recurrence and prognosis of hepatocellular carcinoma (HCC). Whether 11ßHSD1 contributes to HCC metastasis and recurrence remains unclear. Here we found that expression of 11ßHSD1 in human HCC (310 pairs) was frequently decreased compared to the adjacent non-neoplastic liver tissues (ANT), which correlated well with the intrahepatic-metastatic index, serum glycemia, and other malignant clinicopathological characteristics of HCC and predicted poor prognosis. Knockdown of 11ßHSD1 in BEL-7402 cells drastically reduced the pH of culture medium and induced cell death. Meanwhile, overexpression of 11ßHSD1 in SMMC-7721 HCC cells resulted in repression of cell migration, invasion, angiogenesis, and proliferation in vitro. When transferred into BALB/c nude mice, 11ßHSD1 overexpression resulted in decreased intrahepatic metastasis, angiogenesis, and tumor size. F-18-2-fluoro-2-deoxyglucose accumulation assay measured by positron emission tomography elucidated that 11ßHSD1 reduced glucose uptake and glycolysis in SMMC-7721 cells in vitro, and intrahepatic metastasis foci and subcutaneous tumor growth in vivo. We showed that 11ßHSD1 repressed cell metastasis, angiogenesis and proliferation of HCC by causing disruption of glycolysis via the HIF-1α and c-MYC pathways. In conclusion, 11ßHSD1 inhibits the intrahepatic metastasis of HCC via restriction of tumor glycolysis activity and may serve as a prognostic biomarker for patients.

Kinetic analysis of dynamic (11)C-acetate PET/CT imaging as a potential method for differentiation of hepatocellular carcinoma and benign liver lesions.

OBJECTIVE: The kinetic analysis of (11)C-acetate PET provides more information than routine one time-point static imaging. This study aims to investigate the potential of dynamic (11)C-acetate hepatic PET imaging to improve the diagnosis of hepatocellular carcinoma (HCC) and benign liver lesions by using compartmental kinetic modeling and discriminant analysis. METHODS: Twenty-two patients were enrolled in this study, 6 cases were with well-differentiated HCCs, 7 with poorly-differentiated HCCs and 9 with benign pathologies. Following the CT scan, all patients underwent (11)C-acetate dynamic PET imaging. A three-compartment irreversible dual-input model was applied to the lesion time activity curves (TACs) to estimate the kinetic rate constants K1-k3, vascular fraction (VB) and the coefficient α representing the relative hepatic artery (HA) contribution to the hepatic blood supply on lesions and non-lesion liver tissue. The parameter Ki (=K1×k3/(k2 + k3)) was calculated to evaluate the local hepatic metabolic rate of acetate (LHMAct). The lesions were further classified by discriminant analysis with all the above parameters. RESULTS: K1 and lesion to non-lesion standardized uptake value (SUV) ratio (T/L) were found to be the parameters best characterizing the differences among well-differentiated HCC, poorly-differentiated HCC and benign lesions in stepwise discriminant analysis. With discriminant functions consisting of these two parameters, the accuracy of lesion prediction was 87.5% for well-differentiated HCC, 50% for poorly-differentiated HCC and 66.7% for benign lesions. The classification was much better than that with SUV and T/L, where the corresponding classification accuracy of the three kinds of lesions was 57.1%, 33.3% and 44.4%. CONCLUSION: (11)C-acetate kinetic parameter K1 could improve the identification of HCC from benign lesions in combination with T/L in discriminant analysis. The discriminant analysis using static and kinetic parameters appears to be a very helpful method for clinical liver masses diagnosis and staging.

Comparison of three dimeric 18F-AlF-NOTA-RGD tracers.

PURPOSE: RGD peptide-based radiotracers are well established as integrin αvß3 imaging probes to evaluate tumor angiogenesis or tissue remodeling after ischemia or infarction. In order to optimize the labeling process and pharmacokinetics of the imaging probes, we synthesized three dimeric RGD peptides with or without PEGylation and performed in vivo screening. PROCEDURES: Radiolabeling was achieved through the reaction of F-18 aluminum-fluoride complex with the cyclic chelator, 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA). Three imaging probes were synthesized as (18)F-AlF-NOTA-E[c(RGDfK)]2, (18)F-AlF-NOTA-PEG4-E[c(RGDfK)]2, and (18)F-AlF-NOTA-E[PEG4-c(RGDfk)]2. The receptor binding affinity was determined by competitive cell binding assay, and the stability was evaluated by mouse serum incubation. Tumor uptake and whole body distribution of the three tracers were compared through direct tissue sampling and PET quantification of U87MG tumor-bearing mice. RESULTS: All three compounds remained intact after 120 min incubation with mouse serum. They all had a rapid and relatively high tracer uptake in U87MG tumors with good target-to-background ratios. Compared with the other two tracers, (18)F-AlF-NOTA-E[PEG4-c(RGDfk)]2 had the highest tumor uptake and the lowest accumulation in the liver. The integrin receptor specificity was confirmed by co-injection of unlabeled dimeric RGD peptide. CONCLUSION: The rapid one-step radiolabeling strategy by the complexation of (18)F-aluminum fluoride with NOTA-peptide conjugates was successfully applied to synthesize three dimeric RGD peptides. Among the three probes developed, (18)F-AlF-NOTA-E[PEG4-c(RGDfk)]2 with relatively low liver uptake and high tumor accumulation appears to be a promising candidate for further translational research.

(18)F-alfatide II and (18)F-FDG dual-tracer dynamic PET for parametric, early prediction of tumor response to therapy.

UNLABELLED: A single dynamic PET acquisition using multiple tracers administered closely in time could provide valuable complementary information about a tumor's status under quasiconstant conditions. This study aimed to investigate the utility of dual-tracer dynamic PET imaging with (18)F-alfatide II ((18)F-AlF-NOTA-E[PEG4-c(RGDfk)]2) and (18)F-FDG for parametric monitoring of tumor responses to therapy. METHODS: We administered doxorubicin to one group of athymic nude mice with U87MG tumors and paclitaxel protein-bound particles to another group of mice with MDA-MB-435 tumors. To monitor therapeutic responses, we performed dual-tracer dynamic imaging, in sessions that lasted 90 min, starting with injection via the tail vein catheters with (18)F-alfatide II, followed 40 min later by (18)F-FDG. To achieve signal separation of the 2 tracers, we fit a 3-compartment reversible model to the time-activity curve of (18)F-alfatide II for the 40 min before (18)F-FDG injection and then extrapolated to 90 min. The (18)F-FDG tumor time-activity curve was isolated from the 90-min dual-tracer tumor time-activity curve by subtracting the fitted (18)F-alfatide II tumor time-activity curve. With separated tumor time-activity curves, the (18)F-alfatide II binding potential (Bp = k3/k4) and volume of distribution (VD) and (18)F-FDG influx rate ((K1 × k3)/(k2 + k3)) based on the Patlak method were calculated to validate the signal recovery in a comparison with 60-min single-tracer imaging and to monitor therapeutic response. RESULTS: The transport and binding rate parameters K1-k3 of (18)F-alfatide II, calculated from the first 40 min of the dual-tracer dynamic scan, as well as Bp and VD correlated well with the parameters from the 60-min single-tracer scan (R(2) > 0.95). Compared with the results of single-tracer PET imaging, (18)F-FDG tumor uptake and influx were recovered well from dual-tracer imaging. On doxorubicin treatment, whereas no significant changes in static tracer uptake values of (18)F-alfatide II or (18)F-FDG were observed, both (18)F-alfatide II Bp and (18)F-FDG influx from kinetic analysis in tumors showed significant decreases. For therapy of MDA-MB-435 tumors with paclitaxel protein-bound particles, a significant decrease was observed only with (18)F-alfatide II Bp value from kinetic analysis but not (18)F-FDG influx. CONCLUSION: The parameters fitted with compartmental modeling from the dual-tracer dynamic imaging are consistent with those from single-tracer imaging, substantiating the feasibility of this methodology. Even though no significant differences in tumor size were found until 5 d after doxorubicin treatment started, at day 3 there were already substantial differences in (18)F-alfatide II Bp and (18)F-FDG influx rate. Dual-tracer imaging can measure (18)F-alfatide II Bp value and (18)F-FDG influx simultaneously to evaluate tumor angiogenesis and metabolism. Such changes are known to precede anatomic changes, and thus parametric imaging may offer the promise of early prediction of therapy response.

First experience of 18F-alfatide in lung cancer patients using a new lyophilized kit for rapid radiofluorination.

UNLABELLED: (18)F-FPPRGD2, which was approved for clinical study recently, has favorable properties for integrin targeting and showed potential for antiangiogenic therapy and early response monitoring. However, the time-consuming multiple-step synthesis may limit its widespread applications in the clinic. In this study, we developed a simple lyophilized kit for labeling PRGD2 peptide ((18)F-AlF-NOTA-PRGD2, denoted as (18)F-alfatide) using a fluoride-aluminum complex that significantly simplified the labeling procedure. METHODS: Nine patients with a primary diagnosis of lung cancer were examined by both static and dynamic PET imaging with (18)F-alfatide, and 1 tuberculosis patient was investigated using both (18)F-alfatide and (18)F-FDG imaging. Standardized uptake values were measured in tumors and other main organs at 30 min and 1 h after injection. Kinetic parameters were calculated by Logan graphical analysis. Immunohistochemistry and staining intensity quantification were performed to confirm the expression of integrin α(v)ß(3). RESULTS: Under the optimal conditions, the whole radiosynthesis including purification was accomplished within 20 min with a decay-corrected yield of 42.1% ± 2.0% and radiochemical purity of more than 95%. (18)F-alfatide PET imaging identified all tumors, with mean standardized uptake values of 2.90 ± 0.10. Tumor-to-muscle and tumor-to-blood ratios were 5.87 ± 2.02 and 2.71 ± 0.92, respectively. CONCLUSION: (18)F-alfatide can be produced with excellent radiochemical yield and purity via a simple, 1-step, lyophilized kit. PET scanning with (18)F-alfatide allows specific imaging of αvß3 expression with good contrast in lung cancer patients. This technique might be used for the assessment of angiogenesis and for planning and response evaluation of cancer therapies that would affect angiogenesis status and integrin expression levels.

Quantitative analysis and comparison study of [18F]AlF-NOTA-PRGD2, [18F]FPPRGD2 and [68Ga]Ga-NOTA-PRGD2 using a reference tissue model.

With favorable pharmacokinetics and binding affinity for α(v)ß(3) integrin, (18)F-labeled dimeric cyclic RGD peptide ([(18)F]FPPRGD2) has been intensively used as a PET imaging probe for lesion detection and therapy response monitoring. A recently introduced kit formulation method, which uses an (18)F-fluoride-aluminum complex labeled RGD tracer ([(18)F]AlF-NOTA-PRGD2), provides a strategy for simplifying the labeling procedure to facilitate clinical translation. Meanwhile, an easy-to-prepare (68)Ga-labeled NOTA-PRGD2 has also been reported to have promising properties for imaging integrin α(v)ß(3). The purpose of this study is to quantitatively compare the pharmacokinetic parameters of [(18)F]FPPRGD2, [(18)F]AlF-NOTA-PRGD2, and [(68)Ga]Ga-NOTA-PRGD2. U87MG tumor-bearing mice underwent 60-min dynamic PET scans following the injection of three tracers. Kinetic parameters were calculated using Logan graphical analysis with reference tissue. Parametric maps were generated using voxel-level modeling. All three compounds showed high binding potential (Bp(ND) = k(3)/k(4)) in tumor voxels. [(18)F]AlF-NOTA-PRGD2 showed comparable Bp(ND) value (3.75±0.65) with those of [(18)F]FPPRGD2 (3.39±0.84) and [(68)Ga]Ga-NOTA-PRGD2 (3.09±0.21) (p>0.05). Little difference was found in volume of distribution (V(T)) among these three RGD tracers in tumor, liver and muscle. Parametric maps showed similar kinetic parameters for all three tracers. We also demonstrated that the impact of non-specific binding could be eliminated in the kinetic analysis. Consequently, kinetic parameter estimation showed more comparable results among groups than static image analysis. In conclusion, [(18)F]AlF-NOTA-PRGD2 and [(68)Ga]Ga-NOTA-PRGD2 have comparable pharmacokinetics and quantitative parameters compared to those of [(18)F]FPPRGD2. Despite the apparent difference in tumor uptake (%ID/g determined from static images) and clearance pattern, the actual specific binding component extrapolated from kinetic modeling appears to be comparable for all three dimeric RGD tracers.

Quantitative analysis and parametric imaging of 18F-labeled monomeric and dimeric RGD peptides using compartment model.

PURPOSE: Non-invasive PET imaging with radiolabeled RGD peptides for α(v)ß(3) integrin targeting has become an important tool for tumor diagnosis and treatment monitoring in both pre-clinical and clinical studies. To better understand the molecular process and tracer pharmacokinetics, we introduced kinetic modeling in the investigation of (18)F-labeled RGD peptide monomer (18)F-FP-c(RGDyK) (denoted as (18)F-FPRGD) and dimer (18)F-FP-PEG3-E[c(RGDyK)](2) (denoted as (18)F-FPPRGD2). PROCEDURES: MDA-MB-435 tumor-bearing mice underwent 60 min dynamic PET scans following the injection of either (18)F-FPRGD or (18)F-FPPRGD2. Blocking studies with pre-injection of a blocking mass dose were performed for both monomeric and dimeric RGD groups. (18)F-FPRAD (RAD) was used as a negative control. Kinetic parameters (K(1), k(2), k(3), k(4)) of a three-compartment model were fitted to the dynamic data to allow quantitative comparisons between the monomeric and dimeric RGD peptides. RESULTS: Dimeric RGD peptide tracer showed significantly higher binding potential (Bp(ND) = k(3)/k(4), 5.87 ± 0.31) than that of the monomeric analog (2.75 ± 0.48, p = 0.0022, n = 4/group). The Bp(ND) values showed a significantly greater ratio (dimer/monomer ~2.1) than the difference in %ID/g uptake measured from static images (dimer/monomer ~1.5, p = 0.0045). Significant decrease in Bp(ND) was found in the blocked groups compared with the unblocked ones (dimer p = 0.00024, monomer p = 0.005, n = 4/group). Similarly, the RAD control group showed the lowest Bp(ND) value among all the test groups, as the RAD peptide does not bind to integrin α(v)ß(3). Volume of distribution (V(T) = K(1)/k (2)(1 + k (3)/k (4))) could be separated into non-specific (V (ND) = K (1)/k (2)) and specific (V (S) = K (1) k (3)/(k (2) k (4))) components. Specific distribution volume (V(S)) was the dominant component of V(T) in the unblocked groups and decreased in the blocked groups. Unblocked RGD dimer also showed higher V(S) than that of the monomer (dimer V(S) = 2.38 ± 0.15, monomer V(S) = 0.90 ± 0.17, p = 0.0013, n = 4/group), well correlated with Bp(ND) calculations. Little difference in V(ND) was found among all groups. Moreover, parametric maps allowed quantitative analysis at voxel level and provided higher tumor-to-background contrast for Bp(ND) maps than the static images. Tumor heterogeneity in kinetic parameters was found in parametric images, which could not be clearly identified in static intensity images. CONCLUSIONS: The pharmacokinetics of both monomeric and dimeric RGD peptide tracers was compared, and the RGD dimers showed significantly higher binding affinity than the monomeric analogs. Kinetic parameters were demonstrated to be valuable for separating specific and non-specific binding and may allow more sensitive and detailed quantification than simple standardized uptake value analysis.