A Comparison of Globally Applied Prognostic Risk Groups and the Prevalence of Metastatic Disease on Prostate-specific Membrane Antigen Positron Emission Tomography in Patients with Newly Diagnosed Prostate Cancer.

BACKGROUND: Various risk classification systems (RCSs) are used globally to stratify newly diagnosed patients with prostate cancer (PCa) into prognostic groups. OBJECTIVE: To compare the predictive value of different prognostic subgroups (low-, intermediate-, and high-risk disease) within the RCSs for detecting metastatic disease on prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) for primary staging, and to assess whether further subdivision of subgroups would be beneficial. DESIGN, SETTING, AND PARTICIPANTS: Patients with newly diagnosed PCa, in whom PSMA-PET/CT was performed between 2017 and 2022, were studied retrospectively. Patients were stratified into risk groups based on four RCSs: European Association of Urology, National Comprehensive Cancer Network (NCCN), Cambridge Prognostic Group (CPG), and Cancer of the Prostate Risk Assessment. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The prevalence of metastatic disease on PSMA-PET/CT was compared among the subgroups within the four RCSs. RESULTS AND LIMITATIONS: In total, 2630 men with newly diagnosed PCa were studied. Any metastatic disease was observed in 35% (931/2630) of patients. Among patients classified as having intermediate- and high-risk disease, the prevalence of metastases ranged from approximately 12% to 46%. Two RCSs further subdivided these groups. According to the NCCN, metastatic disease was observed in 5.8%, 13%, 22%, and 62% for favorable intermediate-, unfavorable intermediate-, high-, and very-high-risk PCa, respectively. Regarding the CPG, these values were 6.9%, 13%, 21%, and 60% for the corresponding risk groups. CONCLUSIONS: This study underlines the importance of nuanced risk stratification, recommending the further subdivision of intermediate- and high-risk disease given the notable variation in the prevalence of metastatic disease. PSMA-PET/CT for primary staging should be reserved for patients with unfavorable intermediate- or higher-risk disease. PATIENT SUMMARY: The use of various risk classification systems in patients with prostate cancer helps identify those at a higher risk of having metastatic disease on prostate-specific membrane antigen positron emission tomography/computed tomography for primary staging.

Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer.

INTRODUCTION: Radiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients. METHODS: Patients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC). RESULTS: The CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p<0.05). However, validation AUCs for LNI and ECE prediction were not significant (0.57 and 0.63, respectively). Conventional PET metrics-based models had comparable AUCs for LNI (0.59, p>0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, p<0.05). In general, Combat harmonization improved external validation AUCs (-0.03 to +0.18). CONCLUSION: In internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability.

The association between clinical, sociodemographic, familial, and environmental factors and treatment resistance in schizophrenia: A machine-learning-based approach.

BACKGROUND: Prediction of treatment resistance in schizophrenia (TRS) would be helpful to reduce the duration of ineffective treatment and avoid delays in clozapine initiation. We applied machine learning to identify clinical, sociodemographic, familial, and environmental variables that are associated with TRS and could potentially predict TRS in the future. STUDY DESIGN: Baseline and follow-up data on trait(-like) variables from the Genetic Risk and Outcome of Psychosis (GROUP) study were used. For the main analysis, we selected patients with non-affective psychotic disorders who met TRS (n = 200) or antipsychotic-responsive criteria (n = 423) throughout the study. For a sensitivity analysis, we only selected patients who met TRS (n = 76) or antipsychotic-responsive criteria (n = 123) at follow-up but not at baseline. Random forest models were trained to predict TRS in both datasets. SHapley Additive exPlanation values were used to examine the variables' contributions to the prediction. STUDY RESULTS: Premorbid functioning, age at onset, and educational degree were most consistently associated with TRS across both analyses. Marital status, current household, intelligence quotient, number of moves, and family loading score for substance abuse also consistently contributed to the prediction of TRS in the main or sensitivity analysis. The diagnostic performance of our models was modest (area under the curve: 0.66-0.69). CONCLUSIONS: We demonstrate that various clinical, sociodemographic, familial, and environmental variables are associated with TRS. Our models only showed modest performance in predicting TRS. Prospective large multi-centre studies are needed to validate our findings and investigate whether the model's performance can be improved by adding data from different modalities.

Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features.

PURPOSE: Biomarkers that can accurately predict outcome in DLBCL patients are urgently needed. Radiomics features extracted from baseline [18F]-FDG PET/CT scans have shown promising results. This study aims to investigate which lesion- and feature-selection approaches/methods resulted in the best prediction of progression after 2 years. METHODS: A total of 296 patients were included. 485 radiomics features (n = 5 conventional PET, n = 22 morphology, n = 50 intensity, n = 408 texture) were extracted for all individual lesions and at patient level, where all lesions were aggregated into one VOI. 18 features quantifying dissemination were extracted at patient level. Several lesion selection approaches were tested (largest or hottest lesion, patient level [all with/without dissemination], maximum or median of all lesions) and compared to the predictive value of our previously published model. Several data reduction methods were applied (principal component analysis, recursive feature elimination (RFE), factor analysis, and univariate selection). The predictive value of all models was tested using a fivefold cross-validation approach with 50 repeats with and without oversampling, yielding the mean cross-validated AUC (CV-AUC). Additionally, the relative importance of individual radiomics features was determined. RESULTS: Models with conventional PET and dissemination features showed the highest predictive value (CV-AUC: 0.72-0.75). Dissemination features had the highest relative importance in these models. No lesion selection approach showed significantly higher predictive value compared to our previous model. Oversampling combined with RFE resulted in highest CV-AUCs. CONCLUSION: Regardless of the applied lesion selection or feature selection approach and feature reduction methods, patient level conventional PET features and dissemination features have the highest predictive value. Trial registration number and date: EudraCT: 2006-005174-42, 01-08-2008.

Targeting PSMA Revolutionizes the Role of Nuclear Medicine in Diagnosis and Treatment of Prostate Cancer.

Targeting the prostate-specific membrane antigen (PSMA) protein has become of great clinical value in prostate cancer (PCa) care. PSMA positron emission tomography/computed tomography (PET/CT) is increasingly used in initial staging and restaging at biochemical recurrence in patients with PCa, where it has shown superior detection rates compared to previous imaging modalities. Apart from targeting PSMA for diagnostic purposes, there is a growing interest in developing ligands to target the PSMA-protein for radioligand therapy (RLT). PSMA-based RLT is a novel treatment that couples a PSMA-antibody to (alpha or beta-emitting) radionuclide, such as Lutetium-177 (177Lu), to deliver high radiation doses to tumor cells locally. Treatment with 177Lu-PSMA RLT has demonstrated a superior overall survival rate within randomized clinical trials as compared to routine clinical care in patients with metastatic castration-resistant prostate cancer (mCRPC). The current review provides an overview of the literature regarding recent developments in nuclear medicine related to PSMA-targeted PET imaging and Theranostics.

Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography.

Metastatic tumor deposits in bone marrow elicit differential bone responses that vary with the type of malignancy. This results in either sclerotic, lytic, or mixed bone lesions, which can change in morphology due to treatment effects and/or secondary bone remodeling. Hence, morphological imaging is regarded unsuitable for response assessment of bone metastases and in the current Response Evaluation Criteria In Solid Tumors 1.1 (RECIST1.1) guideline bone metastases are deemed unmeasurable. Nevertheless, the advent of functional and molecular imaging modalities such as whole-body magnetic resonance imaging (WB-MRI) and positron emission tomography (PET) has improved the ability for follow-up of bone metastases, regardless of their morphology. Both these modalities not only have improved sensitivity for visual detection of bone lesions, but also allow for objective measurements of bone lesion characteristics. WB-MRI provides a global assessment of skeletal metastases and for a one-step "all-organ" approach of metastatic disease. Novel MRI techniques include diffusion-weighted imaging (DWI) targeting highly cellular lesions, dynamic contrast-enhanced MRI (DCE-MRI) for quantitative assessment of bone lesion vascularization, and multiparametric MRI (mpMRI) combining anatomical and functional sequences. Recommendations for a homogenization of MRI image acquisitions and generalizable response criteria have been developed. For PET, many metabolic and molecular radiotracers are available, some targeting tumor characteristics not confined to cancer type (e.g. 18F-FDG) while other targeted radiotracers target specific molecular characteristics, such as prostate specific membrane antigen (PSMA) ligands for prostate cancer. Supporting data on quantitative PET analysis regarding repeatability, reproducibility, and harmonization of PET/CT system performance is available. Bone metastases detected on PET and MRI can be quantitatively assessed using validated methodologies, both on a whole-body and individual lesion basis. Both have the advantage of covering not only bone lesions but visceral and nodal lesions as well. Hybrid imaging, combining PET with MRI, may provide complementary parameters on the morphologic, functional, metabolic and molecular level of bone metastases in one examination. For clinical implementation of measuring bone metastases in response assessment using WB-MRI and PET, current RECIST1.1 guidelines need to be adapted. This review summarizes available data and insights into imaging of bone metastases using MRI and PET.

SUVs Are Adequate Measures of Lesional 18F-DCFPyL Uptake in Patients with Low Prostate Cancer Disease Burden.

In prostate cancer (PCa) patients, the tumor-to-blood ratio (TBR) has been validated as the preferred simplified method for lesional 18F-DCFPyL (a radiolabeled prostate-specific membrane antigen ligand) uptake quantification on PET. In contrast to SUVs, the TBR accounts for variability in arterial input functions caused by differences in total tumor burden between patients (the sink effect). However, TBR depends strongly on tracer uptake interval and has worse repeatability and is less applicable in clinical practice than SUVs. We investigated whether SUV could provide adequate quantification of 18F-DCFPyL uptake on PET/CT in a patient cohort with low PCa burden. Methods: In total, 116 patients with PCa undergoing 18F-DCFPyL PET/CT imaging were retrospectively included. All 18F-DCFPyL-avid lesions suspected of being PCa were semiautomatically delineated. SUVpeak was plotted against TBR for the most intense lesion of each patient. The correlation of SUVpeak and TBR was evaluated using linear regression and was stratified for patients undergoing PET/CT for primary staging, patients undergoing restaging at biochemical recurrence, and patients with metastatic castration-resistant PCa. Moreover, the correlation was evaluated as a function of tracer uptake time, prostate-specific antigen level, and PET-positive tumor volume. Results: In total, 436 lesions were delineated (median, 1 per patient; range, 1-66). SUVpeak correlated well with TBR in patients with PCa and a total tumor volume of less than 200 cm3 (R2 = 0.931). The correlation between SUV and TBR was not affected by disease setting, prostate-specific antigen level, or tumor volume. SUVpeak depended less on tracer uptake time than did TBR. Conclusion: For 18F-DCFPyL PET/CT, SUVpeak correlates strongly with TBR. Therefore, it is a valuable simplified, semiquantitative measurement in patients with low-volume PCa (<200 cm3). SUVpeak can therefore be applied in 18F-DCFPyL PET assessment as an imaging biomarker to characterize tumors and to monitor treatment outcomes.

Classification of negative and positive 18F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network.

PURPOSE: Visual reading of 18F-florbetapir positron emission tomography (PET) scans is used in the diagnostic process of patients with cognitive disorders for assessment of amyloid-ß (Aß) depositions. However, this can be time-consuming, and difficult in case of borderline amyloid pathology. Computer-aided pattern recognition can be helpful in this process but needs to be validated. The aim of this work was to develop, train, validate and test a convolutional neural network (CNN) for discriminating between Aß negative and positive 18F-florbetapir PET scans in patients with subjective cognitive decline (SCD). METHODS: 18F-florbetapir PET images were acquired and visually assessed. The SCD cohort consisted of 133 patients from the SCIENCe cohort and 22 patients from the ADNI database. From the SCIENCe cohort, standardized uptake value ratio (SUVR) images were computed. From the ADNI database, SUVR images were extracted. 2D CNNs (axial, coronal and sagittal) were built to capture features of the scans. The SCIENCe scans were randomly divided into training and validation set (5-fold cross-validation), and the ADNI scans were used as test set. Performance was evaluated based on average accuracy, sensitivity and specificity from the cross-validation. Next, the best performing CNN was evaluated on the test set. RESULTS: The sagittal 2D-CNN classified the SCIENCe scans with the highest average accuracy of 99% ± 2 (SD), sensitivity of 97% ± 7 and specificity of 100%. The ADNI scans were classified with a 95% accuracy, 100% sensitivity and 92.3% specificity. CONCLUSION: The 2D-CNN algorithm can classify Aß negative and positive 18F-florbetapir PET scans with high performance in SCD patients.

Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer.

PURPOSE: Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [18F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features. METHODS: In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [18F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC. RESULTS: The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance. CONCLUSION: Machine learning-based analysis of quantitative [18F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice.

Repeatability of Quantitative 18F-DCFPyL PET/CT Measurements in Metastatic Prostate Cancer.

Quantitative evaluation of radiolabeled prostate-specific membrane antigen (PSMA) PET scans may be used to monitor treatment response in patients with prostate cancer (PCa). To interpret longitudinal differences in PSMA uptake, the intrinsic variability of tracer uptake in PCa lesions needs to be defined. The aim of this study was to investigate the repeatability of quantitative PET/CT measurements using 18F-DCFPyL ([2-(3-(1-carboxy-5-[(6-18F-fluoro-pyridine-3-carbonyl)-amino]-pentyl)-ureido)-pentanedioic acid], a second-generation 18F-PSMA-ligand) in patients with PCa. Methods: Twelve patients with metastatic PCa were prospectively included, of whom 2 were excluded from final analyses. Patients received 2 whole-body 18F-DCFPyL PET/CT scans (median dose, 317 MBq; uptake time, 120 min) within a median of 4 d (range, 1-11 d). After semiautomatic (isocontour-based) tumor delineation, the following lesion-based metrics were derived: mean, peak, and maximum tumor-to-blood ratio; SUVmean, SUVpeak, and SUVmax normalized to body weight; tumor volume; and total lesion uptake (TLU). Additionally, patient-based total tumor volume (TTV) (sum of PSMA-positive tumor volumes) and total tumor burden (TTB) (sum of all lesion TLUs) were derived. Repeatability was analyzed using repeatability coefficients (RC) and intraclass correlation coefficients. Additionally, the effect of point-spread function (PSF) image reconstruction on the repeatability of uptake metrics was evaluated. Results: In total, 36 18F-DCFPyL PET-positive lesions were analyzed (≤5 lesions per patient). The RCs for mean, peak, and maximum tumor-to-blood ratio were 31.8%, 31.7%, and 37.3%, respectively. For SUVmean, SUVpeak, and SUVmax, the RCs were 24.4%, 25.3%, and 31.0%, respectively. All intraclass correlation coefficients were at least 0.97. Tumor volume delineations were quite repeatable, with an RC of 28.1% for individual lesion volumes and 17.0% for TTV. TTB had an RC of 23.2% and 33.4% when based on SUVmean and mean tumor-to-blood ratio, respectively. Small lesions (<4.2 cm3) had worse repeatability for volume measurements. The repeatability of SUVpeak, TLU, and all patient-level metrics was not affected by PSF reconstruction. Conclusion:18F-DCFPyL uptake measurements are quite repeatable and can be used for clinical validation in future treatment response assessment studies. Patient-based TTV may be preferred for multicenter studies because its repeatability was both high and robust to different image reconstructions.

Sensitivity of 18F-fluorodihydrotestosterone PET-CT to count statistics and reconstruction protocol in metastatic castration-resistant prostate cancer.

OBJECTIVES: Whole body [18F]-fluorodihydrotestosterone positron emission tomography ([18F]FDHT PET) imaging directly targets the androgen receptor and is a promising prognostic and predictive biomarker in metastatic castration-resistant cancer (mCRPC). To optimize [18F]FDHT PET-CT for diagnostic and response assessment purposes, we assessed how count statistics and reconstruction protocol affect its accuracy, repeatability, and lesion detectability. METHODS: Whole body [18F]FDHT PET-CT scans were acquired on an analogue PET-CT on two consecutive days in 14 mCRPC patients harbouring a total of 336 FDHT-avid lesions. Images were acquired at 45 min post-injection of 200 MBq [18F]FDHT at 3 min per bed position. List-mode PET data were split on a count-wise basis, yielding two statistically independent scans with each 50% of counts. Images were reconstructed according to current EANM Research Ltd. (EARL1, 4 mm voxel) and novel EARL2 guidelines (4 mm voxel + PSF). Per lesion, we measured SUVpeak, SUVmax, SUVmean, and contrast-to-noise ratio (CNR). SUV was normalized to dose per bodyweight as well as to the parent plasma input curve integral. Variability was assessed with repeatability coefficients (RCs). RESULTS: Count reduction increased liver coefficient of variation from 9.0 to 12.5% and from 10.8 to 13.2% for EARL1 and EARL2, respectively. SUVs of EARL2 images were 12.0-21.7% higher than EARL1. SUVs of 100% and 50% count data were highly correlated (R2 > 0.98; slope = 0.97-1.01; ICC = 0.99-1.00). Intrascan variability was volume-dependent, and count reduction resulted in higher intrascan variability for EARL2 than EARL1 images. Intrascan RCs were lowest for SUVmean (8.5-10.6%), intermediate for SUVpeak (12.0-16.0%), and highest for SUVmax (17.8-22.2%). Count reduction increased test-retest variance non-significantly (p > 0.05) for all SUV types and normalizations. For SUVpeak at 50% of counts, RCs remained < 30% when small lesions were excluded. Splitting data reduced CNR by median 4.6% (interquartile range 1.2-8.7%) and 4.6% (interquartile range 1.2-8.7%) for EARL1 and EARL2 images, respectively. CONCLUSIONS: Reducing [18F]FDHT PET acquisition time from 3 min to 1.5 per bed position resulted in a repeatability of SUVpeak (bodyweight) remaining ≤ 30%, which is generally acceptable for response monitoring purposes. However, EARL2 reconstruction was more affected, especially for SUVmax whose repeatability tended to exceed 30%. Lesion detectability was only slightly impaired by reducing acquisition time, which might not be clinically relevant in mCRPC.

Simplified Methods for Quantification of 18F-DCFPyL Uptake in Patients with Prostate Cancer.

Radiolabeled prostate-specific membrane antigen (PSMA) PET has demonstrated promising results for prostate cancer (PCa) imaging. Quantification of PSMA radiotracer uptake is desired as it enables reliable interpretation of PET images, use of PSMA uptake as an imaging biomarker for tumor characterization, and evaluation of treatment effects. The aim of this study was to perform a full pharmacokinetic analysis of 2-(3-(1-carboxy-5-[(6-18F-fluoro-pyridine-3-carbonyl)-amino]-pentyl)-ureido)-pentanedioic acid (18F-DCFPyL), a second-generation 18F-labeled PSMA ligand. On the basis of the pharmacokinetic analysis (reference method), simplified methods for quantification of 18F-DCFPyL uptake were validated. Methods: Eight patients with metastasized PCa were included. Dynamic PET acquisitions were performed at 0-60 and 90-120 min after injection of a median dose of 313 MBq of 18F-DCFPyL (range, 292-314 MBq). Continuous and manual arterial blood sampling provided calibrated plasma tracer input functions. Time-activity curves were derived for each PCa metastasis, and 18F-DCFPyL kinetics were described using standard plasma input tissue-compartment models. Simplified methods for quantification of 18F-DCFPyL uptake (SUVs; tumor-to-blood ratios [TBRs]) were correlated with kinetic parameter estimates obtained from full pharmacokinetic analysis. Results: In total, 46 metastases were evaluated. A reversible 2-tissue-compartment model was preferred for 18F-DCFPyL kinetics in 59% of the metastases. The observed k4 was small, however, resulting in nearly irreversible kinetics during the course of the PET study. Hence, k4 was fixated (0.015) and net influx rate, Ki, was preferred as the reference kinetic parameter. Whole-blood TBR provided an excellent correlation with Ki from full kinetic analysis (R2 = 0.97). This TBR could be simplified further by replacing the blood samples with an image-based, single measurement of blood activity in the ascending aorta (image-based TBR, R2 = 0.96). SUV correlated poorly with Ki (R2 = 0.47 and R2 = 0.60 for SUV normalized to body weight and lean body mass, respectively), most likely because of deviant blood activity concentrations (i.e., tumor tracer input) in patients with higher tumor volumes. Conclusion:18F-DCFPyL kinetics in PCa metastases are best described by a reversible 2-tissue-compartment model. Image-based TBRs were validated as a simplified method to quantify 18F-DCFPyL uptake and might be applied to clinical, whole-body PET scans. SUV does not provide reliable quantification of 18F-DCFPyL uptake.

Healthy Tissue Uptake of 68Ga-Prostate-Specific Membrane Antigen, 18F-DCFPyL, 18F-Fluoromethylcholine, and 18F-Dihydrotestosterone.

PET is increasingly used for prostate cancer (PCa) diagnostics. Important PCa radiotracers include 68Ga-prostate-specific membrane antigen HBED-CC (68Ga-PSMA), 18F-DCFPyL, 18F-fluoromethylcholine (18F-FCH), and 18F-dihydrotestosterone (18F-FDHT). Knowledge on the variability of tracer uptake in healthy tissues is important for accurate PET interpretation, because malignancy is suspected only if the uptake of a lesion contrasts with its background. Therefore, the aim of this study was to quantify uptake variability of PCa tracers in healthy tissues and identify stable reference regions for PET interpretation. Methods: A total of 232 PCa PET/CT scans from multiple hospitals was analyzed, including 87 68Ga-PSMA scans, 50 18F-DCFPyL scans, 68 18F-FCH scans, and 27 18F-FDHT scans. Tracer uptake was assessed in the blood pool, lung, liver, bone marrow, and muscle using several SUVs (SUVmax, SUVmean, SUVpeak). Variability in uptake between patients was analyzed using the coefficient of variation (COV%). For all tracers, SUV reference ranges (95th percentiles) were calculated, which could be applicable as image-based quality control for future PET acquisitions. Results: For 68Ga-PSMA, the lowest uptake variability was observed in the blood pool (COV, 19.9%), which was significantly more stable than all other tissues (COV, 29.8%-35.2%; P = 0.001-0.024). For 18F-DCFPyL, the lowest variability was observed in the blood pool and liver (COV, 14.4% and 21.7%, respectively; P = 0.001-0.003). The least variable 18F-FCH uptake was observed in the liver, blood pool, and bone marrow (COV, 16.8%-24.2%; P = 0.001-0.012). For 18F-FDHT, low uptake variability was observed in all tissues, except the lung (COV, 14.6%-23.6%; P = 0.001-0.040). The different SUV types had limited effect on variability (COVs within 3 percentage points). Conclusion: In this multicenter analysis, healthy tissues with limited uptake variability were identified, which may serve as reference regions for PCa PET interpretation. These reference regions include the blood pool for 68Ga-PSMA and 18F-DCFPyL and the liver for 18F-FCH and 18F-FDHT. Healthy tissue SUV reference ranges are presented and applicable as image-based quality control.

Prognostic Value of [18F]-Fluoromethylcholine Positron Emission Tomography/Computed Tomography Before Stereotactic Body Radiation Therapy for Oligometastatic Prostate Cancer.

PURPOSE: To investigate the predictive value of [18F]-fluoromethylcholine positron emission tomography/computed tomography (PET/CT)-derived parameters on progression-free survival (PFS) in oligometastatic prostate cancer patients treated with stereotactic body radiation therapy (SBRT). METHODS AND MATERIALS: In [18F]-fluoromethylcholine PET/CT scans of 40 consecutive patients with ≤4 metachronous metastases treated with SBRT we retrospectively measured the number of metastases, standardized uptake values (SUVmean, SUVmax, SUVpeak), metabolically active tumor volume (MATV), and total lesion choline uptake. Partial-volume correction was applied using the iterative deconvolution Lucy-Richardson algorithm. RESULTS: Thirty-seven lymph node and 13 bone metastases were treated with SBRT. Thirty-three patients (82.5%) had 1 lesion, 4 (10%) had 2 lesions, and 3 (7.5%) had 3 lesions. After a median follow-up of 32.6 months (interquartile range, 35.5 months), the median PFS was 11.5 months (95% confidence interval 8.4-14.6 months). Having more than a single metastasis was a significant prognostic factor (hazard ratio 2.74; P = .03), and there was a trend in risk of progression for large MATV (hazard ratio 1.86; P = .10). No SUV or total lesion choline uptake was significantly predictive for PFS, regardless of partial-volume correction. All PET semiquantitative parameters were significantly correlated with each other (P ≤ .013). CONCLUSIONS: The number of choline-avid metastases was a significant prognostic factor for progression after [18F]-fluormethylcholine PET/CT-guided SBRT for recurrent oligometastatic prostate cancer, and there seemed to be a trend in risk of progression for patients with large MATVs. The lesional level of [18F]-fluoromethylcholine uptake was not prognostic for progression.

Impact of partial-volume correction in oncological PET studies: a systematic review and meta-analysis.

PURPOSE: Positron-emission tomography can be useful in oncology for diagnosis, (re)staging, determining prognosis, and response assessment. However, partial-volume effects hamper accurate quantification of lesions <2-3× the PET system's spatial resolution, and the clinical impact of this is not evident. This systematic review provides an up-to-date overview of studies investigating the impact of partial-volume correction (PVC) in oncological PET studies. METHODS: We searched in PubMed and Embase databases according to the PRISMA statement, including studies from inception till May 9, 2016. Two reviewers independently screened all abstracts and eligible full-text articles and performed quality assessment according to QUADAS-2 and QUIPS criteria. For a set of similar diagnostic studies, we statistically pooled the results using bivariate meta-regression. RESULTS: Thirty-one studies were eligible for inclusion. Overall, study quality was good. For diagnosis and nodal staging, PVC yielded a strong trend of increased sensitivity at expense of specificity. Meta-analysis of six studies investigating diagnosis of pulmonary nodules (679 lesions) showed no significant change in diagnostic accuracy after PVC (p = 0.222). Prognostication was not improved for non-small cell lung cancer and esophageal cancer, whereas it did improve for head and neck cancer. Response assessment was not improved by PVC for (locally advanced) breast cancer or rectal cancer, and it worsened in metastatic colorectal cancer. CONCLUSIONS: The accumulated evidence to date does not support routine application of PVC in standard clinical PET practice. Consensus on the preferred PVC methodology in oncological PET should be reached. Partial-volume-corrected data should be used as adjuncts to, but not yet replacement for, uncorrected data.

Benefits of Using Stereotactic Body Radiotherapy in Patients With Metachronous Oligometastases of Hormone-Sensitive Prostate Cancer Detected by [18F]fluoromethylcholine PET/CT.

INTRODUCTION: For patients with oligometastatic recurrence of prostate cancer (PC), stereotactic body radiation therapy (SBRT) represents an attractive treatment option, as it is safe without major side effects. The aim of this study was to investigate the impact of SBRT in delaying the start of androgen deprivation therapy (ADT). PATIENTS AND METHODS: Forty-three patients treated with SBRT for oligometastatic recurrence (< 5 metastases) of hormone-sensitive PC, defined with [18F]fluoromethylcholine positron emission tomography/computed tomography were included. As a control group, 20 patients with oligometastatic disease not treated with SBRT were identified from another hospital. Data were collected retrospectively. RESULTS: A post-SBRT prostate-specific antigen (PSA) response was seen in 29 (67.4%) of 43 patients. Median ADT-free survival (ADT-FS) was 15.6 months (95% confidence interval [CI], 11.7-19.5) for the whole group, and 25.7 months (95% CI, 9.0-42.4) for patients with a PSA response. Seven patients were treated with a second course of SBRT because of oligometastatic disease recurrence; the ADT-FS in this group was 32.1 months (95% CI, 7.8-56.5). Compared with the control group, the ADT-FS from first diagnosis of metastasis was significantly longer, with 17.3 (95% CI, 13.7-20.9) months versus 4.19 months (95% CI, 0.0-9.0), P < .001. Also, time between diagnosis of the metastasis until progression of disease during ADT use (castration resistance) was longer for the SBRT-treated patients (mean 66.6, 95% CI, 53.5-79.8, vs. 36.41, 95% CI, 26.0-46.8 months, P = .020). There were no grade III or IV adverse events reported. CONCLUSION: SBRT can safely and effectively be used to postpone ADT in appropriately selected patients with oligometastatic recurrence of PC.

Baseline and longitudinal variability of normal tissue uptake values of [18F]-fluorothymidine-PET images.

PURPOSE: [18F]-fluorothymidine ([18F]-FLT) is a PET-tracer enabling in-vivo visualization and quantification of tumor cell proliferation. For qualitative and quantitative analysis, adequate knowledge of normal tissue uptake is indispensable. This study aimed to quantitatively investigate baseline tracer uptake of blood pool, lung, liver and bone marrow and their precision, and to assess the longitudinal effect of systemic treatment on biodistribution. METHODS: 18F-FLT-PET(/CT) scans (dynamic or static) of 90 treatment-naïve oncological patients were retrospectively evaluated. Twenty-three patients received double baseline scans, and another 39 patients were also scanned early and late during systemic treatment with a tyrosine kinase inhibitor. Reproducible volume of interest were placed in blood pool, lung, liver, and bone marrow. For semi-quantitative analysis, SUVmean, SUVmax, and SUVpeak with several normalizations were derived. RESULTS: SUVs of basal lung, liver, and bone marrow were not significantly different between averaged dynamic and static images, in contrast with blood pool and apical lung. Highest repeatability was seen for liver and bone marrow, with repeatability coefficients of 18.6% and 20.4% when using SUVpeak. Systemic treatment with TKIs both increased and decreased normal tissue tracer uptake at early and late time points during treatment. CONCLUSION: Simultaneous evaluation of liver and bone marrow uptake in longitudinal response studies may be used to assess image quality, where changes in uptake outside repeatability limits should trigger investigators to perform additional quality control on individual PET images. ADVANCES IN KNOWLEDGE: For [18F]-FLT PET images, liver and bone marrow have low intra-patient variability when quantified with SUVpeak, but may be affected by systemic treatment. IMPLICATIONS FOR PATIENT CARE: In [18F]-FLT-PET response monitoring trials, liver and bone marrow uptake may be used for quality control of [18F]-FLT PET images.

Accuracy and Precision of Partial-Volume Correction in Oncological PET/CT Studies.

Accurate quantification of tracer uptake in small tumors using PET is hampered by the partial-volume effect as well as by the method of volume-of-interest (VOI) delineation. This study aimed to investigate the effect of partial-volume correction (PVC) combined with several VOI methods on the accuracy and precision of quantitative PET. METHODS: Four image-based PVC methods and resolution modeling (applied as PVC) were used in combination with several common VOI methods. Performance was evaluated using simulations, phantom experiments, and clinical repeatability studies. Simulations were based on a whole-body 18F-FDG PET scan in which differently sized spheres were placed in lung and mediastinum. A National Electrical Manufacturers Association NU2 quality phantom was used for the experiments. Repeatability data consisted of an 18F-FDG PET/CT study on 11 patients with advanced non-small cell lung cancer and an 18F-fluoromethylcholine PET/CT study on 12 patients with metastatic prostate cancer. RESULTS: Phantom data demonstrated that most PVC methods were strongly affected by the applied resolution kernel, with accuracy differing by about 20%-50% between full-width-at-half-maximum settings of 5.0 and 7.5 mm. For all PVC methods, large differences in accuracy were seen among all VOI methods. Additionally, the image-based PVC methods were observed to have variable sensitivity to the accuracy of the VOI methods. For most PVC methods, accuracy was strongly affected by more than a 2.5-mm misalignment of true (simulated) VOI. When the optimal VOI method for each PVC method was used, high accuracy could be achieved. For example, resolution modeling for mediastinal lesions and iterative deconvolution for lung lesions were 99% ± 1.5% and 99% ± 0.9% accurate, respectively, for spheres 15-40 mm in diameter. Precision worsened slightly for resolution modeling and to a larger extent for some image-based PVC methods. Uncertainties in delineation propagated into uncertainties in PVC performance, as confirmed by the clinical data. CONCLUSION: The accuracy and precision of the tested PVC methods depended strongly on VOI method, resolution settings, contrast, and spatial alignment of the VOI. PVC has the potential to substantially improve the accuracy of tracer uptake assessment, provided that robust and accurate VOI methods become available. Commonly used delineation methods may not be adequate for this purpose.