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.

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.

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. SUV_peak was plotted against TBR for the most intense lesion of each patient. The correlation of SUV_peak 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). SUV_peak correlated well with TBR in patients with PCa and a total tumor volume of less than 200 cm³ (R ² = 0.931). The correlation between SUV and TBR was not affected by disease setting, prostate-specific antigen level, or tumor volume. SUV_peak depended less on tracer uptake time than did TBR. Conclusion: For ¹⁸F-DCFPyL PET/CT, SUV_peak correlates strongly with TBR. Therefore, it is a valuable simplified, semiquantitative measurement in patients with low-volume PCa (<200 cm³). SUV_peak 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; SUV_mean, SUV_peak, and SUV_max 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 SUV_mean, SUV_peak, and SUV_max, 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 SUV_mean and mean tumor-to-blood ratio, respectively. Small lesions (<4.2 cm³) had worse repeatability for volume measurements. The repeatability of SUV_peak, 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.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0531-8) contains supplementary material, which is available to authorized users.

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 k ₄ was small, however, resulting in nearly irreversible kinetics during the course of the PET study. Hence, k ₄ was fixated (0.015) and net influx rate, K_i, was preferred as the reference kinetic parameter. Whole-blood TBR provided an excellent correlation with K_i from full kinetic analysis (R ² = 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, R ² = 0.96). SUV correlated poorly with K_i (R ² = 0.47 and R ² = 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.

Partial-volume correction in dynamic PET-CT: effect on tumor kinetic parameter estimation and validation of simplified metrics

Background

Partial-volume effects generally result in an underestimation of tumor tracer uptake on PET-CT for small lesions, necessitating partial-volume correction (PVC) for accurate quantification. However, investigation of PVC in dynamic oncological PET studies to date is scarce. The aim of this study was to investigate PVC’s impact on tumor kinetic parameter estimation from dynamic PET-CT acquisitions and subsequent validation of simplified semi-quantitative metrics. Ten patients with EGFR-mutated non-small cell lung cancer underwent dynamic ¹⁸F-fluorothymidine PET-CT before, 7 days after, and 28 days after commencing treatment with a tyrosine kinase inhibitor. Parametric PVC was applied using iterative deconvolution without and with highly constrained backprojection (HYPR) denoising, respectively. Using an image-derived input function with venous parent plasma calibration, we estimated full kinetic parameters V_T, K₁, and k₃/k₄ (BP_ND) using a reversible two-tissue compartment model, and simplified metrics (SUV and tumor-to-blood ratio) at 50–60 min post-injection.

Results

PVC had a non-linear effect on measured activity concentrations per timeframe. PVC significantly changed each kinetic parameter, with a median increase in V_T of 11.8% (up to 25.1%) and 10.8% (up to 21.7%) without and with HYPR, respectively. Relative changes in kinetic parameter estimates vs. simplified metrics after applying PVC were poorly correlated (correlations 0.36–0.62; p < 0.01). PVC increased correlations between simplified metrics and V_T from 0.82 and 0.81 (p < 0.01) to 0.90 and 0.88 (p < 0.01) for SUV and TBR, respectively, albeit non-significantly. PVC also increased correlations between treatment-induced changes in simplified metrics vs. V_T at 7 (SUV) and 28 (SUV and TBR) days after treatment start non-significantly. Delineation on partial-volume corrected PET images resulted in a median decrease in metabolic tumor volume of 14.3% (IQR − 22.1 to − 7.5%), and increased the effect of PVC on kinetic parameter estimates.

Conclusion

PVC has a significant impact on tumor kinetic parameter estimation from dynamic PET-CT data, which differs from its effect on simplified metrics. However, it affected validation of these simplified metrics both as single measurements and as biomarkers of treatment response only to a small extent. Future dynamic PET studies should preferably incorporate PVC.

Trial registration

Dutch Trial Register, NTR3557.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0483-z) contains supplementary material, which is available to authorized users.

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 (SUV_max, SUV_mean, SUV_peak). 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.

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.

Electronic supplementary material

The online version of this article (doi:10.1007/s00259-017-3775-4) contains supplementary material, which is available to authorized users.

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.

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.