Detection Of Prostate Cancer Lesions Research Articles

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.

Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materials and Methods This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 (P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 (P < .001) for lesions with PI-RADS scores of 4 or greater. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024.

Deep learning-based whole-body characterization of prostate cancer lesions on [68Ga]Ga-PSMA-11 PET/CT in patients with post-prostatectomy recurrence.

The automatic segmentation and detection of prostate cancer (PC) lesions throughout the body are extremely challenging due to the lesions' complexity and variability in appearance, shape, and location. In this study, we investigated the performance of a three-dimensional (3D) convolutional neural network (CNN) to automatically characterize metastatic lesions throughout the body in a dataset of PC patients with recurrence after radical prostatectomy. We retrospectively collected [68Ga]Ga-PSMA-11 PET/CT images from 116 patients with metastatic PC at two centers: center 1 provided the data for fivefold cross validation (n = 78) and internal testing (n = 19), and center 2 provided the data for external testing (n = 19). PET and CT data were jointly input into a 3D U-Net to achieve whole-body segmentation and detection of PC lesions. The performance in both the segmentation and the detection of lesions throughout the body was evaluated using established metrics, including the Dice similarity coefficient (DSC) for segmentation and the recall, precision, and F1-score for detection. The correlation and consistency between tumor burdens (PSMA-TV and TL-PSMA) calculated from automatic segmentation and artificial ground truth were assessed by linear regression and Bland‒Altman plots. On the internal test set, the DSC, precision, recall, and F1-score values were 0.631, 0.961, 0.721, and 0.824, respectively. On the external test set, the corresponding values were 0.596, 0.888, 0.792, and 0.837, respectively. Our approach outperformed previous studies in segmenting and detecting metastatic lesions throughout the body. Tumor burden indicators derived from deep learning and ground truth showed strong correlation (R2 ≥ 0.991, all P < 0.05) and consistency. Our 3D CNN accurately characterizes whole-body tumors in relapsed PC patients; its results are highly consistent with those of manual contouring. This automatic method is expected to improve work efficiency and to aid in the assessment of tumor burden.

Prostate cancer lesion detection, volume quantification and high-grade cancer differentiation using cancer risk maps derived from multiparametric MRI with histopathology as the reference standard

Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and location of both all PCa and high grade (Gleason grade group ≥ 3) PCa. Such capabilities would aid physicians and patients in treatment decisions, targeting biopsy, and planning focal therapy. A cohort of men with biopsy proven prostate cancer and pre-prostatectomy mpMRI were studied. PCa and benign ROIs (1524) were identified on mpMRI and histopathology with histopathology serving as the reference standard. Logistic regression models were created to differentiate PCa from benign tissues. The MRI images were registered to ensure correct overlay. The cancer models were applied to each image voxel within prostates to create probability maps of cancer and of high-grade cancer. Use of an optimum probability threshold quantified PCa volume for all lesions >0.1 cc. Accuracies were calculated using area under the curve (AUC) for the receiver operating characteristic (ROC). The PCa models utilized apparent diffusion coefficient (ADC), T2 weighted (T2W), dynamic contrast-enhanced MRI (DCE MRI) enhancement slope, and DCE MRI washout as the statistically significant MRI scans. Application of the PCa maps method provided total PCa volume and individual lesion volumes. The AUCs derived from lesion analysis were 0.91 for all PCa and 0.73 for high-grade PCa. At the optimum threshold, the PCa maps detected 135 / 150 (90%) histopathological lesions >0.1 cc. This study showed the feasibility of cancer risk maps, created from pre-prostatectomy, mpMR images validated with histopathology, to detect PCa lesions >0.1 cc. The method quantified the volume of cancer within the prostate. Method improvements were identified by determining root causes for over and underestimation of cancer volumes. The maps have the potential for improved non-invasive capability in quantitative detection, localization, volume estimation, and MRI characterization of PCa.

A General Bayesian Functional Spatial Partitioning Method for Multiple Region Discovery Applied to Prostate Cancer MRI.

Current protocols to estimate the number, size, and location of cancerous lesions in the prostate using multiparametric magnetic resonance imaging (mpMRI) are highly dependent on reader experience and expertise. Automatic voxel-wise cancer classifiers do not directly provide estimates of number, location, and size of cancerous lesions that are clinically important. Existing spatial partitioning methods estimate linear or piecewise-linear boundaries separating regions of local stationarity in spatially registered data and are inadequate for the application of lesion detection. Frequentist segmentation and clustering methods often require pre-specification of the number of clusters and do not quantify uncertainty. Previously, we developed a novel Bayesian functional spatial partitioning method to estimate the boundary surrounding a single cancerous lesion using data derived from mpMRI. We propose a Bayesian functional spatial partitioning method for multiple lesion detection with an unknown number of lesions. Our method utilizes functional estimation to model the smooth boundary curves surrounding each cancerous lesion. In a Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) framework, we develop novel jump steps to jointly estimate and quantify uncertainty in the number of lesions, their boundaries, and the spatial parameters in each lesion. Through simulation we show that our method is robust to the shape of the lesions, number of lesions, and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using MRI.

A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI.

Spatial partitioning methods correct for nonstationarity in spatially related data by partitioning the space into regions of local stationarity. Existing spatial partitioning methods can only estimate linear partitioning boundaries. This is inadequate for detecting an arbitrarily shaped anomalous spatial region within a larger area. We propose a novel Bayesian functional spatial partitioning (BFSP) algorithm, which estimates closed curves that act as partitioning boundaries around anomalous regions of data with a distinct distribution or spatial process. Our method utilizes transitions between a fixed Cartesian and moving polar coordinate system to model the smooth boundary curves using functional estimation tools. Using adaptive Metropolis-Hastings, the BFSP algorithm simultaneously estimates the partitioning boundary and the parameters of the spatial distributions within each region. Through simulation we show that our method is robust to shape of the target zone and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using magnetic resonanceimaging.

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

Artificial intelligence development for detecting prostate cancer in MRI

BackgroundArtificial intelligence (AI) is the recently advanced technology in machine learning which is increasingly used to help radiologists, especially when working in arduous conditions. Microsoft Corporation offered a free-trial service calling Custom Vision to develop AI for images.ResultsThis study included 161 prostate cancer images with 189 lesions from 52 patients. The 160-tag iteration presented the best performance: precision 20.0%, recall 6.3%, mean average precision (M.A.P.) 13.1%, and prediction rate 31.58%. The performance of a 1-h training was better than quick training, but was not different from a 2-h training.ConclusionHealth personnel can easily develop AI for the detection of prostate cancer lesions in MRI. However, the AI development is further required, and the result should be interpreted along with radiologist.

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve—AUC— = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.

EP006 - Artificial intelligence development for detecting prostate cancer in MRI

Artificial intelligence (AI) is the recently advanced technology in machine learning which is increasingly used to help radiologists, especially when working in arduous conditions. Microsoft Corporation offered a free-trial service calling Custom Vision to develop AI for images. This study included 161 prostate cancer images with 189 lesions from 52 patients. The 160-tag iteration presented the best performance: precision 20.0%, recall 6.3%, mean average precision (M.A.P.) 13.1%, and prediction rate 31.58%. The performance of a 1-h training was better than quick training, but was not different from a 2-h training. Health personnel can easily develop AI for the detection of prostate cancer lesions in MRI. However, the AI development is further required, and the result should be interpreted along with radiologist.

Radiolabelled PSMA PET/CT in breast cancer. A systematic review.

Radiolabelled prostate-specific membrane antigen (PSMA)-based PET/CT is a whole-body imaging technique currently performed for the detection of prostate cancer lesions. PSMA has been also demonstrated to be expressed by the neovasculature of many other solid tumours. The aim of this review is to evaluate the possible diagnostic role of radiolabelled PSMA PET/CT in breast cancer. A comprehensive literature search of the PubMed/MEDLINE, Scopus, Embase and Cochrane Library databases was conducted to find relevant published articles about the diagnostic performance of radiolabelled PSMA PET/CT in breast cancer. The comprehensive computer literature search revealed 652 articles. On reviewing the titles and abstracts, 640 articles were excluded because the reported data were not within the field of interest of this review. Twelve articles were selected and retrieved in full-text version; no additional study was found when screening the references of these articles. In total, 12 articles were included in the systematic review. Further studies enrolling a wider population are needed to clarify the.

4D perfusion CT of prostate cancer for image-guided radiotherapy planning: A proof of concept study

PurposeAdvanced forms of prostate cancer (PCa) radiotherapy with either external beam therapy or brachytherapy delivery techniques aim for a focal boost and thus require accurate lesion localization and lesion segmentation for subsequent treatment planning. This study prospectively evaluated dynamic contrast-enhanced computed tomography (DCE-CT) for the detection of prostate cancer lesions in the peripheral zone (PZ) using qualitative and quantitative image analysis compared to multiparametric magnet resonance imaging (mpMRI) of the prostate.MethodsWith local ethics committee approval, 14 patients (mean age, 67 years; range, 57–78 years; PSA, mean 8.1 ng/ml; range, 3.5–26.0) underwent DCE-CT, as well as mpMRI of the prostate, including standard T2, diffusion-weighted imaging (DWI), and DCE-MRI sequences followed by transrectal in-bore MRI-guided prostate biopsy. Maximum intensity projections (MIP) and DCE-CT perfusion parameters (CTP) were compared between healthy and malignant tissue. Two radiologists independently rated image quality and the tumor lesion delineation quality of PCa using a five-point ordinal scale. MIP and CTP were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/area under the curve (AUC) analysis.ResultsThe PCa detection rate ranged between 57 to 79% for the two readers for DCE-CT and was 92% for DCE-MRI. DCE-CT perfusion parameters in PCa tissue in the PZ were significantly different compared to regular prostate tissue and benign lesions. Image quality and lesion visibility were comparable between DCE-CT and DCE-MRI (VGC: AUC 0.612 and 0.651, p>0.05).ConclusionOur preliminary results suggest that it is feasible to use DCE-CT for identification and visualization, and subsequent segmentation for focal radiotherapy approaches to PCa.

Diagnostic performance of Gallium-68 prostate-specific membrane antigen positron emission tomography-computed tomography in intermediate and high risk prostate cancer.

Gallium-68 prostate-specific membrane antigen positron emission tomography-computed tomography (Ga-68 PSMA PET/CT) is an imaging modality that promises improved sensitivity and specificity of detection of prostate cancer lesions based on their increased uptake of PSMA-based radiotracers. It remains an emerging modality that has not yet been endorsed in the guidelines for the management of prostate cancer pending more established evidence to prove its efficacy. The objective of the study is to assess the value of Ga-68 PSMA PET/CT in the detection and localization of patients diagnosed with intermediate or high risk prostate cancer.Twenty three patients with intermediate or high risk prostate cancer had undergone Ga-68 PSMA PET/CT imaging prior to robotic assisted radical prostatectomy. Surgical specimens were then submitted for histological examinations. Lesions visualized on PET/CT and histology were independently mapped unto a 36-segment (Prostate Imaging Reporting and Data System version 2 [PI-RADS v.2]) map of the prostate. Concordance of visualization on PET/CT as compared to the histology as gold standard reference was then assessed. Lesions visualized on PET/CT and histology were independently mapped unto a 36-segment (PI-RADS v.2) map of the prostate. Concordance of visualization on PET/CT as compared to the histology as gold standard reference was then assessed.Sensitivity for all lesions identified on Ga-68 PSMA PET/CT was 42.37%; specificity was 88.61%. Both parameters were higher when considering only index lesions for which sensitivity was 68.42% and specificity was 98.23%. Sensitivity for the index lesions in intermediate risk group was 53.2% and was higher in the high risk group reaching 83.33%.Ga-68 PSMA PET/CT provides accurate localization of tumor lesions in patients with intermediate and high risk prostate cancer.

Detection of prostate cancer lesions using Gallium-68 PSMA-11 PET in men with biochemical recurrence following radical prostatectomy.

236 Background: Conventional imaging techniques infrequently detect the site of prostate cancer disease in men with biochemical recurrence following radical prostatectomy (RP). We examine the use of Gallium-68 Prostate-Specific Membrane Antigen (PSMA) positron emission tomography (PET) to determine the site of disease recurrence in these men. Methods: We retrospectively reviewed men with persistently detectable prostate specific antigen (PSA) and those with formal biochemical recurrence (PSA ≥ 0.2 ng/mL) following RP who underwent PSMA PET scan under our institution's prospective trials (NCT02918357 &amp; NCT02611882). We assessed the location of detected recurrences, and examined the distribution of recurrence location (prostate bed, pelvic lymph nodes (LN), and extra-pelvic disease) according to PSA at time of PET scan. Results: One hundred and fifty-eight men underwent PSMA PET after RP, 46% of men had previously undergone either adjuvant or salvage radiation therapy. Eleven men (7%) had the scan for persistently detectable PSA &lt; 0.2 and 93% of men had biochemical recurrence. At time of RP, 66% of men had ≥pT3a disease, 40% had positive surgical margins, and 18% had positive lymph nodes. Gleason grade at RP was ≤3+3 in 5% of men, 3+4 in 27%, 4+3 in 32% and ≥4+4 in 36%. Overall, 77% of men had a lesion detectable on PSMA PET. Men with higher PSA at time of scan had a higher prevalence of lesions, with all men PSA &gt; 6.0 having at least one lesion on PSMA PET (Table). Overall, we found extra-pelvic lesions in 44% of all men, and 58% of men with PSA ≥ 1.0. These are areas not typically covered by salvage radiation fields. Conclusions: PSMA PET detects lesions in a high proportion of men with biochemical recurrence following RP, with many lesions outside of the pelvis. This may facilitate more precise targeting of treatment areas for these patients. Clinical trial information: NCT02918357. Clinical trial information: NCT02611882. [Table: see text]

PSMA-11-Derived Dual-Labeled PSMA Inhibitors for Preoperative PET Imaging and Precise Fluorescence-Guided Surgery of Prostate Cancer.

Resection of tumors using targeted dual-modality probes combining preoperative imaging with intraoperative guidance is of high clinical relevance and might considerably affect the outcome of prostate cancer therapy. This work aimed at the development of dual-labeled prostate-specific membrane antigen (PSMA) inhibitors derived from the established N,N'-bis[2-hydroxy-5-(carboxyethyl)benzyl]ethylenediamine-N,N'-diacetic acid (HBED-CC)-based PET tracer 68Ga-Glu-urea-Lys(Ahx)-HBED-CC (68Ga-PSMA-11) to allow accurate intraoperative detection of PSMA-positive tumors. Methods: A series of novel PSMA-targeting fluorescent dye conjugates of Glu-urea-Lys-HBED-CC was synthesized, and their biologic properties were determined in cell-based assays and confocal microscopy. As a preclinical proof of concept, specific tumor uptake, pharmacokinetics, and feasibility for intraoperative fluorescence guidance were investigated in tumor-bearing mice and healthy pigs. Results: The designed dual-labeled PSMA inhibitors exhibited high binding affinity and PSMA-specific effective internalization. Conjugation of fluorescein isothiocyanate (10.86 ± 0.94 percentage injected dose [%ID]/g), IRDye800CW (13.66 ± 3.73 %ID/g), and DyLight800 (15.62 ± 5.52 %ID/g) resulted in a significantly increased specific tumor uptake, whereas 68Ga-Glu-urea-Lys-HBED-CC-AlexaFluor488 (9.12 ± 5.47 %ID/g) revealed a tumor uptake similar to that of 68Ga-PSMA-11 (4.89 ± 1.34 %ID/g). The first proof-of-concept studies with the clinically relevant candidate 68Ga-Glu-urea-Lys-HBED-CC-IRDye800CW reinforced a fast, specific enrichment in PSMA-positive tumors, with rapid background clearance. With regard to intraoperative navigation, a specific fluorescence signal was detected in PSMA-expressing tissue. Conclusion: This study demonstrated that PSMA-11-derived dual-labeled dye conjugates are feasible for providing PSMA-specific pre-, intra-, and postoperative detection of prostate cancer lesions and have high potential for future clinical translation.

Intermediate-term outcome after PSMA-PET guided high-dose radiotherapy of recurrent high-risk prostate cancer patients

BackgroundBy the use of PSMA positron emission tomography (PET) detection of prostate cancer lesions with a high sensitivity and specificity combined with a favorable lesion to background contrast is feasible. Therefore, PSMA-PET is increasingly used for planning of radiotherapy treatment; however, any data on intermediate-term outcome is missing so far.MethodsPatients with high-risk or very high risk prostate cancer, referred for salvage radiotherapy (SRT, n = 22) between 2013 and 2015, underwent PSMA-PET prior to therapy. Irradiation was planned on PET data with boost to macroscopic tumors/metastases. Treatment related toxicity was measured using Common Terminology Criteria for Adverse Events (CTCAE, v4.0).ResultFindings in PSMA-PET led to treatment modifications in 77% of SRT patients compared to available CT information. One patient did not receive irradiation due to disseminated disease, the other patients received increased boost doses to macroscopic disease and/or inclusion of additional target volumes. Toxicity was low as only 2 patients reported toxicities > grade 1. With a Median follow-up time of 29 in patients that were not lost to follow-up, prolonged PSA responses below baseline were observed in the majority of patients (14 of 20). In hormone-naïve SRT patients (n = 11), radiotherapy led to prolonged PSA decrease in 8/11 patients, however with 3 of these 8 patients receiving repeated PSMA based irradiation of novel lesions during follow-up.ConclusionPSMA-PET guided planning of radiotherapy led to change of treatment in the majority of patients. Treatment related toxicity was well tolerated and promising results regarding intermediate-term PSA decrease were observed.Trial registrationNo trial registration was performed due to retrospective evaluation.

18F-DCFPyL PET/CT in the Detection of Prostate Cancer at 60 and 120 Minutes: Detection Rate, Image Quality, Activity Kinetics, and Biodistribution.

There is increasing interest in PET/CT with prostate-specific membrane antigen (PSMA) tracers for imaging of prostate cancer because of the higher detection rates of prostate cancer lesions than with PET/CT with choline. For 68Ga-PSMA-11 tracers, late imaging at 180 min after injection instead of imaging at 45-60 min after injection improves the detection of prostate cancer lesions. For 18F-DCFPyL, improved detection rates have recently been reported in a small pilot study. In this study, we report the effects of PET/CT imaging at 120 min after injection of 18F-DCFPyL in comparison to images acquired at 60 min after injection in a larger clinical cohort of 66 consecutive patients with histopathologically proven prostate cancer. Methods: Images were acquired 60 and 120 min after injection of 18F-DCFPyL. We report the positive lesions specified for anatomic locations (prostate, seminal vesicles, local lymph nodes, distant lymph nodes, bone, and others) at both time points by visual analysis, the image quality at both time points, and a semiquantitative analysis of the tracer activity in both prostate cancer lesions as well as normal tissues at both time points. Results: Our data showed a significantly increasing uptake of 18F-DCFPyL between 60 and 120 min after injection in 203 lesions characteristic for prostate cancer (median, 10.78 vs. 12.86, P < 0.001, Wilcoxon signed-rank test). By visual analysis, 38.5% of all patients showed more lesions using images at 120 min after injection than using images at 60 min after injection, and in 9.2% a change in TNM staging was found. All lesions seen on images 60 min after injection were also visible on images 120 min after injection. A significantly better mean signal-to-noise ratio of 11.93 was found for images acquired 120 min after injection (P < 0.001, paired t test; signal-to-noise ratio at 60 min after injection, 11.15). Conclusion:18F-DCFPyL PET/CT images at 120 min after injection yield a higher detection rate of prostate cancer characteristic lesions than images at 60 min after injection. Further studies are needed to elucidate the best imaging time point for 18F-DCFPyL.

PET imaging with 68Gallium-labelled ligand of prostate-specific membrane antigen (68Ga-HBED-PSMA) for staging of biochemical recurrent prostate cancer after radical prostatectomy.

5023 Background: Staging of recurrent prostate cancer (PCa) after radical prostatectomy (RP) remains challenging especially at low PSA values. Prostate-specific membrane antigen (PSMA) shows increased expression on most PCa and might therefore be useful for detection of PCa lesions. Aim of this retrospective analysis of consecutive pts was to evaluate the detection rate of PET hybrid imaging using the PSMA inhibitor Glu-NH-CO-NH-Lys-(Ahx)-[(68)Ga(HBED-CC)] (68Ga-HBED-PSMA) in pts with biochemical recurrence (BCR) of PCa after RP. Methods: 332 consecutive pts with BCR of PCa after RP and a median PSA value of 1.7 ng/ml (range 0.2-63 ng/ml) were included in this analysis. After injection of 122±17 MBq 68Ga-HBED-PSMA-PET/CT or PET/MR was performed in 256 pts and 76 pts, respectively. One nuclear medicine physician and one radiologist reviewed imaging in consensus. Detection rates according to PSA value were determined. Results: In total, detection rates for 68Ga-HBED-PSMA-PET hybrid imaging were 96.0% (143/1...

EO8 Initial experience with PSMA-radioguided surgery in prostate cancer patients

Introduction & Objectives: Staging of primary intermediate and high-risk prostate cancer patients before curative treatment usually comprises bone scintigraphy and CT orMRI of the abdomen. Recently, Glu-NH-CO-NH-Lys-(Ahx)-[(68)Ga(HBED-CC)] as a novel 68Galliumlabelled ligand of the prostate-specific membrane antigen (68GaHBED-PSMA) has been developed. First reports state high sensitivity and specificity in the detection of prostate cancer lesions. Thus, the aim of this prospective analysis was to investigate 68Ga-HBED-PSMA PET/MR for staging in this patient cohort and correlate it with the results of postoperative histological findings. Material & Methods: This analysis included 53 patients prior to planned radical prostatectomy with intermediate or high-risk prostate cancer according to d’Amico classification. After injection of 100–150 MBq 68Ga-HBED-PSMA a fully-diagnostic PET/MR including multiparametric prostate MRI was performed and images were reviewed by one nuclear medicine physician and one radiologist in consensus. Visualization of local tumor, evidence of lymph node or distantmetastaseswas evaluated and compared to postoperative histological findings. Results: Despite unremarkable conventional imaging 68Ga-HBEDPSMA PET/MRI revealed metastasized disease in two patients who therefore did not undergo radical prostatectomy. Tumor involvement of the prostate could be visualized by 68Ga-HBED-PSMA PET in 88.3% of patients (45/51). In patients with PSMA-positive primary tumor 68Ga-HBED-PSMA PET detected 11 out of 13 patients with histological lymph node involvement (sensitivity: 84.6%) and correctly classified 31 out of 32 patients without histological evidence of lymph node metastases (specificity: 96.6%; accuracy 93.3%). Of note, the two patients with lymph node metastases that were missed by 68GaHBED-PSMA PET only showed micrometastasis in one lymph node. Conclusions: In this initial series, 68Ga-HBED-PSMA PET/MRI showed a high detection rate of the primary tumor within the prostate and proved to be of high sensitivity, specificity and accuracy regarding lymph node staging. Therefore, 68Ga-HBED-PSMA PET/MRI as single investigation might have the potential to stage intermediate and high-risk prostate cancer patients more accurately than standard imaging.

Novel Preclinical and Radiopharmaceutical Aspects of [68Ga]Ga-PSMA-HBED-CC: A New PET Tracer for Imaging of Prostate Cancer.

The detection of prostate cancer lesions by PET imaging of the prostate-specific membrane antigen (PSMA) has gained highest clinical impact during the last years. 68Ga-labelled Glu-urea-Lys(Ahx)-HBED-CC ([68Ga]Ga-PSMA-HBED-CC) represents a successful novel PSMA inhibitor radiotracer which has recently demonstrated its suitability in individual first-in-man studies. The radiometal chelator HBED-CC used in this molecule represents a rather rarely used acyclic complexing agent with chemical characteristics favourably influencing the biological functionality of the PSMA inhibitor. The simple replacement of HBED-CC by the prominent radiometal chelator DOTA was shown to dramatically reduce the in vivo imaging quality of the respective 68Ga-labelled PSMA-targeted tracer proving that HBED-CC contributes intrinsically to the PSMA binding of the Glu-urea-Lys(Ahx) pharmacophore. Owing to the obvious growing clinical impact, this work aims to reflect the properties of HBED-CC as acyclic radiometal chelator and presents novel preclinical data and relevant aspects of the radiopharmaceutical production process of [68Ga]Ga-PSMA-HBED-CC.

Dual-Modality Image-Guided Surgery of Prostate Cancer with a Radiolabeled Fluorescent Anti-PSMA Monoclonal Antibody

Both radionuclide imaging and near-infrared fluorescent (NIRF) imaging have a high sensitivity to detect tumors in vivo. The combination of these modalities using dual-labeled antibodies may allow both preoperative and intraoperative tumor localization and may be used in image-guided surgery to ensure complete resection of tumor tissue. Here, we evaluated the potential of dual-modality imaging of prostate cancer with the monoclonal antibody D2B, directed against an extracellular domain of prostate-specific membrane antigen (PSMA). For these studies, D2B was labeled both with (111)In and with the NIRF dye IRDye800CW. D2B was conjugated with N-hydroxysuccinimide-IRDye800CW and p-isothiocyanatobenzyl-diethylenetriaminepentaacetic acid (ITC-DTPA) and subsequently radiolabeled with (111)In. For biodistribution and NIRF imaging, (111)In-DTPA-D2B-IRDye800CW (2 μg, 0.55 MBq/mouse) was injected intravenously into BALB/c nude mice with subcutaneous PSMA-expressing LNCaP tumors (right flank) and PSMA-negative PC3 tumors (left flank). The biodistribution was determined at 1, 2, 3, and 7 d after injection. In addition, micro-SPECT/CT and NIRF imaging with (111)In-DTPA-D2B-IRDye800CW (3 μg, 8.5 MBq/mouse) was performed on mice with intraperitoneally growing LS174T-PSMA tumors. (111)In-DTPA-D2B-IRDye800CW specifically accumulated in subcutaneous PSMA-positive LNCaP tumors (45.8 ± 8.0 percentage injected dose per gram at 168 h after injection), whereas uptake in subcutaneous PSMA-negative PC3 tumors was significantly lower (6.6 ± 1.3 percentage injected dose per gram at 168 h after injection). Intraperitoneal LS174T-PSMA tumors could be visualized specifically with both micro-SPECT/CT and NIRF imaging at 2 d after injection, and the feasibility of image-guided resection of intraperitoneal tumors was demonstrated in this model. Dual-labeled (111)In-DTPA-D2B-IRDye800CW enables specific and sensitive detection of prostate cancer lesions in vivo with micro-SPECT/CT and NIRF imaging. In addition to preoperative micro-SPECT/CT imaging to detect tumors, NIRF imaging enables image-guided surgical resection. These preclinical findings warrant clinical studies with (111)In-DTPA-D2B-IRDye800CW to improve tumor detection and resection in prostate cancer patients.