Abstract: Comparison studies using histopathology as standard of reference enable a validation of the diagnostic performance of imaging methods. This study analysed (1) the impact of different image-histopathology co-registration pathways, (2) the impact of the applied data analysis method and (3) intraindividually compared multiparametric magnet resonance tomography (mpMRI) and prostate specific membrane antigen positron emission tomography (PSMA-PET) by using the different approaches. Ten patients with primary PCa who underwent mpMRI and [18F]PSMA-1007 PET/CT followed by prostatectomy were prospectively enrolled. We demonstrate that the choice of the intermediate registration step [(1) via ex-vivo CT or (2) mpMRI] does not significantly affect the performance of the registration framework. Comparison of analysis methods revealed that methods using high spatial resolutions e.g. quadrant-based slice-by-slice analysis are beneficial for a differentiated analysis of performance, compared to methods with a lower resolution (segment-based analysis with 6 or 18 segments and lesions-based analysis). Furthermore, PSMA-PET outperformed mpMRI for intraprostatic PCa detection in terms of sensitivity (median %: 83-85 vs. 60-69, p

Abstract: Purpose: Accurate contouring of intraprostatic gross tumor volume (GTV) is pivotal for successful delivery of focal therapies and for biopsy guidance in patients with primary prostate cancer (PCa). Contouring of GTVs, using 18-Fluor labeled tracer prostate specific membrane antigen positron emission tomography ([18F]PSMA-1007/PET) has not been examined yet. Patients and Methods: Ten Patients with primary PCa who underwent [18F]PSMA-1007 PET followed by radical prostatectomy were prospectively enrolled. Coregistered histopathological gross tumor volume (GTV-Histo) was used as standard of reference. PSMA-PET images were contoured on two ways: (1) manual contouring with PET scaling SUVmin-max: 0-10 was performed by three teams with different levels of experience. Team 1 repeated contouring at a different time point, resulting in n = 4 manual contours. (2) Semi-automatic contouring approaches using SUVmax thresholds of 20-50% were performed. Interobserver agreement was assessed for manual contouring by calculating the Dice Similarity Coefficient (DSC) and for all approaches sensitivity, specificity were calculated by dividing the prostate in each CT slice into four equal quadrants under consideration of histopathology as standard of reference. Results: Manual contouring yielded an excellent interobserver agreement with a median DSC of 0.90 (range 0.87-0.94). Volumes derived from scaling SUVmin-max 0-10 showed no statistically significant difference from GTV-Histo and high sensitivities (median 87%, range 84-90%) and specificities (median 96%, range 96-100%). GTVs using semi-automatic segmentation applying a threshold of 20-40% of SUVmax showed no significant difference in absolute volumes to GTV-Histo, GTV-SUV50% was significantly smaller. Best performing semi-automatic contour (GTV-SUV20%) achieved high sensitivity (median 93%) and specificity (median 96%). There was no statistically significant difference to SUVmin-max 0-10. Conclusion: Manual contouring with PET scaling SUVmin-max 0-10 and semi-automatic contouring applying a threshold of 20% of SUVmax achieved high sensitivities and very high specificities and are recommended for [18F]PSMA-1007 PET based focal therapy approaches. Providing high specificities, semi-automatic approaches applying thresholds of 30-40% of SUVmax are recommend for biopsy guidance

Abstract: Purpose This study aims to evaluate the association of the maximum standardized uptake value (SUVmax) in positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET) prior to salvage radiotherapy (sRT) on biochemical recurrence free survival (BRFS) in a large multicenter cohort. Methods Patients who underwent 68 Ga-PSMA11-PET prior to sRT were enrolled in four high-volume centers in this retrospective multicenter study. Only patients with PET-positive local recurrence (LR) and/or nodal recurrence (NR) within the pelvis were included. Patients were treated with intensity-modulated-sRT to the prostatic fossa and elective lymphatics in case of nodal disease. Dose escalation was delivered to PET-positive LR and NR. Androgen deprivation therapy was administered at the discretion of the treating physician. LR and NR were manually delineated and SUVmax was extracted for LR and NR. Cox-regression was performed to analyze the impact of clinical parameters and the SUVmax-derived values on BRFS. Results Two hundred thirty-five patients with a median follow-up (FU) of 24 months were included in the final cohort. Two-year and 4-year BRFS for all patients were 68% and 56%. The presence of LR was associated with favorable BRFS (p = 0.016). Presence of NR was associated with unfavorable BRFS (p = 0.007). While there was a trend for SUVmax values ≥ median (p = 0.071), SUVmax values ≥ 75% quartile in LR were significantly associated with unfavorable BRFS (p = 0.022, HR: 2.1, 95%CI 1.1-4.6). SUVmax value in NR was not significantly associated with BRFS. SUVmax in LR stayed significant in multivariate analysis (p = 0.030). Sensitivity analysis with patients for who had a FU of > 12 months (n = 197) confirmed these results. Conclusion The non-invasive biomarker SUVmax can prognosticate outcome in patients undergoing sRT and recurrence confined to the prostatic fossa in PSMA-PET. Its addition might contribute to improve risk stratification of patients with recurrent PCa and to guide personalized treatment decisions in terms of treatment intensification or de-intensification

Abstract: Background Multiparametric MRI (mpMRI) improves the detection of aggressive prostate cancer (PCa) subtypes. As cases of active surveillance (AS) increase and tumor progression triggers definitive treatment, we evaluated whether an AI-driven algorithm can detect clinically significant PCa (csPCa) in patients under AS. Methods Consecutive patients under AS who received mpMRI (PI-RADSv2.1 protocol) and subsequent MR-guided ultrasound fusion (targeted and extensive systematic) biopsy between 2017 and 2020 were retrospectively analyzed. Diagnostic performance of an automated clinically certified AI-driven algorithm was evaluated on both lesion and patient level regarding the detection of csPCa. Results Analysis of 56 patients resulted in 93 target lesions. Patient level sensitivity and specificity of the AI algorithm was 92.5%/31% for the detection of ISUP ≥ 1 and 96.4%/25% for the detection of ISUP ≥ 2, respectively. The only case of csPCa missed by the AI harbored only 1/47 Gleason 7a core (systematic biopsy; previous and subsequent biopsies rendered non-csPCa). Conclusions AI-augmented lesion detection and PI-RADS scoring is a robust tool to detect progression to csPCa in patients under AS. Integration in the clinical workflow can serve as reassurance for the reader and streamline reporting, hence improve efficiency and diagnostic confidence

Abstract: Prostate magnetic resonance imaging has become the imaging standard for prostate cancer in various clinical settings, with interpretation standardized according to the Prostate Imaging Reporting and Data System (PI-RADS). Each year, hundreds of scientific studies that report on the diagnostic performance of PI-RADS are published. To keep up with this ever-increasing evidence base, systematic reviews and meta-analyses are essential. As systematic reviews are highly resource-intensive, we investigated whether a machine learning framework can reduce the manual workload and speed up the screening process (title and abstract). We used search results from a living systematic review of the diagnostic performance of PI-RADS (1585 studies, of which 482 were potentially eligible after screening). A naïve Bayesian classifier was implemented in an active learning environment for classification of the titles and abstracts. Our outcome variable was the percentage of studies that can be excluded after 95% of relevant studies have been identified by the classifier (work saved over sampling: WSS@95%). In simulation runs of the entire screening process (controlling for classifier initiation and the frequency of classifier updating), we obtained a WSS@95% value of 28% (standard error of the mean ±0.1%). Applied prospectively, our classification framework would translate into a significant reduction in manual screening effort

Abstract: Background: In prostate cancer (PC) diagnosis, additional systematic biopsy (SB) is recommended to complement MRI-targeted biopsy (TB) to address the limited sensitivity of TB alone. The combination of TB+SB is beneficial for diagnosing additional significant PC (sPC) but harmful in terms of the additional diagnosis of indolent PC (iPC), morbidity, and resource expenditures. We aimed to investigate the benefit of additional SB and to identify predictors for this outcome. Methods: We analyzed the frequency of upgrading to sPC by additional SB in a retrospective single-center cohort of 1043 men. Regression analysis (RA) was performed to identify predictors for this outcome. Reclassification rates of ISUP grade groups between prostate biopsy and a subsequent radical prostatectomy were assessed. Results: Additional SB led to upgrading to sPC in 98/1043 men (9.4%) and to the additional diagnosis of iPC in 71/1043 (6.8%). In RA, men harboring a PI-RADS 2-4 lesion were more likely to have TB results upgraded by SB (p

Abstract: Introduction: An accurate delineation of the intraprostatic gross tumor volume (GTV) is of importance for focal treatment in patients with primary prostate cancer (PCa). Multiparametric MRI (mpMRI) is the standard of care for lesion detection but has been shown to underestimate GTV. This study investigated how far the GTV has to be expanded in MRI in order to reach concordance with the histopathological reference and whether this strategy is practicable in clinical routine. Patients and Methods: Twenty-two patients with planned prostatectomy and preceded 3 Tesla mpMRI were prospectively examined. After surgery, PCa contours delineated on histopathological slides (GTV-Histo) were superimposed on MRI using ex-vivo imaging as support for co-registration. According to the PI-RADSv2 classification, GTV was manually delineated in MRI (GTV-MRI) by two experts in consensus. For volumetric analysis, we compared GTV-MRI and GTV-Histo. Subsequently, we isotropically enlarged GTV-MRI in 1 mm increments within the prostate and also compared those with GTV-Histo regarding the absolute volumes. For evaluating the spatial accuracy, we considered the coverage ratio of GTV-Histo, the Sørensen-Dice coefficient (DSC), as well as the contact with the urethra. Results: In 19 of 22 patients MRI underestimated the intraprostatic tumor volume compared to histopathological reference: median GTV-Histo (4.7 cm3, IQR: 2.5-18.8) was significantly (p0.001) lager than median GTV-MRI (2.6 cm3, IQR: 1.2-6.9). A median expansion of 1 mm (range: 0-4 mm) adjusted the initial GTV-MRI to at least the volume of GTV-Histo (GTVexp-MRI). Original GTV-MRI and expansion with 1, 2, 3, and 4 mm covered in median 39% (IQR: 2%-78%), 62% (10%-91%), 70% (15%-95%), 80% (21-100), 87% (25%-100%) of GTV-Histo, respectively. Best DSC (median: 0.54) between GTV-Histo and GTV-MRI was achieved by median expansion of 2 mm. The urethra was covered by initial GTVs-MRI in eight patients (36%). After applying an expansion with 2 mm the urethra was covered in one more patient by GTV-MRI.br