ImportanceActive surveillance (AS) for patients with prostate cancer (PC) often includes fixed repeat prostate biopsies that do not account for the varying risk of reclassification to significant disease. Given the invasive nature and potential complications of biopsies, a personalized approach is needed to balance the burden of biopsies with the risk of missing disease progression.ObjectiveTo develop and externally validate a dynamic model that predicts an individual’s risk of PC reclassification during AS.Design, Setting, and ParticipantsThis prognostic study developed a dynamic prediction model using data from the Prostate Cancer Research International: Active Surveillance (PRIAS) study, which was initiated in 2006. Follow-up was truncated until April 2023. External validation was conducted using cohorts from the world’s largest centralized AS database, the Global Action Plan Prostate Cancer Active Surveillance initiative database. The PRIAS study is a multicenter, prospective, web-based cohort study monitoring patients undergoing AS, involving more than 175 academic, nonacademic, and private centers across 23 countries worldwide. For the development and external validation of the model, all patients diagnosed with Grade Group 1 PC who underwent at least 1 baseline or follow-up magnetic resonance imaging (MRI) and 1 follow-up biopsy were included. Data were analyzed from September 2023 to January 2024.ExposuresAS, including prostate-specific antigen (PSA) tests, MRI, and prostate biopsies according to a fixed follow-up schedule.Main Outcomes and MeasuresA joint model for longitudinal and time-to-event data was used to predict reclassification to Grade Group 2 or greater on repeat biopsy using predefined baseline and repeated clinical characteristics. Performance was assessed using time-dependent area under the receiver operating characteristic curve and negative predictive value.ResultsThe development cohort included 2512 patients (median [IQR] age, 65 [59-69] years). Characteristics significantly associated with a higher risk of reclassification were increased age, higher PSA and velocity, lower prostate volume, a suspicious lesion on MRI, and no previous negative biopsy findings. Depending on the threshold and time point used, the model demonstrated a negative predictive value of 86% to 97%. External validation included 3199 patients from 9 other cohorts. The time-dependent area under the curve ranged from 0.81 to 0.84 in the development cohort and 0.52 to 0.90 at external validation.Conclusions and RelevanceIn this prognostic study, the developed dynamic risk model effectively identified patients at low risk of PC reclassification during AS. After prospective validation, this model may support personalized, risk-based AS and reduce the burden of unnecessary biopsies.

The prognostication of individual disease trajectory and selection of optimal therapy in patients with localized, low-grade prostate cancer often presents significant difficulty. The phosphatase and tensin homolog on chromosome 10 (PTEN) has emerged as a potential novel biomarker in this clinical context, based on its demonstrated prognostic significance in multiple retrospective studies. Incorporation into standard clinical practice necessitates exceptional diagnostic accuracy, and PTEN’s binary readout—retention or loss—suggests its suitability as a biomarker. This multi-institutional ring trial aimed to validate the diagnostic precision of PTEN immunohistochemistry in localized, low- to intermediate-risk prostate cancer, across ten university pathology institutes in Germany. The trial incorporated 90 cases of patients diagnosed with acinar adenocarcinoma of the prostate of grade groups 1 (n = 8, 8.9

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Background Androgen signalling remains the seminal therapeutic approach for the management of advanced prostate cancer. However, most tumours eventually shift towards an aggressive phenotype, characterised by androgen independence and treatment resistance. The cyclic adenosine monophosphate (cAMP) pathway plays a crucial role in regulating various cellular processes, with the phosphodiesterase PDE4D7 being a vital modulator of cAMP signalling in prostate cancer cells. Methods Using shRNA-mediated PDE4D7 knockdown in LNCaP cells and downstream analysis via RNA sequencing and phenotypic assays, we replicate clinical observations that diminished PDE4D7 expression promotes an aggressive prostate cancer phenotype. Results Our study provides evidence that loss of PDE4D7 expression represents a pivotal switch driving the transition from an androgen-sensitive state to hormone unresponsiveness and neuroendocrine differentiation. In addition, we demonstrate that PDE4D7 loss affects DNA repair pathways, conferring resistance to poly ADP ribose polymerase (PARP) inhibitors. Conclusion Reinstating PDE4D7 expression sensitises prostate cancer cells to anti-androgens, DNA damage response inhibitors, and cytotoxic therapies. These findings provide significant insight into the regulatory role of PDE4D7 in the development of lethal prostate cancer and the potential of its modulation as a novel therapeutic strategy.

Background A human perception-based assessment of multi-parametric magnetic resonance imaging (mpMRI) of the prostate does not necessarily tap the full potential in determining prostate cancer (PCa) and identifying significant prostate cancer (sPCa). Methods Our multi-institutional international study includes 6,448 mpMRI prostate images from 1,830 patients (PCa diagnosis in 69.7% of patients). MR Images from a single institution were utilized for the model development and in-house validation, and from two international institutions for external validation. We utilized volumetric data, PlexusNET architecture, and attention algorithms to develop deep learning models. Performance was measured using the area under receiving characteristic operating curve (AUROC) and compared to the PI-RADS score system (version 2) at the case level for PCa diagnosis and sPCa identification. The reduction rate of biopsy settings without missing any PCa cases measured the clinical utility. Results Our compact models were internally and externally validated for a significant improvement in PCa detection by 7.25% compared to the PI-RADS score system. Following the model recommendation would avoid at least 11.3% of unnecessary biopsies. Moreover, the DL model correctly predicted PCa presence in 22.5% of cases, which were misclassified according to the PI-RADS score system. The identification accuracy of sPCa for the model was statistically significantly higher than PI-RADS scores (AUROC: 0.769 vs. 0.726; p < 0.021) on a PCa cohort with 79% sPCa. Conclusions Our solution facilitates mpMRI assessment of the prostate for PCa diagnosis and the determination of sPCa; we demonstrated a great potential of AI for clinical utility and improved mpMRI assessment.

Supplementary Figure S1 and Supplementary Tables S1-S3.

PURPOSE Development of intelligence systems for bladder lesion detection is cost intensive. An efficient strategy to develop such intelligence solutions is needed. MATERIALS AND METHODS We used four deep learning models (ConvNeXt, PlexusNet, MobileNet, and SwinTransformer) covering a variety of model complexity and efficacy. We trained these models on a previously published educational cystoscopy atlas (n = 312 images) to estimate the ratio between normal and cancer scores and externally validated on cystoscopy videos from 68 cases, with region of interest (ROI) pathologically confirmed to be benign and cancerous bladder lesions (ie, ROI). The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case. RESULTS Specificity was comparable between four models at frame (range, 30.0%-44.8%) and block levels (56%-67%). Although sensitivity at the frame level (range, 81.4%-88.1%) differed between the models, sensitivity at the block level (100%) and ROI level (100%) was comparable between these models. MobileNet and PlexusNet were computationally more efficient for real-time ROI detection than ConvNeXt and SwinTransformer. CONCLUSION Educational cystoscopy atlas and efficient models facilitate the development of real-time intelligence system for bladder lesion detection.

Abstract Androgen signalling remains the seminal therapeutic approach for management of advanced prostate cancer. However, most tumours eventually shift towards an aggressive phenotype, characterised by androgen-independence and treatment resistance. The cyclic adenosine monophosphate (cAMP) pathway plays a crucial role in regulating various cellular processes, with the phosphodiesterase PDE4D7 being a vital modulator of cAMP signalling in prostate cancer cells. Our study provides evidence that loss of PDE4D7 expression represents a pivotal switch driving the transition from an androgen-sensitive state to hormone unresponsiveness and neuroendocrine differentiation. Additionally, we demonstrate that PDE4D7 loss results affects DNA repair pathways, conferring resistance to poly ADP ribose polymerase (PARP) inhibitors. Reinstating PDE4D7 expression sensitises prostate cancer cells to anti-androgens, DNA damage response inhibitors, and cytotoxic therapies. These findings provide significant insight into the regulatory role of PDE4D7 in the development of lethal prostate cancer and the potential of its modulation as a novel therapeutic strategy.

You have accessJournal of UrologyCME1 Apr 2023MP60-18 EFFICIENT AUGMENTED INTELLIGENCE STRATEGY WITH POTENTIAL USE FOR REAL-TIME BLADDER TUMOR DETECTION Okyaz Eminaga, Timothy Lee, Mark Laurie, Jessie Ge, Vinh LA, Jin Long, Eugene Shkolyar, Xiao Jia, Axel Semjonow, Martin Bogemann, Hubert Lau, Lei Xing, and Joseph Liao Okyaz EminagaOkyaz Eminaga More articles by this author , Timothy LeeTimothy Lee More articles by this author , Mark LaurieMark Laurie More articles by this author , Jessie GeJessie Ge More articles by this author , Vinh LAVinh LA More articles by this author , Jin LongJin Long More articles by this author , Eugene ShkolyarEugene Shkolyar More articles by this author , Xiao JiaXiao Jia More articles by this author , Axel SemjonowAxel Semjonow More articles by this author , Martin BogemannMartin Bogemann More articles by this author , Hubert LauHubert Lau More articles by this author , Lei XingLei Xing More articles by this author , and Joseph LiaoJoseph Liao More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003318.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Development of intelligence systems for bladder tumor detection is cost- and labor-intensive. Specifically, image annotation is one of the most expensive tasks in the development of intelligence systems. Moreover, previous studies utilized selective screenshots or frame sequences for model development and validation, despite that cystoscopy is a dynamic visual inspection impacted by random noises. The current work proposes an efficient strategy to develop augmented intelligence strategy ready for real-time bladder tumor detection as computer-aided assistance tool for clinicians. METHODS: We used a previously published educational cystoscopy atlas (n=312 images) and our deep learning models (ConvNeXt, PlexusNet, MobileNet, SwinTransformer) covering a variety of model complexity and computation efficacy to estimate the ratio between cancer and normal confidence scores; We applied an image augmentation strategy called RandAugment to populate the training set for model training and externally validated on video records for the initial diagnostic cystoscopy prior to TURBT from 68 cases with benign and bladder cancers tumors (i.e., region of interest, ROI) at a single center. Each frame of the video was labeled by ROI status. ROI was confirmed by pathology examination and the Delphi method. The ROI status was predicted based on the ratio (if the ratio>1, then the frame is positive for ROI, otherwise negative). On external validation, areas of adequate illumination in video frames were considered as input for models. The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case. The block level split each full-length video into small segments according to the ROI status. RESULTS: Specificity was statistically comparable between four models at frame (range: 30.0–44.8%) and block levels (56–67%) by p>0.05. While sensitivity at frame level (range: 81.4 – 88.1) statistically differed between the models, sensitivity at block level (100%) and ROI level (100%) were comparable between these models, indicating that all ROI are detectable by these models and that the frame-level model performance is impacted by the random noises. MobileNet and PlexusNet were computationally more efficient (22 and 19 frames per second, retrospectively) and suitable for real-time detection task than ConvNeXt and SwinTransformer (13 and 17 frames per second, retrospectively). CONCLUSIONS: Educational cystoscopy atlas and cost-effective model development strategy facilitates the development of accurate and efficient intelligence systems with potential use for real-time bladder tumor detection. Source of Funding: The work was supported by National Institutes of Health R01 CA260426 to JCL. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e850 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Okyaz Eminaga More articles by this author Timothy Lee More articles by this author Mark Laurie More articles by this author Jessie Ge More articles by this author Vinh LA More articles by this author Jin Long More articles by this author Eugene Shkolyar More articles by this author Xiao Jia More articles by this author Axel Semjonow More articles by this author Martin Bogemann More articles by this author Hubert Lau More articles by this author Lei Xing More articles by this author Joseph Liao More articles by this author Expand All Advertisement PDF downloadLoading ...

Objectives: To investigate the association of the prognostic risk score CAPRA&PDE4D5/7/9 as measured on pre-surgical diagnostic needle biopsy tissue with pathological outcomes after radical prostatectomies in a clinically low−intermediate-risk patient cohort. Patients and Methods: RNA was extracted from biopsy punches of diagnostic needle biopsies. The patient cohort comprises n = 151 patients; of those n = 84 had low−intermediate clinical risk based on the CAPRA score and DRE clinical stage 2, or pathological pT stage > pT3a, or tumor penetrated prostate capsular status, or pN1 disease); (ii) any ISUP pathological Gleason >2; (iii) any ISUP pathological Gleason >1. In the n = 84 patients with low to intermediate clinical risk profiles, the clinical-genomics CAPRA&PDE4D5/7/9_BCR risk score was significantly lower in patients with favorable vs. unfavorable outcomes. In univariable logistic regression modeling the genomics PDE4D5/7/9_BCR as well as the clinical-genomics CAPRA&PDE4D5/7/9_BCR combination model were significantly associated with all three post-surgical pathology outcomes (p = 0.02, p = 0.0004, p = 0.04; and p = 0.01, p = 0.0002, p = 0.01, respectively). The clinically used PRIAS criteria for the selection of low-risk candidate patients for active surveillance (AS) were not significantly associated with any of the three tested post-operative pathology outcomes (p = 0.3, p = 0.1, p = 0.1, respectively). In multivariable analysis adjusted for the CAPRA score, the genomics PDE4D5/7/9_BCR risk score remained significant for the outcomes of adverse pathology (p = 0.04) and ISUP pathological Gleason >2 (p = 0.004). The negative predictive value of the CAPRA&PDE4D5/7/9_BCR risk score using the low-risk cut-off (0.1) for the three pathological endpoints was 82.0%, 100%, and 59.1%, respectively for a selected low-risk cohort of n = 22 patients (26.2% of the entire cohort) compared to 72.1%, 94.4%, and 55.6% for n = 18 low-risk patients (21.4% of the total cohort) selected based on the PRIAS inclusion criteria. Conclusion: In this study, we have shown that the previously reported clinical-genomics prostate cancer risk model CAPRA&PDE4D5/7/9_BCR which was developed to predict biological outcomes after surgery of primary prostate cancer is also significantly associated with post-surgical pathology outcomes. The risk score predicts adverse pathology independent of the clinical risk metrics. Compared to clinically used active surveillance inclusion criteria, the clinical-genomics CAPRA&PDE4D5/7/9_BCR risk model selects 22% (n = 8) more low-risk patients with higher negative predictive value to experience unfavorable post-operative pathology outcomes.

Background Revealing molecular mechanisms linked to androgen receptor activity can help to improve diagnosis and treatment of prostate cancer. Retinoic acid-induced 2 (RAI2) protein is thought to act as a transcriptional coregulator involved in hormonal responses and epithelial differentiation. We evaluated the clinical relevance and biological function of the RAI2 protein in prostate cancer. Methods We assessed RAI2 gene expression in the Cancer Genome Atlas prostate adenocarcinoma PanCancer cohort and protein expression in primary tumors (n = 199) by immunohistochemistry. We studied RAI2 gene expression as part of a multimarker panel in an enriched circulating tumor cell population isolated from blood samples (n = 38) of patients with metastatic prostate cancer. In prostate cancer cell lines, we analyzed the consequences of androgen receptor inhibition on RAI2 protein expression and the consequences of RAI2 depletion on the expression of the androgen receptor and selected target genes. Results Abundance of the RAI2 protein in adenocarcinomas correlated with the androgen receptor; keratins 8, 18, and 19; and E-cadherin as well as with an early biochemical recurrence. In circulating tumor cells, detection of RAI2 mRNA significantly correlated with gene expression of FOLH1, KLK3, RAI2, AR, and AR-V7. In VCaP and LNCaP cell lines, sustained inhibition of hormone receptor activity induced the RAI2 protein, whereas RAI2 depletion augmented the expression of MME, STEAP4, and WIPI1. Conclusions The RAI2 protein functions as a transcriptional coregulator of the androgen response in prostate cancer cells. Detection of RAI2 gene expression in blood samples from patients with metastatic prostate cancer indicated the presence of circulating tumor cells.

Background: There is ongoing discussion whether a multivariable approach including magnetic resonance imaging (MRI) can safely prevent unnecessary protocol-advised repeat biopsy during active surveillance (AS).Objective: To determine predictors for grade group (GG) reclassification in patients undergoing an MRI-informed prostate biopsy (MRI-Bx) during AS and to evaluate whether a confirmatory biopsy can be omitted in patients diagnosed with upfront MRI.Design, setting, and participants: The Prostate cancer Research International: Active Surveillance (PRIAS) study is a multicenter prospective study of patients on AS (www. prias-project.org). We selected all patients undergoing MRI-Bx (targeted +/- systematic biopsy) during AS.Outcome measurements and statistical analysis: A time-dependent Cox regression anal-ysis was used to determine the predictors of GG progression/reclassification in patients undergoing MRI-Bx. A sensitivity analysis and a multivariable logistic regression analysis were also performed.Results and limitations: A total of 1185 patients underwent 1488 MRI-Bx sessions. The time-dependent Cox regression analysis showed that age (per 10 yr, hazard ratio [HR] 0.84 [95% confidence interval {CI} 0.71-0.99]), MRI outcome (Prostate Imaging Reporting and Data System [PIRADS] 3 vs negative HR 2.46 [95% CI 1.56-3.88], PIRADS 4 vs negative HR 3.39 [95% CI 2.28-5.05], and PIRADS 5 vs negative HR 4.95 [95% CI 3.25-7.56]), prostate-specific antigen (PSA) density (per 0.1 ng/ml cm3, HR 1.20 [95% CI 1.12-1.30]), and percentage positive cores on the last systematic biopsy (per 10%, HR 1.16 [95% CI 1.10-1.23]) were significant predictors of GG reclassification. Of the patients with negative MRI and a PSA density of <0.15 ng/ml cm3 (n = 315), 3% were reclassified to GG >= 2 and 0.6% to GG >= 3. At the confirmatory biopsy, reclassification to GG >= 2 and >= 3 was observed in 23% and 7% of the patients diagnosed without upfront MRI and in 19% and 6% of the patients diagnosed with upfront MRI, respectively. The multivariable analysis showed no significant difference in upgrading at the confirmatory biopsy between patients diagnosed with or without upfront MRI.Conclusions: Age, MRI outcome, PSA density, and percentage positive cores are signifi-cant predictors of reclassification at an MRI-informed biopsy. Patients with negative MRI and a PSA density of <0.15 ng/ml cm3 can safely omit a protocol-based prostate biopsy, whereas in other patients, a multivariable approach is advised. Being diagnosed with upfront MRI appears not to significantly affect reclassification risk; hence, a confir-matory MRI-Bx cannot totally be omitted yet.Patient summary: A protocol-based prostate biopsy while on active surveillance can be omitted in patients with negative magnetic resonance imaging (MRI) and prostate -specific antigen density <0.15 ng/ml cm3. A confirmatory biopsy cannot simply be omit-ted in all patients diagnosed with upfront MRI.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

Background: Prostate-specific antigen (PSA)-based detection of prostate cancer (PCa) often leads to negative biopsy results or detection of clinically insignificant PCa, more frequently in the PSA range of 2-10 ng/ml, in men with increased prostate volume and normal digital rectal examination (DRE). Objective: This study evaluated the accuracy of Proclarix, a novel blood-based diagnostic test, to help in biopsy decision-making in this challenging patient population. Design, setting, and participants: Ten clinical sites prospectively enrolled 457 men presenting for prostate biopsy with PSA between 2 and 10 ng/ml, normal DRE, and prostate volume >= 35 cm(3). Transrectal ultrasound-guided and multiparametric magnetic resonance imaging (mpMRI)-guided biopsy techniques were allowed. Outcome measurements and statistical analysis: Serum samples were tested blindly at the end of the study. Diagnostic performance of Proclarix risk score was established in correlation to systematic biopsy outcome and its performance compared with %free PSA (%fPSA) and the European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculator (RC) as well as Proclarix density compared with PSA density in men undergoing mpMRI. Results and limitations: The sensitivity of Proclarix risk score for clinically significant PCa (csPCa) defined as grade group (GG) >= 2 was 91% (n = 362), with higher specificity than both %fPSA (22% vs 14%; difference = 8% [95% confidence interval {CI}, 2.6-14%], p = 0.005) and RC (22% vs 15%; difference = 7% [95% CI, 0.7-12%], p = 0.028). In the subset of men undergoing mpMRI-fusion biopsy (n = 121), the specificity of Proclarix risk score was significantly higher than PSA density (26% vs 8%; difference = 18% [95% CI, 7-28%], p < 0.001), and at equal sensitivity of 97%, Proclarix density had an even higher specificity of 33% [95% CI, 23-43%]. Conclusions: In a routine use setting, Proclarix accurately discriminated csPCa from no or insignificant PCa in the most challenging patients. Proclarix represents a valuable rule-out test in the diagnostic algorithm for PCa, alone or in combination with mpMRI. Patient summary: Proclarix is a novel blood-based test with the potential to accurately rule out clinically significant prostate cancer, and therefore to reduce the number of unneeded biopsies. (c) 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Abstract Background In patients with bone metastatic castration-resistant prostate cancer (bmCRPC) on systemic treatment, it is difficult to differentiate between continuous rise of prostate specific antigen (PSA) representing progression, and PSA-surge, which is followed by clinical response or stable disease. The purpose of this study was to evaluate the prognostic value of dynamic changes of alkaline phosphatase (ALP) and lactic acid dehydrogenase (LDH) levels as a predictor of clinical efficacy or therapeutic resistance of patients who do not show a sufficient initial PSA decline of ≥50% from baseline during early therapy with Enzalutamide. Methods Forty-eight men with bmCRPC on Enzalutamide 07/2010-09/2019 with initially rising PSA were analyzed. We monitored PSA, LDH and ALP at week 0, 2, 4, and every 4 weeks thereafter and analyzed the correlation between ALP rising at 12 weeks with or without LDH-normalization and the association with survival. For this we used Kaplan Meier analysis and uni- and multivariate cox-regression models. Results In Kaplan-Meier analysis, ALP rising at 12 weeks with or without LDH-normalization was associated with significantly worse median progression-free survival (PFS) of 3 months vs. 5 months (Log rank P = 0.02) and 3 months vs. 5 months (P = 0.01), respectively and overall survival (OS) with 8 months vs. 15 months (P = 0.02) and 8 months vs. 17 months (P < 0.01). In univariate analysis of PFS, ALP rising at 12 weeks alone, ALP rising at 12 weeks without LDH-normalization and application of Enzalutamide after chemotherapy showed a statistically significant association towards shorter PFS (hazard ratio (HR): 0.51, P = 0.04; HR: 0.48, P = 0.03; HR: 0.48, P = 0.03). Worse OS was significantly associated with ALP rising at 12 weeks alone, ALP rising at 12 weeks without LDH-normalization, and application of Enzalutamide after chemotherapy (HR: 0.47, P = 0.02; HR: 0.36, P < 0.01; HR: 0.31, P < 0.01). In multivariate analysis only the application of Enzalutamide after chemotherapy remained an independent prognostic factor for worse OS (HR: 0.36, P = 0.01). Conclusions Dynamic changes of ALP (non-rise) and LDH (normalization) under therapy with Enzalutamide may be associated with clinical benefit, better PFS, and OS in patients with bmCRPC who do not show a PSA decline.

Background: Prognostication is essential to determine the risk profile of patients with urologic cancers. Methods: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan–Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability. Results: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795–0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable. Conclusions: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.

You have accessJournal of UrologyCME1 May 2022MP43-14 EXTERNAL VALIDATION OF A MODEL PREDICTING INCREASE IN GLEASON GRADE FOR MEN ON ACTIVE SURVEILLANCE IN THE GAP3 CONSORTIUM DATABASE Daan Nieboer, Ivo de Vos, Lui Shiong Lee, Phillip Stricker, Mark Frydenburg, Anders Bjartell, Jose Rubio-Briones, Axel Semjonow, Antti Rannikko, Mieke van Hemelrijck, Christian Pavlovich, Peter Carroll, and Monique Roobol Daan NieboerDaan Nieboer More articles by this author , Ivo de VosIvo de Vos More articles by this author , Lui Shiong LeeLui Shiong Lee More articles by this author , Phillip StrickerPhillip Stricker More articles by this author , Mark FrydenburgMark Frydenburg More articles by this author , Anders BjartellAnders Bjartell More articles by this author , Jose Rubio-BrionesJose Rubio-Briones More articles by this author , Axel SemjonowAxel Semjonow More articles by this author , Antti RannikkoAntti Rannikko More articles by this author , Mieke van HemelrijckMieke van Hemelrijck More articles by this author , Christian PavlovichChristian Pavlovich More articles by this author , Peter CarrollPeter Carroll More articles by this author , and Monique RoobolMonique Roobol More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002609.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We have previously developed a dynamic prediction model predicting increase in Gleason grade on biopsy for men with prostate cancer (PCa) treated with active surveillance (AS). The model was based on a joint model for longitudinal and survival data predicting the risk of increase in gleason grade at biopsy using PSA, change in PSA, prostate volume and whether or not a positive lesion was found on MRI. It allows for dynamic predictions, updating the risk of increase in Gleason grade as new PSA, MRI, and biopsy data becomes available. We now aimed to validate the previously developed prediction model. METHODS: External validation of our previously developed model was performed using the six largest cohorts in the GAP3 database v3.3 containing information on MRI (John Hopkins, UCSF, UCL, MUSIC, Valencia, Sydney). The time-dependent area under the ROC (AUC) was used to quantify the ability of the prediction model to predict Gleason grade upgrading using all available information at a certain timepoint. RESULTS: Data from 2,086 patients were used for external validation. The median age at diagnosis was 64 (59-68 25th-75th percentile) and median PSA at diagnosis was 5.4 (4.1-7.3). The overall median follow-up time was 3.7 years (range: 1.4-5.1 years). Most cohorts showed a time-dependent AUC ranging between 0.65-0.80 (Figure 1), similar to the time-dependent AUC of our at model development (range 0.70-0.75). Only the time-dependent AUC for UCL and Johns Hopkins were lower (range 0.60-0.65). CONCLUSIONS: The discriminative ability of our developed model was confirmed at external validation. Further research should investigate possible sources of heterogeneity in the observed discriminative ability in the different cohorts. Source of Funding: This work was supported by the Movember Foundation. The funder did not play any role in the study design, collection, analysis or interpretation of data, or in the drafting of this paper. This abstract was submitted on behalf of the Movember Foundation’s Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e746 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Daan Nieboer More articles by this author Ivo de Vos More articles by this author Lui Shiong Lee More articles by this author Phillip Stricker More articles by this author Mark Frydenburg More articles by this author Anders Bjartell More articles by this author Jose Rubio-Briones More articles by this author Axel Semjonow More articles by this author Antti Rannikko More articles by this author Mieke van Hemelrijck More articles by this author Christian Pavlovich More articles by this author Peter Carroll More articles by this author Monique Roobol More articles by this author Expand All Advertisement PDF downloadLoading ...

You have accessJournal of UrologyCME1 May 2022MP43-05 UPFRONT MRI IS THE NEW STANDARD, HAVE CONFIRMATORY BIOPSIES BECOME OBSOLETE? Henk Luiting, Ivo Izaak de Vos, Sebastiaan Remmers, Egbert Boevé, Chris Bangma, Riccardo Valdagni, Peter Chiu, Axel Semjonow, Viktor Berge, Karl Tully, Antti Rannikko, Frédéric Staerman, and Monique Roobol Henk LuitingHenk Luiting More articles by this author , Ivo Izaak de VosIvo Izaak de Vos More articles by this author , Sebastiaan RemmersSebastiaan Remmers More articles by this author , Egbert BoevéEgbert Boevé More articles by this author , Chris BangmaChris Bangma More articles by this author , Riccardo ValdagniRiccardo Valdagni More articles by this author , Peter ChiuPeter Chiu More articles by this author , Axel SemjonowAxel Semjonow More articles by this author , Viktor BergeViktor Berge More articles by this author , Karl TullyKarl Tully More articles by this author , Antti RannikkoAntti Rannikko More articles by this author , Frédéric StaermanFrédéric Staerman More articles by this author , and Monique RoobolMonique Roobol More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002609.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The AUA guidelines recommend confirmatory biopsies in all patients within two years after diagnosis whereas the EAU guidelines recommend refraining from confirmatory biopsies during active surveillance (AS) in patients diagnosed with upfront MRI. Data on these recommendations is conflicting and limited to highly experienced centers. The objective of this study is to add to the current evidence and to determine the risk difference for grade group (GG) reclassification on confirmatory biopsies between patients included with and without upfront MRI. METHODS: The PRIAS study is a multicenter prospective study providing clinical data of patients on AS. The inclusion criteria and recommended follow up schedule are available on www.prias-project.org. In this analysis, we selected patients diagnosed with or without upfront MRI, who underwent (a second) MRI before confirmatory biopsies. All subsequently underwent targeted biopsies (±systematic biopsies (SBx)) in lesions PIRADS ≥3, or SBx in case of no lesion. Multivariable logistic regression analysis was performed to determine the risk difference for GG reclassification at confirmatory biopsies between patients included with and without upfront MRI. RESULTS: In total, 732 patients underwent MRI informed confirmatory biopsies at a median PSA of 6.4 (interquartile range 4.6-8.4) ng/ml. 524 patients were diagnosed without upfront MRI and 208 with upfront MRI. Biopsy outcome and the outcome of multivariable logistic regression analysis are shown in Figure 1. At confirmatory biopsy, 108 (21%) patients without upfront MRI reclassified whereas 39 (19%) patients with upfront MRI reclassified. Reclassification to GG ≥3 was seen in 6% and 7% of the patients without and with upfront MRI respectively. No significant difference in risk for reclassification at confirmatory biopsies was seen between the two groups (OR 1.11 (95%CI 0.72-1.73), p=0.6), whereas MRI outcome, DRE outcome and PSA density were significant predictors for reclassification. CONCLUSIONS: Our results show that upfront MRI does not reduce reclassification rates at confirmatory biopsy and as such does not support the EAU guidelines recommendation to simply omit confirmatory biopsies if upfront MRI is used. In preventing unnecessary biopsies during AS, our results highlight the importance of MRI outcome. Source of Funding: - © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e741 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Henk Luiting More articles by this author Ivo Izaak de Vos More articles by this author Sebastiaan Remmers More articles by this author Egbert Boevé More articles by this author Chris Bangma More articles by this author Riccardo Valdagni More articles by this author Peter Chiu More articles by this author Axel Semjonow More articles by this author Viktor Berge More articles by this author Karl Tully More articles by this author Antti Rannikko More articles by this author Frédéric Staerman More articles by this author Monique Roobol More articles by this author Expand All Advertisement PDF downloadLoading ...

Background: In bone metastatic castration-resistant prostate cancer (bmCRPC) treated with Enzalutamide commonly used prostate-specific antigen (PSA) can be misleading since initial PSA-flares may occur. In other therapies, bouncing of alkaline phosphatase (ALP-bouncing) was shown to be a promising surrogate for survival outcome. Low lactate dehydrogenase (LDH) is usually associated with better outcome. We evaluated the prognostic ability of ALP-bouncing, LDH, PSA, and the combination of these markers after initiation of Enzalutamide. Methods: Eighty-nine patients with bmCRPC and dynamic changes of PSA, LDH and ALP were analyzed. ALP-bouncing, an increase after therapy start followed by a decline below baseline during the first 8 weeks, LDH-normalization and PSA-decline were analyzed regarding their association with survival using Kaplan-Meier analyses and uni- and multivariate (UV and MV) Cox-regression models. Results: In Kaplan-Meier analysis a PSA-decline >50%, LDH-normalization and ALP-bouncing were associated with longer median progression-free survival (PFS) with 7 [95% confidence interval (CI): 4.2-9.8] vs. 3 (2.3-3.7) months for PSA-decline (log-rank P<0.01), 6 (4.1-8) vs. 2 (1.2-2.8) for LDH-normalization (P<0.01) and 8 (0-16.3) vs. 3 (1.9-4.1) for ALP-bouncing (P=0.01). Analysis of overall survival (OS) showed similar, not for all parameters significant, results with 17 (11.7-22.3) vs. 12 (7.0-17.1) months for PSA (P=0.35), 17 (13.2-20.8) vs. 7 (5.8-8.2) for LDH-normalization (P<0.01) and 19 (7.9-30.1) vs. 12 (7.7-16.3) for ALP-bouncing (P=0.32). In UV analysis, ALP-bouncing [hazard ratio (HR): 0.5 (0.3-1.0); P=0.02], PSA-decline >50% [HR: 0.5 (0.3-0.7); P<0.01] and LDH-normalization [HR: 0.4 (0.2-0.6); P<0.01] were significantly associated with longer PFS. For OS, LDH-normalization significantly prognosticated longer survival [HR: 0.4 (0.2-0.6); P<0.01]. In MV analysis, LDH-normalization was associated with a trend towards better OS [HR: 0.5 (0.2-1.1); P=0.09]. Comparing ALP-bouncing, LDH-normalization and PSA-decline with a PSA-decline alone, Kaplan-Meier analysis showed significantly longer PFS [11 (0.2- 21.8) vs. 4 (0-8.6); P=0.01] and OS [20 (17.7-22.3) vs. 8 (0.3-15.7); P=0.02] in favor of the group presenting with the beneficial dynamics of all three markers. In UV analysis, the presence of favorable changes in the three markers was significantly associated with longer PFS [HR: 0.2 (0.1-0.7); P<0.01] and OS [HR: 0.3 (0.1-0.8); P=0.02]. Conclusions: ALP-bouncing and LDH-normalization may add to identification of bmCRPC-patients with favorable prognosis under Enzalutamide.