ImportanceProstate cancer (PCa) is marked by disparities in clinical outcomes by race, ethnicity, and age. Equitable enrollment in clinical trials is fundamental to promoting health equity.ObjectiveTo evaluate disparities in the inclusion of racial and ethnic minority groups and older adults across PCa clinical trials.Data SourcesMEDLINE, Embase, and ClinicalTrials.gov were searched to identify primary trial reports from each database's inception through February 2021. Global incidence in age subgroups and US population-based incidence in racial and ethnic subgroups were acquired from the Global Burden of Disease and Surveillance, Epidemiology, and End Results 21 incidence databases respectively.Study SelectionAll phase 2/3 randomized PCa clinical trials were eligible for age disparity analyses. Trials recruiting exclusively from the US were eligible for primary racial and ethnic disparity analyses.Data Extraction and SynthesisThis study was reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. Data were pooled using a random-effects model.Main Outcomes and MeasuresEnrollment incidence ratios (EIRs), trial proportions (TPs) of participants 65 years or older or members of a racial and ethnic subgroup divided by global incidence in the corresponding age group, or US population–based incidence in the corresponding racial and ethnic subgroup, were calculated. Meta-regression was used to explore associations between trial characteristics and EIRs and trends in EIRs during the past 3 decades.ResultsOf 9552 participants among trials reporting race, 954 (10.8%) were African American/Black, 80 (1.5%) were Asian/Pacific Islander, and 8518 (78.5) were White. Of 65 US trials, 45 (69.2%) reported race and only 9 (13.8%) reported data on all 5 US racial categories. Of 286 global trials, 75 (26.2%) reported the enrollment proportion of older adults. Outcomes by race and age were reported in 2 (3.1%) and 41 (15.0%) trials, respectively. Black (EIR, 0.70; 95% CI, 0.59-0.83) and Hispanic (EIR, 0.70; 95% CI, 0.59-0.83) patients were significantly underrepresented in US trials. There was no disparity in older adult representation (TP, 21 143 [71.1%]; EIR, 1.00; 95% CI, 0.95-1.05). The representation of Black patients was lower in larger trials (meta-regression coefficient, −0.06; 95% CI, −0.10 to −0.02; P = .002).Conclusions and RelevanceThe results of this meta-analysis suggest that Black and Hispanic men are underrepresented in trials compared with their share of PCa incidence. The representation of Black patients has consistently remained low during the past 2 decades.

136 Background: Intensification of initial treatment of in patients with metastatic castration-sensitive prostate cancer (mCSPC) with androgen pathway inhibition (API) in addition to docetaxel (DOC) and androgen deprivation therapy (ADT) has shown promise to improve clinical outcomes. Thus, we synthesize the data from modern clinical trials to estimate overall estimates of progression and survival outcomes. Methods: A systematic search of electronic databases (MEDLINE and EMBASE) was conducted to include phase III randomized controlled trials (RCTs) evaluating triplet therapy (API+DOC+ADT) against doublet therapy (DOC+ADT) in mCSPC. Outcomes of interest included overall survival (OS) and radiographic progression-free survival (rPFS). A DerSimonian-Laird random-effects meta-analysis was performed to pool precomputed hazard ratios (HRs) and confidence intervals (CIs) after logarithmic transformation using inverse-variance weighting approach. Cochran’s Q statistical test was used to assess statistically significant heterogeneity not explained by chance, while I2 statistical test was used to quantify the total observed variability, due to between-study heterogeneity. I2 values >50% indicated substantial heterogeneity. A summary of findings table was constructed to translate relative estimates to absolute risks. Results: A total of 1,531 patients in four RCTs with direct comparative data between triplet and doublet therapies, were included in this meta-analysis. PEACE-1 was the only RCT directly assessing this question (AAP+DOC+ADT). ENZA+DOC+ADT was evaluated as a subgroup in two RCTs (ENZAMET; ARCHES), APA+DOC+ADT was evaluated as a subgroup in one (TITAN). To be able to pool studies, the relative efficacy of control arm in ENZAMET (first generation bicalutamide + ADT) was considered equivalent to ADT based on prior literature. A total of 672 rPFS events were observed (34%; 261 events in triplet therapy, 54%; 411 events in doublet therapy). The difference was statistically significant (HR: 0.49; 95% CI: 0.42-0.58; I2: 0%). Similarly, 469 OS events were observed (28%; 217 events in triplet therapy, 33%; 252 events in doublet therapy). The difference was statistically significant (HR: 0.80; 95% CI: 0.67-0.96; I2: 0%). The summary of findings is outlined in the table. Conclusions: The results of this meta-analysis support an improvement in rPFS and OS in favor of triplet therapy over doublet therapy for mCRPC. However, comparative effectiveness of different triplet regimens may be different and needs further exploration.[Table: see text]

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Accurate clinical staging of bladder cancer aids in optimizing the process of clinical decision-making, thereby tailoring the effective treatment and management of patients. While several radiomics approaches have been developed to facilitate the process of clinical diagnosis and staging of bladder cancer using grayscale computed tomography (CT) scans, the performances of these models have been low, with little validation and no clear consensus on specific imaging signatures. We propose a hybrid framework comprising pre-trained deep neural networks for feature extraction, in combination with statistical machine learning techniques for classification, which is capable of performing the following classification tasks: (1) bladder cancer tissue vs. normal tissue, (2) muscle-invasive bladder cancer (MIBC) vs. non-muscle-invasive bladder cancer (NMIBC), and (3) post-treatment changes (PTC) vs. MIBC.