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Objective: To characterize national trends in and associated outcomes of more often than annual prostate-specific antigen (PSA) screening, which we term "prosteria.", Methods: Men in the Optum Clinformatics Data Mart with ≥2 years from first PSA test to censoring at the end of insurance or available data (January 2003 to June 2019) or following exclusionary diagnoses or procedures, such as PCa treatment, were included. PSAs within 90 days were treated as one PSA. Prosteria was defined as having ≥3 PSA testing intervals of ≤270 days., Results: A total of 9,734,077 PSAs on 2,958,923 men were included. The average inter-PSA testing interval was 1.5 years, and 4.5% of men had prosteria, which increased by 0.53% per year. Educated, wealthy, non-White patients were more likely to have prosteria. Men within the recommended screening age (ie 55-69) had lower rates of prosteria. Prosteria patients had higher average PSA values (2.5 vs 1.4 ng/mL), but lower values at PCa diagnosis. Prosteria was associated with biopsy and PCa diagnosis; however, there were comparable rates of treatment within 2 years of diagnosis., Conclusion: In this large cohort study, prosteria was common, increased over time, and was associated with demographic characteristics. Importantly, there were no clinically meaningful differences in PSA values at diagnosis or rates of early treatment, suggesting prosteria leads to both overdiagnosis and overtreatment. These results support current AUA and USPTF guidelines and can be used to counsel men seeking more frequent PSA screening., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Genitourinary rhabdomyosarcoma (GU-RMS) is a rare, pediatric malignancy originating from embryonic mesenchyme. Current approaches to prognostication rely upon conventional statistical methods such as Cox proportional hazards (CPH) models and have suboptimal predictive ability. Given the success of deep learning approaches in other specialties, we sought to develop and compare deep learning models with CPH models for the prediction of 5-year survival in pediatric GU-RMS patients., Methods: Patients less than 20 years of age with GU-RMS were identified within the Surveillance, Epidemiology, and End Results (SEER) database (1998-2011). Deep neural networks (DNN) were trained and tested on an 80/20 split of the dataset in a 5-fold cross-validated fashion. Multivariable CPH models were developed in parallel. The primary outcomes were 5-year overall survival (OS) and disease-specific survival (DSS). Variables used for prediction were age, sex, race, primary site, histology, degree of tumor extension, tumor size, receipt of surgery, and receipt of radiation. Receiver operating characteristic curve analysis was conducted, and DNN models were tested for calibration., Results: 277 patients were included. The area under the curve (AUC) for the DNN models was 0.93 for OS and 0.91 for DSS. AUC for the CPH models was 0.82 for OS and 0.84 for DSS. The DNN models were well-calibrated: OS model (slope = 1.02, intercept = -0.06) and DSS model (slope = 0.79, intercept = 0.21)., Conclusions: A deep learning-based model demonstrated excellent performance, superior to that of CPH models, in the prediction of pediatric GU-RMS survival. Deep learning approaches may enable improved prognostication for patients with rare cancers., (Copyright © 2020 Elsevier Ltd. All rights reserved.)