BackgroundIndigenous Peoples have higher morbidity rates and lower life expectancies than non‐Indigenous Canadians. Identification of disparities between Indigenous and non‐Indigenous men regarding prostate cancer (PCa) screening, diagnoses, management, and outcomes was sought.MethodsAn observational cohort of men diagnosed with PCa between June 2014 and October 2022 was studied. Men were prospectively enrolled in the province‐wide Alberta Prostate Cancer Research Initiative. The primary outcomes were tumor characteristics (stage, grade, and prostate‐specific antigen [PSA]) at diagnosis. Secondary outcomes were PSA testing rates, time from diagnosis to treatment, treatment modality, and metastasis‐free, cancer‐specific, and overall survivals.ResultsExamination of 1,444,974 men for whom aggregate PSA testing data were available was performed. Men in Indigenous communities were less likely to have PSA testing performed than men outside of Indigenous communities (32 vs. 46 PSA tests per 100 men [aged 50–70 years] within 1 year; p < .001). Among 6049 men diagnosed with PCa, Indigenous men had higher risk disease characteristics: a higher proportion of Indigenous men had PSA ≥ 10 ng/mL (48% vs. 30%; p < .01), TNM stage ≥ T2 (65% vs. 47%; p < .01), and Gleason grade group ≥ 2 (79% vs. 64%; p < .01) compared to non‐Indigenous men. With a median follow‐up of 40 months (interquartile range, 25–65 months), Indigenous men were at higher risk of developing PCa metastases (hazard ratio, 2.3; 95% CI, 1.2–4.2; p < .01) than non‐Indigenous men.ConclusionsDespite receiving care in a universal health care system, Indigenous men were less likely to receive PSA testing and more likely to be diagnosed with aggressive tumors and develop PCa metastases than non‐Indigenous men.

PurposeThe Alberta Prostate Cancer Research Initiative (APCaRI) Registry and Biorepository was established in 2014 by the APCaRI to facilitate the collection of clinical and patient-reported data, biospecimen, to measure prostate cancer outcomes and to support the development and clinical translation of innovative technologies to better diagnose and predict outcomes for patients with prostate cancer.ParticipantsMen suspected with prostate cancer and referred to Urology centres in Alberta were enrolled in the APCaRI 01 study, while men with a prior prostate cancer diagnosis participated in the APCaRI 03 study from 1 July 2014 to 30 June 2019. The APCaRI Registry and Biorepository links biospecimens and data from a wide representation of patients drawn from an Alberta population of more than 4 million.Findings to dateFrom 1 July 2014 to 30 June 2019, total APCaRI 01 and 03 study recruitment was 3754 men; 142 (4%) of these men withdrew in full, 65 men (2%) withdrew biospecimens and 123 men (3%) died of any cause. Over this same time, 8677 patient-reported outcome measure (PROM) surveys and 7368 biospecimens were collected and are available from the registry and biorepository, respectively. The data entry error rate was 0.8% and 0.95% for critical and non-critical values, respectively, and 1.8% for patient-reported surveys.Future plansThe APCaRI Registry and Biorepository will collect longitudinal data and PROM surveys until 2024, patient outcomes up to 25 years after recruitment and biospecimen storage for up to 25 years. The APCaRI cohorts will continue to provide data and samples to researchers conducting retrospective studies. The richness of the data and biospecimens will complement many different research questions, ultimately to improve the quality of care for men with prostate cancer.

Automated brain tumor segmenters typically run a "skull-stripping" pre-process to extract the brain from the 3D image, before segmenting the area of interest within the extracted volume. We demonstrate that an effective existing segmenter can be improved by replacing its skull-stripper component with one that instead uses a registration-based approach. In particular, we compare our automated brain segmentation system with the original system as well as three other approaches that differ only by using a different skull-stripper-BET, HWA, and ROBEX: (1) Over scans of 120 patients with brain tumors, our system's segmentation accuracy (Dice score with respect to expert segmentation) is 8.6% (resp. 2.7%) better than the original system on gross tumor volumes (resp. edema); (2) Over 103 scans of controls, the new system found 92.9% (resp. 57.8%) fewer false positives on T1C (resp. FLAIR) volumes. (The other three methods were significantly worse on both tasks). Finally, the new registration-based approach is over 15% faster than the original, requiring on average only 178 CPU seconds per volume.

PURPOSE An online clinical information system, called Predictive Research Online System Prostate Cancer Tasks (PROSPeCT), was developed to enable users to query the Alberta Prostate Cancer Registry database hosted by the Alberta Prostate Cancer Research Initiative. To deliver high-quality patient treatment, prostate cancer clinicians and researchers require a user-friendly system that offers an easy and efficient way to obtain relevant and accurate information about patients from a robust and expanding database. METHODS PROSPeCT was designed and implemented to make it easy for users to query the prostate cancer patient database by creating, saving, and reusing simple and complex definitions. We describe its intuitive nature by exemplifying the creation and use of a complex definition to identify a “high-risk” patient cohort. RESULTS PROSPeCT was made to minimize user error and to maximize efficiency without requiring the user to have programming skills. Thus, it provides tools that allow both novice and expert users to easily identify patient cohorts, manage individual patient care, perform Kaplan Meier estimates, plot aggregate PSA views, compute PSA-doubling time, and visualize results. CONCLUSION This report provides an overview of PROSPeCT, a system that helps clinicians to identify appropriate patient treatments and researchers to develop prostate cancer hypotheses, with the overarching goal of improving the quality of life of patients with prostate cancer. We have made available the code for the PROSPeCT implementation at https://github.com/max-uhlich/e-PROSPeCT .