Related Experiment Video
Updated: Jul 20, 2025

06:28
E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
Published on: August 1, 2019
8.4K
A pilot study of an organised population-based testing programme for prostate cancer
Max Alterbeck1,2, Erik Thimansson3,4, Johan Bengtsson3,4
1Department of Urology, Skåne University Hospital, Malmö, Sweden.
BJU International
|July 31, 2023
Summary
A pilot program for digitally automated organised prostate cancer testing (OPT) in Sweden demonstrated operational feasibility. Further research is needed to assess the diagnostic model's efficacy and patient outcomes.
Area of Science:
- Urology
- Oncology
- Health Informatics
Background:
- Organised prostate cancer testing (OPT) programs aim to improve early detection and patient outcomes.
- Digital automation offers potential for efficient and scalable population-based screening initiatives.
Purpose of the Study:
- To evaluate the operational feasibility of a digitally automated, population-based organised prostate cancer testing (OPT) program in Southern Sweden.
- To assess the performance of a risk stratification model incorporating prostate-specific antigen (PSA), PSA density (PSAD), and multiparametric magnetic resonance imaging (MRI).
Main Methods:
- A pilot study involved 999 randomly selected men aged 50, 56, and 62 years.
- Risk stratification used PSA levels, PSAD, and bi-parametric prostate MRI (PI-RADS 4-5).
- An automated digital system managed patient selection, communication, and data processing.
Main Results:
- 418 men (42%) participated, with higher uptake in older age groups.
- 10 men were diagnosed with prostate cancer, with six undergoing active treatment and four active surveillance.
- The study demonstrated the operational feasibility of the automated system.
Conclusions:
- The digitally automated OPT model is operationally feasible.
- Limited study scale prevents conclusions on diagnostic model efficacy or patient outcomes.
- Further investigation is warranted to validate the model's effectiveness in a larger population.
More Related Videos
Related Concept Videos
Longitudinal Research
12.0K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.0K
Cancer Prevention
6.2K
Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Some...
6.2K
Testing a Claim about Population Proportion
3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K

