The purpose of this study is to develop a new way to diagnose prostate cancer through the use of artificial intelligence. The goal is for this new method to reduce delays in diagnoses and to avoid invasive procedures such as biopsies.
The overall goal of this project is to create a diagnosis and patient management strategy (called HIT-PIRADS) for prostate cancer (PCa) that will significantly increase positive predictive value (PPV) for clinically significant PCa detection while minimizing unnecessary prostate biopsies and related morbidities. Due to its interpretable nature and bias correction paradigm, the AI system will generate predictions that physicians can trust. One of the immediate outcomes of the system will be a reproducible risk scoring system that can be used in community hospitals and locales without MRI-subspeciality genitourinary trained radiologists to improve the accuracy of prostate imaging nationwide. In the long term, the investigators expect HIT-PIRADS to be widely adopted in clinics and trigger other treatment and prevention strategies to be developed based on HIT-PIRADS.
Study Type
OBSERVATIONAL
Enrollment
800
National Institutes of Health
Bethesda, Maryland, United States
RECRUITINGNumber of Participants with detection of clinically significant prostate cancer (csPCa)
csPCa is defined as PCa with a Gleason Grade Group of 2 through 5 found on prostate biopsy. Detection of csPCa will be compared for HITPIRADS vs. PIRADS v2.1 with a focus on suspicious lesions.
Time frame: 01/01/2016-12/31/2026
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.