This study evaluates how new magnetic resonance imaging (MRI) and artificial intelligence techniques improve the image quality and quantitative information for future prostate MRI exams in patients with suspicious of confirmed prostate cancer. The MRI and artificial intelligence techniques developed in this study may improve the accuracy in diagnosing prostate cancer in the future using less invasive techniques than what is currently used.
Study sponsor and potential other locations can be found on ClinicalTrials.gov for NCT04765150.
PRIMARY OBJECTIVES:
I. To develop and evaluate quantitative dynamic contrast-enhanced (DCE)-MRI analysis techniques that minimize patient- and scanner-specific variabilities in the calculation of quantitative parameters.
II. To develop and evaluate diffusion weighted imaging (DWI) methods that reduce prostate geometric distortion due to patient- and scanner-specific susceptibility and eddy current effects.
III. To develop and evaluate multi-class deep learning models that systematically integrate quantitative multi-parametric (mp)-MRI features for accurate detection and classification of clinically significant prostate cancer (csPCa).
OUTLINE: This is an observational study.
Patients undergo additional 3 Tesla (T) MRI imaging over 30 minutes before, during, or after their standard of care 3T MRI for a total of 1.5 hours and have their medical records retrospectively reviewed on study.
Trial PhaseNo phase specified
Trial TypeNot provided by clinicaltrials.gov
Lead OrganizationUCLA / Jonsson Comprehensive Cancer Center
Principal InvestigatorKyung Hyun Sung
