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.
Male patients 18 years of age and older
Clinical suspicion of prostate cancer or biopsy-confirmed prostate cancer
Undergone or undergoing multi-parametric 3 T prostate MRI at the University of California at Los Angeles (UCLA)
Ability to provide consent
Contraindications to MRI (e.g., cardiac devices, prosthetic valves, severe claustrophobia)
Contraindications to gadolinium contrast-based agents other than the possibility of an allergic reaction to the gadolinium contrast-based agent
Prior radiotherapy
Patients at the University of California, Los Angeles (UCLA) who may have already undergone 3 T prostate multi-parametric MRI or were referred for 3 T multi-parametric prostate MRI prior to biopsy or radical prostatectomy.
Cohort
Other
3 Tesla Magnetic Resonance Imaging
Procedure/SurgeryUndergo 3T MRI
Electronic Health Record Review
Other interventionMedical charts are reviewed
Development of quantitative dynamic contrast (DCE)-enhanced-magnetic resonance imaging (MRI) analysis techniques
Both transfer constant (Ktrans) and rate constant (Kep) from normal prostate tissue will be evaluated for the inter-scanner variability. Pairwise dissimilarities between distributions will be estimated by computing the Kolmogorov-Smirnov statistic, defined as the maximum difference between the empirical distribution functions over the range of the parameter, using 200 cases for each of three MRI scanners. The mean of these pairwise dissimilarities between scanners will be computed to quantify the overall discrepancy of each DCE-MRI model. Construction of a 95% confidence interval for the difference in the mean discrepancies using the nonparametric bootstrap will be done to compare this mean discrepancy between DCE-MRI models. 10,000 bootstrap samples will be generated by sampling patients with replacement, stratifying by the scanner. Will conclude that the proposed DCE-MRI model has a reduced inter-scanner variability if the 95% confidence interval is entirely less than zero.
Development of diffusion weighted imaging (DWI) methods that reduce prostate geometric distortion
Differences between rectangular field of view-ENCODE and standard DWI in terms of the prostate Dice's similarity coefficient (primary outcome) and apparent diffusion coefficient consistency will be compared.
Development of multi-class deep learning models
The overall performance of FocalNet and Prostate Imaging Reporting \& Data System version 2 will be compared in terms of area under the curve. Comparison between area under the curves will be performed using DeLong's test. Will also include the comparison between FocalNet and baseline deep learning methods (U-Net and Deeplab without focal loss \[FL\] and mutual finding loss \[MFL\]) to characterize the advantages of using FL and MFL with the same study cohort. For each of these approaches, an optimal cut-point for classification of clinically significant prostate cancer will be identified by maximizing Youden's J (= sensitivity + specificity - 1) and will report sensitivity, specificity and 95% confidence intervals based on the selected cut-point.
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Jonsson Comprehensive Cancer Center
Lead sponsor
National Institutes of Health (NIH)
Collaborator
National Cancer Institute (NCI)
Collaborator
