Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
This study is looking at new ways to use Magnetic Resonance Imaging (MRI) and artificial intelligence to get clearer images and more information from prostate MRI scans. The goal is to improve how accurately prostate cancer is diagnosed in the future, potentially making it less invasive than current methods. Researchers are developing new techniques for analyzing MRI images, specifically dynamic contrast-enhanced (DCE)-MRI and diffusion weighted imaging (DWI), and creating deep learning models to better identify and classify prostate cancer. This is an observational study, meaning you won't receive a new treatment. You might be able to join if you are a man aged 18 or older with suspected or confirmed prostate cancer, and have had or will have a 3 Tesla (3T) prostate MRI at UCLA. The study is currently unclear on its recruitment status.
- Study design
- This is an observational study with a planned enrollment of 275 men. It involves both reviewing past medical records and prospective (forward-looking) participation.
- What's involved
- If you participate prospectively, you will undergo an additional 3 Tesla (T) MRI scan for about 30 minutes, either before, during, or after your standard 3T MRI, for a total of 1.5 hours.
- Compensation
- Not stated in the trial record.
- Follow-up
- The primary goals of this study are measured over a period of up to 5 years, focusing on the development of new imaging and analysis techniques.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
At a glance
Conditions
Where it's being run
1 sites across 1 statesStudy leadership
- Kyung H Sung, PhD · PRINCIPAL_INVESTIGATOR · UCLA / Jonsson Comprehensive Cancer Center
Who to contact
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Do you actually qualify for this trial?
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Inclusion
Exclusion
What this trial measures
- Development of quantitative dynamic contrast (DCE)-enhanced-magnetic resonance imaging (MRI) analysis techniquesUp to 5 years
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 distortionUp to 5 years
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 modelsUp to 5 years
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.