Prostate CarcinomaNCT04765150
Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
An observational study of Prostate Carcinoma, run by Jonsson Comprehensive Cancer Center at 1 site in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older, men only
- Conditions the study is for
- Prostate Carcinoma
- What is being tested?
- 3 Tesla Magnetic Resonance Imaging and Electronic Health Record Review
- Type of treatment
- Surgery or procedure
- How many people
- 275 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Apr 2021to 1 Jun 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Men only
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
UCLA / Jonsson Comprehensive Cancer Center
Los Angeles, California, 90095
Nashla Barroso, 310-794-7952, nbarroso@mednet.ucla.edu
Kyung H. Sung, PhD
About this study
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.
Read the detailed description
PRIMARY OBJECTIVES:
- 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:
RETROSPECTIVE: Patients' medical records are reviewed.
PROSPECTIVE: 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.
- Conditions
- Prostate Carcinoma
- Being tested
- 3 Tesla Magnetic Resonance Imaging, Electronic Health Record Review
- Medical subject headings
- Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
3 Tesla Magnetic Resonance ImagingProcedure or surgery
Also called 3 Tesla MRI, 3T MRI
Undergo 3T MRI
Electronic Health Record ReviewOther
Medical charts are reviewed
| Study group | Role | What they receive |
|---|---|---|
| Observational (electronic health record review, 3 T MRI) | RETROSPECTIVE: Patients' medical records are reviewed.
PROSPECTIVE: Patients undergo additional 3T MRI imaging over 30 minutes before, during, or after their standard of care 3T MRI for a total of 1.5 hours. Receives: Procedure: 3 Tesla Magnetic Resonance Imaging; Other: Electronic Health Record Review |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Up to 5 years
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.
Measured: Up to 5 years
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.
Measured: Up to 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2021
- Study start1 Apr 2021
- Primary completion1 Jun 2027 (estimated)
- Study completion1 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostate CarcinomaStudies of the same condition, open and under way.
- More trials run by Jonsson Comprehensive Cancer CenterThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 275 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jonsson Comprehensive Cancer Center
- Sponsor type
- University, hospital or other
- Collaborators
- National Institutes of Health (NIH), National Cancer Institute (NCI)
- Responsible party
- Sponsor
- Investigators
- Kyung H Sung, PhD, Principal investigator, UCLA / Jonsson Comprehensive Cancer Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04765150
- Sponsor's protocol ID
- 19-002202
- Other IDs
- NCI-2021-00373, 19-002202, R01CA248506, 441480-KS-29447
- Status last verified
- May 2026
- First submitted
- 18 Feb 2021
- Last updated
- 7 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
- ClinicalTrials.gov ID
- NCT04765150
- Acronym / sponsor ID
- 19-002202
- Conditions
- Prostate Carcinoma
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 275 participants (estimated)
- Lead sponsor
- Jonsson Comprehensive Cancer CenterUniversity, hospital or other
- Collaborators
- National Institutes of Health (NIH), National Cancer Institute (NCI)
- Study start
- 1 Apr 2021 (actual)
- Primary completion
- 1 Jun 2027 (estimated)
- Study completion
- 1 Jun 2028 (estimated)
- First posted
- 21 Feb 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
PRIMARY OBJECTIVES:
- 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:
RETROSPECTIVE: Patients' medical records are reviewed.
PROSPECTIVE: 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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Observational (electronic health record review, 3 T MRI) | – | Procedure: 3 Tesla Magnetic Resonance Imaging, Other: Electronic Health Record ReviewRETROSPECTIVE: Patients' medical records are reviewed. PROSPECTIVE: Patients undergo additional 3T MRI imaging over 30 minutes before, during, or after their standard of care 3T MRI for a total of 1.5 hours. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 3 Tesla Magnetic Resonance ImagingAlso: 3 Tesla MRI, 3T MRIUndergo 3T MRI | Procedure or surgery | Observational (electronic health record review, 3 T MRI) |
| Electronic Health Record ReviewMedical charts are reviewed | Other | Observational (electronic health record review, 3 T MRI) |
Primary outcome
Development of quantitative dynamic contrast (DCE)-enhanced-magnetic resonance imaging (MRI) analysis techniques
Time frame: Up 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 distortion
Time frame: Up 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 models
Time frame: Up 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jul 2026.
Related trials for Prostate Carcinoma
Not yet recruitingObservational study
Assessment of Intelligent Algorithms for Detection, Diagnosis and Risk Stratification of Tumors of the Kidney and Prostate
Kidney Cancers, Prostate Cancer
Not yet recruiting
WATER IV PCa (AS Sub-study)
Localized Prostate Cancer
Recruiting
The Application of Somatosensory Interactive Pelvic Floor Muscle Training for Stress Urinary Incontinence in Men Underwent Radical Prostatectomy
Prostate Cancer (Post Prostatectomy)
RecruitingPhase 1
A Study of [225Ac]Ac-AKY-2519 in Patients With Metastatic Castration-Resistant Prostate Cancer
Castration Resistant Metastatic Prostate Cancer, Prostate Cancer
Enrolling by invitationObservational study
Natural History Study of Early Life Exposures in Agriculture (ELEA)
Prostate Cancer, Leukemia
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Kyung H Sung, PhD principal investigator
UCLA / Jonsson Comprehensive Cancer Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Prostate Carcinoma, run by Jonsson Comprehensive Cancer Center at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 7 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nashla Barroso ((310) 794-7952, nbarroso@mednet.ucla.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of quantitative dynamic contrast (DCE)-enhanced-magnetic resonance imaging (MRI) analysis techniques (time frame: Up to 5 years). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Jul 2026.