Clinical trial · Observational
Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
NCT04765150CI-TRIAL-00117140recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Prostate Carcinoma | Prostate Carcinoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| 3 Tesla Magnetic Resonance Imaging | Procedure | — | UNRESOLVED |
| Electronic Health Record Review | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Observational (electronic health record review, 3 T MRI)
- description
- 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.
- interventionNames
- Procedure: 3 Tesla Magnetic Resonance Imaging
- Other: Electronic Health Record Review
Primary outcomes (3)
- measure
- Development of quantitative dynamic contrast (DCE)-enhanced-magnetic resonance imaging (MRI) analysis techniques
- timeFrame
- Up to 5 years
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 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 Exclusion Criteria: * 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
References
Publications (0)
Data not yet available
No reference posted for this study.