Ignite Creation Date:
2025-12-24 @ 5:08 PM
Ignite Modification Date:
2025-12-28 @ 12:46 PM
Study NCT ID:
NCT04765150
Status:
RECRUITING
Last Update Posted:
2025-05-13
First Post:
2021-02-18
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
Sponsor:
Jonsson Comprehensive Cancer Center
Organization:
Study Overview
Official Title:
Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
Status:
RECRUITING
Status Verified Date:
2025-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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. 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:
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:
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.
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:
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 Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
Secondary ID Infos
| Secondary ID | Type | Domain | Link | View |
|---|---|---|---|---|
| NCI-2021-00373 | REGISTRY | CTRP (Clinical Trial Reporting Program) | View | |
| 19-002202 | OTHER | UCLA / Jonsson Comprehensive Cancer Center | View | |
| R01CA248506 | NIH | None | https://reporter.nih.gov/quic… | View |
| 441480-KS-29447 | OTHER_GRANT | NCI | View |