Clinical trial NCT04765150: Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
Official title: Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Prostate Carcinoma
Lead sponsor: Jonsson Comprehensive Cancer Center
Sponsor class: OTHER
Collaborators: name: National Institutes of Health (NIH)
name: National Cancer Institute (NCI)
First posted: 2021-02-21
Enrollment: 275
Sex: MALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Development of quantitative dynamic contrast (DCE)-enhanced-magnetic resonance imaging (MRI) analysis techniques
timeFrame: Up to 5 years
measure: Development of diffusion weighted imaging (DWI) methods that reduce prostate geometric distortion
timeFrame: Up to 5 years
measure: Development of multi-class deep learning models
timeFrame: Up to 5 years
Secondary outcomes:
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: UCLA / Jonsson Comprehensive Cancer Center
state: California
Study officials:
affiliation: UCLA / Jonsson Comprehensive Cancer Center
name: Kyung H Sung, PhD
role: PRINCIPAL_INVESTIGATOR
References: