AI Algorithm-Informed Biopsy for Prostate Cancer Detection With Indeterminate and Low-Risk Prostate MRI Lesions

Part of paid clinical trials in Little Rock, Arkansas.

Sponsor
University of Arkansas
Study ID
NCT07231627
Status
Not Yet Recruiting

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Conditions

Eligibility Criteria

Sex
MALE
Age
40 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • Bi-parametric MRI-based cascaded deep-learning AI algorithm — DEVICE
    Artificial intelligence system used in medical imaging, primarily for the automated detection and classification of lesions (such as prostate cancer) using only specific types of magnetic resonance imaging (MRI) data.

Study Details

Use of AI algorithm for PCa detection is feasible, and AI-informed biopsies (AI-targeted and perilesional biopsy) improves csPCa detection in patients with indeterminate MRI lesions and in patients with low-risk MRI lesions and high-risk clinical features.

Key Dates

Start date
Jun 30, 2026
Status verified
Jun 2026
Primary completion
Jan 31, 2028
Completion
Jan 31, 2029

Study Design

Enrollment
50 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC

Arms

  • Experimental: Bi-parametric MRI-based cascaded deep-learning AI algorithm
    The AI model inputs biparametric DICOM sequences (T2-weighted images, high-b-value diffusion-weighted images, and apparent diffusion coefficient maps), and the outputs include binary prostate organ and intraprostatic lesion segmentations. This study will assess a recently developed and both internally and externally validated AI algorithm for PCa detection capability in patients with equivocal lesions (PI-RADS 3 lesions) and negative lesions (PI-RADS 1-2 lesions) with higher clinical risk features such as high PSA density.
  • No Intervention: Perilesional prostate biopsy
    Standard of care prostate biopsy which is a systematic template biopsy (with 12 biopsy cores) + MRI-targeted biopsy (for PI-RADS category 3 lesions only, with 3 biopsy cores), consistent with current NCCN guideline recommendations

Primary Outcome Measure

Acceptance rate of randomization and biopsy recommendations based on study protocol and AI algorithm results by the patients [ Time Frame: 4 months ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
University of Arkansas for Medical SciencesLittle RockArkansas72205
Ahmet Aydin, MD
501-686-8530
Ahmet M Aydin, MD (PRINCIPAL_INVESTIGATOR)

Find similar trials in Little Rock, AR

By condition
Prostate cancer
By specialty
OncologyProstate oncologyMen's healthUrology
By research site
University of Arkansas for Medical Sciences· Little Rock, AR

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