Recruiting

AI-informed Biopsies

Sponsor:

University of Arkansas

Code:

NCT07231627

Conditions

Prostate Cancer

Eligibility Criteria

Sex: Male

Age: 40+

Healthy Volunteers: Not accepted

Interventions

Bi-parametric MRI-based cascaded deep-learning AI algorithm

Study Details

Brief summary:

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.

Conditions

Prostate Cancer

Study ID

NCT07231627

Start date

Jun 19, 2026

Status verified date

Aug, 2026

Completion date

Jan, 2029

Anticipated

Primary completion date

Jan, 2028

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: Male

Age: 40+

Healthy Volunteers: Not accepted

Inclusion Criteria:

1. 40 years of age or older.
2. A recent pMRI performed within last 12 weeks
3. Eastern Cooperative Oncology Group (ECOG) performance status 0 - 1.
4. Any patient with PIRADS 3 lesions per pMRI, AND elevated PSA ("=> 3.0 ng/ml" for patients between 40 and 75 years old, and "=> 4.0 ng/ml" for the patients older than 75 years).
5. Patients with PIRADS 1-2 lesions per pMRI, AND elevated PSA ("=> 3.0 ng/ml" for patients between 40 and 75 years old, and "=> 4.0 ng/ml" for the patients older than 75 years), AND at least one of the following:

1. High PSA density (0.15 ng/ml/g or higher),
2. suspicious DRE,
3. a positive/high-risk blood or urine biomarker test,
4. high-risk ancestry (Black/African American),
5. those with germline mutations that increase the risk for prostate cancer,
6. significant personal medical history,
7. significant family history,
8. persistent and significant increase in PSA levels (persistently elevated PSA for at least 12 months with an increase of at least 100% or more within 24 months, last level confirmed twice).

Exclusion Criteria:

1. Patients younger than 18 years old.
2. Any patient with PIRADS 4-5 lesion per pMRI.
3. Any patient with known csPCa (GS ≥7 (3+4)) per biopsy.
4. Any patient with PCa and managed with active surveillance, surgery or radiation.

a. (Patients who never scanned with pMRI before, had GS 6 (3+3) PCa only per systematic biopsy, and currently need confirmatory prostate biopsy will be allowed to enroll in the trial).
5. Medically unfit for anesthesia.
6. Any history of allergic reactions attributed to contrast agents, or other compounds of similar chemical compositions.
7. Any medical history preventing pMRI or prostate biopsy.
8. Any medical condition distorting quality of pMRI such as artificial hip prosthesis, and excessive rectal gas.
9. Any other condition that, in the opinion of the investigator, might interfere with the safe conduct of the study.

Inclusion of Women and Minorities: All participants will be men without previous diagnosis for PCa. Men of all ethnic groups and races are eligible for the study. Thus, women will not be included in this study.

Study Design

Enrollment

50 participants

Anticipated

Allocation

Randomized

Intervention Model

Parallel Assignment

Primary purpose

Diagnostic

Interventions and Outcome Measures

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

Interventions

Bi-parametric MRI-based cascaded deep-learning AI algorithm

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.

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 ]
  • Per-patient and per-lesion csPCa detection rates of AI algorithm-informed biopsy (the intervention arm) versus contemporary biopsy (the control arm) in patients randomly allocated 1:1 to each arm [ Time Frame: 4 months ]

Central Contacts and Locations

Central contacts

Locations

University of Arkansas for Medical Sciences

Recruiting

Little Rock, Arkansas, United States, 72205

Contacts

Ahmet Aydin, MD

501-686-8530

Principal Investigator:

Ahmet M Aydin, MD

More Information

Sponsor

University of Arkansas

Last update posted

Aug 19, 2026

Last verified

Aug, 2026

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by University of Arkansas on 2026-08-19.