Prostate CancerNCT07231627
AI Algorithm-Informed Biopsy for Prostate Cancer Detection With Indeterminate and Low-Risk Prostate MRI Lesions
A study testing Bi-parametric MRI-based cascaded deep-learning AI algorithm in people with Prostate Cancer, run by University of Arkansas at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 40 years and older, men only
- Conditions the study is for
- Prostate Cancer
- What is being tested?
- Bi-parametric MRI-based cascaded deep-learning AI algorithm
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 50 participants
- Where
- 1 siteUnited States
- Study dates
- 19 Jun 2026to Jan 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 years and older
- Men only
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 40 years of age or older.
- A recent pMRI performed within last 12 weeks
- Eastern Cooperative Oncology Group (ECOG) performance status 0 - 1.
- 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).
- 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:
- High PSA density (0.15 ng/ml/g or higher),
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- suspicious DRE,
- a positive/high-risk blood or urine biomarker test,
- high-risk ancestry (Black/African American),
- those with germline mutations that increase the risk for prostate cancer,
- significant personal medical history,
- significant family history,
- 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).
Who cannot join
- Patients younger than 18 years old.
- Any patient with PIRADS 4-5 lesion per pMRI.
- Any patient with known csPCa (GS ≥7 (3+4)) per biopsy.
- Any patient with PCa and managed with active surveillance, surgery or radiation.
- (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).
- Medically unfit for anesthesia.
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- Any history of allergic reactions attributed to contrast agents, or other compounds of similar chemical compositions.
- Any medical history preventing pMRI or prostate biopsy.
- Any medical condition distorting quality of pMRI such as artificial hip prosthesis, and excessive rectal gas.
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
University of Arkansas for Medical Sciences
Little Rock, Arkansas, 72205
Ahmet Aydin, MD, 501-686-8530
Ahmet M Aydin, MD
About this study
Official title: A Prospective Randomized Phase I/II Study of Artificial Intelligence Algorithm-Informed Biopsy for Detection of Prostate Cancer in Patients With Indeterminate and Low-risk Prostate MRI Lesions
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.
Read the detailed description
Primary Feasibility Objective:
- Assess the acceptance rate of randomization and biopsy recommendations based on study protocol and AI algorithm results by the patients. This will be assessed in the first 10 patients who enroll during the phase I feasibility segment.
Primary Efficacy Objective:
- Evaluate the 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. This will be evaluated in all 25 patients per arm (50 patients).
Secondary Objectives (These objectives will be satisfied using endpoint data from all 50 subjects (25/arm) enrolled):
- Evaluate benign and clinically non-significant PCa rates (GS <7) in patients who underwent AI-algorithm informed (the intervention arm) versus contemporary (the control arm) prostate biopsies.
- Evaluate the specificity and sensitivity of AI algorithm-informed biopsy (AI-targeted and perilesional prostate biopsy) versus contemporary biopsy in detection of csPCa.
- Obtain and evaluate adverse events (AEs), urinary function (IPSS), sexual function (IIEF) quality of life (QOL) [ SF-12 and TMI scores] and decision regret (DRS) measures on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy.
Exploratory Objective:
- Collect data via genomic and transcriptomic approaches (Whole exome sequencing + Targeted RNA sequencing OR single cell RNA sequencing) in patients whose standard contemporary biopsy, perilesional biopsy and AI-targeted biopsy revealed csPCa, and compare collected data on all endpoints for differences among perilesional biopsy, AI-targeted biopsy and contemporary standard biopsy.
- Conditions
- Prostate Cancer
- Being tested
- Bi-parametric MRI-based cascaded deep-learning AI algorithm
- Medical subject headings
- Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Bi-parametric MRI-based cascaded deep-learning AI algorithmDevice
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 group | Role | What they receive |
|---|---|---|
| Bi-parametric MRI-based cascaded deep-learning AI algorithm | Experimental | 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. Receives: Device: Bi-parametric MRI-based cascaded deep-learning AI algorithm |
| Perilesional prostate biopsy | No intervention | 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
Acceptance rate of randomization and biopsy recommendations based on study protocol and AI algorithm results by the patients
Number and percent of the first 10 enrolled and randomized subjects who agree to undergo the prostate biopsy procedure to which they were randomized and accept the biopsy recommendations based on study protocol and AI algorithm results.
Measured: 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
The percent of csPCa detected per-patient and per-lesion in the biopsy cores obtained from each study arm. We expect at least 42% for the csPCa detection rate on the AI algorithm-informed-biopsy arm, which would be a 27% increase relative to the current csPCa detection rate (15%) expected on the contemporary prostate-biopsy arm.
Measured: 4 months
Secondary outcomes (7)
Percentage of benign and clinically non-significant PCa detected in patients who underwent AI Algorithm-informed or contemporary prostate biopsies.
Measured: 4 months
Percentage of true positive, false positive, true negative and false negative findings for csPCa in all patients who enrolled in both study arms.versus contemporary biopsy in detection of csPCa
Measured: 4 months
Evaluation of adverse events on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy graded by CTCAE v5.0
Measured: 4 months
Evaluation of urinary function (IPSS) on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy
Measured: 4 months
Evaluation of sexual function (Form IIEF) on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy
Measured: 4 months
Evaluation of quality of life using Form TMI and Form SF12 scores on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy
Measured: 4 months
Evaluation of decision regret using the Decision Regret Scale (Form DRS) to measure on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy
Measured: 4 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Nov 2025
- Study start19 Jun 2026
- Primary completionJan 2028 (estimated)
- Study completionJan 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostate CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Arkansas
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Ahmet M Aydin, MD, Principal investigator, University of Arkansas
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07231627
- Sponsor's protocol ID
- 299514
- Status last verified
- Aug 2026
- First submitted
- 13 Nov 2025
- Last updated
- 19 Aug 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Randomized Phase I/II Study of Artificial Intelligence Algorithm-Informed Biopsy for Detection of Prostate Cancer in Patients With Indeterminate and Low-risk Prostate MRI Lesions
- ClinicalTrials.gov ID
- NCT07231627
- Acronym / sponsor ID
- 299514
- Conditions
- Prostate Cancer
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- University of ArkansasUniversity, hospital or other
- Study start
- 19 Jun 2026 (actual)
- Primary completion
- Jan 2028 (estimated)
- Study completion
- Jan 2029 (estimated)
- First posted
- 17 Nov 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Primary Feasibility Objective:
- Assess the acceptance rate of randomization and biopsy recommendations based on study protocol and AI algorithm results by the patients. This will be assessed in the first 10 patients who enroll during the phase I feasibility segment.
Primary Efficacy Objective:
- Evaluate the 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. This will be evaluated in all 25 patients per arm (50 patients).
Secondary Objectives (These objectives will be satisfied using endpoint data from all 50 subjects (25/arm) enrolled):
- Evaluate benign and clinically non-significant PCa rates (GS <7) in patients who underwent AI-algorithm informed (the intervention arm) versus contemporary (the control arm) prostate biopsies.
- Evaluate the specificity and sensitivity of AI algorithm-informed biopsy (AI-targeted and perilesional prostate biopsy) versus contemporary biopsy in detection of csPCa.
- Obtain and evaluate adverse events (AEs), urinary function (IPSS), sexual function (IIEF) quality of life (QOL) [ SF-12 and TMI scores] and decision regret (DRS) measures on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy.
Exploratory Objective:
- Collect data via genomic and transcriptomic approaches (Whole exome sequencing + Targeted RNA sequencing OR single cell RNA sequencing) in patients whose standard contemporary biopsy, perilesional biopsy and AI-targeted biopsy revealed csPCa, and compare collected data on all endpoints for differences among perilesional biopsy, AI-targeted biopsy and contemporary standard biopsy.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Bi-parametric MRI-based cascaded deep-learning AI algorithm | Experimental | Device: Bi-parametric MRI-based cascaded deep-learning AI algorithmThe 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. |
| Perilesional prostate biopsy | No intervention | –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 |
Who may qualify
- Ages
- 40 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (13 listed)
- 40 years of age or older.
- A recent pMRI performed within last 12 weeks
- Eastern Cooperative Oncology Group (ECOG) performance status 0 - 1.
- 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).
- 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:
Key exclusion criteria (11 listed)
- Patients younger than 18 years old.
- Any patient with PIRADS 4-5 lesion per pMRI.
- Any patient with known csPCa (GS ≥7 (3+4)) per biopsy.
- Any patient with PCa and managed with active surveillance, surgery or radiation.
- Medically unfit for anesthesia.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Bi-parametric MRI-based cascaded deep-learning AI algorithmArtificial 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. | Device | Bi-parametric MRI-based cascaded deep-learning AI algorithm |
Primary outcome
Acceptance rate of randomization and biopsy recommendations based on study protocol and AI algorithm results by the patients
Time frame: 4 months
Number and percent of the first 10 enrolled and randomized subjects who agree to undergo the prostate biopsy procedure to which they were randomized and accept the biopsy recommendations based on study protocol and AI algorithm results.
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
The percent of csPCa detected per-patient and per-lesion in the biopsy cores obtained from each study arm. We expect at least 42% for the csPCa detection rate on the AI algorithm-informed-biopsy arm, which would be a 27% increase relative to the current csPCa detection rate (15%) expected on the contemporary prostate-biopsy arm.
7 secondary outcomes
Percentage of benign and clinically non-significant PCa detected in patients who underwent AI Algorithm-informed or contemporary prostate biopsies.
Time frame: 4 months
Percentage of true positive, false positive, true negative and false negative findings for csPCa in all patients who enrolled in both study arms.versus contemporary biopsy in detection of csPCa
Time frame: 4 months
Evaluation of adverse events on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy graded by CTCAE v5.0
Time frame: 4 months
Evaluation of urinary function (IPSS) on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy
Time frame: 4 months
Evaluation of sexual function (Form IIEF) on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy
Time frame: 4 months
Evaluation of quality of life using Form TMI and Form SF12 scores on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy
Time frame: 4 months
Evaluation of decision regret using the Decision Regret Scale (Form DRS) to measure on subjects that underwent contemporary biopsy versus AI Algorithm-informed biopsy
Time frame: 4 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Aug 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ahmet M Aydin, MD principal investigator
University of Arkansas
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Bi-parametric MRI-based cascaded deep-learning AI algorithm in people with Prostate Cancer, run by University of Arkansas at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 13 inclusion and 11 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 19 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Aaron Holley (5016868274, JAHolley@uams.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the 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). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Aug 2026; AzTrials last refreshed its copy on 27 Oct 2026.