Artificial Intelligence (AI)-Assisted Risk-based Prostate Cancer Detection

May 18, 2025 updated by: CHIU Ka Fung Peter, Chinese University of Hong Kong

Artificial Intelligence (AI)-Assisted Risk-based Prostate Cancer Detection: A Synergy of Novel Biomarkers, Advanced Imaging, and Robotic-assisted Diagnosis

This is a prospective clinical study recruiting 510 men at risk of PCa to undergo urine, blood, AI-assisted ultrasound and AI-assisted MRI investigations to stratify risk of clinically significant PCa (csPCa). (sample size calculation in section 5)

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

All recruited patients will undergo investigations including urine for spermine, blood for miRNA, TRUS, and MRI prostate. Patients with high suspicion of csPCa in any one step (urine, blood, ultrasound, OR MRI) will be offered an image-guided prostate biopsy. This will be followed by machine learning techniques to find the best combination in predicting csPCa.

Study Type

Observational

Enrollment (Estimated)

510

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Hong Kong
      • Hong Kong, Hong Kong
        • Recruiting
        • Prince of Wales Hospital, Chinese University of Hong Kong
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

14 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

All consecutive men referred for elevated PSA 4-20 ng/mL

Description

Inclusion Criteria:

  • Men ≥18 years of age
  • Clinical suspicion of prostate cancer
  • Serum Prostate-specific antigen (PSA) 4-20 ng/mL
  • Digital rectal examination ≤ cT2 (organ confined cancer)
  • Able to provide written informed consent

Exclusion Criteria:

  • Prior prostate biopsy
  • Past or current history of prostate cancer
  • Contraindicated to undergo plain MRI scan (e.g. pacemaker in-situ, claustrophobia)
  • Contraindicated to transperineal prostate biopsy: active urinary tract infection, fail TRUS probe insertion or lithotomy position, uncorrectable coagulopathy, antiplatelet or anticoagulant which cannot be stopped

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnosis of clinically significant Prostate cancer (csPCa); • csPCa is diagnosis of ISUP Grade group ≥2 prostate cancer in at least 1 biopsy core
Time Frame: Through study completion, an average of 1 year
Assessed by by machine learning algorithms utilizing clinical parameters, novel biomarkers and AI-assisted imaging
Through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnosis of any grade of prostate cancer
Time Frame: Through study completion, an average of 1 year
Assessed by prostate biopsy
Through study completion, an average of 1 year
Proportion of men with diagnosis of clinically insignificant prostate cancer
Time Frame: Through study completion, an average of 1 year
Assessed by prostate biopsy
Through study completion, an average of 1 year
Prostate biopsies that can be avoided
Time Frame: Through study completion, an average of 1 year
Assessed by using different machine learning algorithms
Through study completion, an average of 1 year
The concordance of AI-assisted TRUS & MRI diagnosis and biopsy outcomes
Time Frame: Through study completion, an average of 1 year
Assessed by using different machine learning algorithms and prostate biopsy result
Through study completion, an average of 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese University of Hong Kong

Investigators

  • Principal Investigator: Peter Ka-Fung CHIU, FRCS, PhD, Chinese University of Hong Kong

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2023

Primary Completion (Estimated)

July 31, 2026

Study Completion (Estimated)

October 30, 2026

Study Registration Dates

First Submitted

June 22, 2022

First Submitted That Met QC Criteria

June 30, 2022

First Posted (Actual)

July 5, 2022

Study Record Updates

Last Update Posted (Estimated)

May 20, 2025

Last Update Submitted That Met QC Criteria

May 18, 2025

Last Verified

May 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CRE 2022.245

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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