To Evaluate an MRI-based Optimized Prostate Cancer Diagnostic Pathway Powered by Artificial Intelligence

NCT06360523 | Recruiting

• 1 other identifier: CRE-2024.141
• Study Type: interventional
• Target: 368
• Locations: 1 country
• Sites: 1

Brief Summary

It is a prospective paired-cohort study for diagnostic test evaluation. The study aim to determine the accuracy of AI review and investigate whether AI review could detect MRI visible significant cancer as effective as radiologist review. MRI image of about 368 men recommended for biopsy will be reviewed by an AI model and an experienced radiologist, respectively. AI review (index) and radiologist review (standard) will be blinded to each other, while biopsy urologists will be well-informed of the findings of both AI review and radiologist review and make personalized biopsy plan by combining both findings. The pathological results of MRI-ultrasound fusion biopsy (reference) will serve as the gold standard to assess the diagnostic accuracy.

Conditions

Prostate Cancer

Outcome Measures

Primary Outcomes (1)

• Prostate Cancer detection rate of ISUP grade group ≥2

  Assessed by AI review, Radiologist review, and Biopsy (Targeted).

  Through study completion, an average of 1 year

Secondary Outcomes (1)

• Diagnosis of any grade of prostate cancer

  Through study completion, an average of 1 year

Study Arms (2)

AI review (index)

EXPERIMENTAL

self-paired design, AI review (index)

Diagnostic Test: AI modal

Radiologist review (Standard)

ACTIVE COMPARATOR

self-paired design, radiologist review (standard)

Diagnostic Test: Standard review

Interventions

AI modal DIAGNOSTIC_TEST

The MRI image will be reviewed by radiologist and AI model(s) respectively. The urologist will combine the results of the two approaches to optimize the biopsy strategy which is expected to result in more accurate diagnosis.

AI review (index)

Standard review DIAGNOSTIC_TEST

The MRI image will be reviewed by radiologist.

Radiologist review (Standard)

Eligibility Criteria

• Age: 18 Years+
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients recommended for prostate biopsy for diagnosis or re-assessment.
• Men at least 18 years or over
• Patients with prostate MRI image eligible for radiologist review and AI review.
• Patient Informed Consent is signed.

You may not qualify if:

• Patient contraindicated to prostate biopsy
• Patient failed to complete the biopsy procedure

Sponsors & Collaborators

• Chinese University of Hong Kong: lead

Study Sites (1)

Prince of Wales Hospital, Chinese University of Hong Kong

Hong Kong, Hong Kong

RECRUITING

Related Publications (16)

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  PMID: 23787357 BACKGROUND

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MeSH Terms

Conditions

Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Study Officials

• Peter CHIU, FRCS, PhD

  Chinese University of Hong Kong

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Peter Ka-Fung CHIU, FRCS, PhD

CONTACT

35052625; peterchiu@surgery.cuhk.edu.hk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: CARE PROVIDER
• Masking Details: AI review (index) and radiologist review (standard) will be blinded to each other, while biopsy urologists will be well-informed of the findings of both AI review and radiologist review and make personalized biopsy plan by combining both findings.
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: April 8, 2024
• First Posted: April 11, 2024
• Study Start: June 14, 2024
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): March 31, 2028
• Last Updated: May 6, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)