Comprehensive Evaluation of MRI-AI in Prostate Cancer Diagnosis

Comprehensive Evaluation of MRI-AI in Prostate Cancer Diagnosis: a Real-World Prospective Diagnostic Study

The goal of this real-world prospective diagnostic study is to comprehensively evaluate the value of MRI artificial intelligence (MRI-AI) in assisting the diagnosis of prostate cancer (PCa). The main questions it aims to answer are:

Does MRI-AI promote the accurate diagnosis and treatment of prostate cancer? What's the capability of prostate MRI-AI in calculating the prostate volumn? What's the value of prostate MRI-AI assistant diagnosis system in detecting the suspicious lesions on MRI and guiding prostate targeted biopsy? What's the value of prostate MRI-AI assistant diagnosis system in predicting the pathological results of prostate targeted biopsy? Researchers will compare the cancer detection rates of suspicious lesions detected by MRI-AI and senior radiologists.

Participants will:

Receive combination of systematic biopsy and targeted biopsy.

Study Overview

• Status: Completed
• Conditions: Prostate Cancer
• Intervention / Treatment: Diagnostic test: Combination of targeted biopsy and systematic biopsy

Detailed Description

In recent years, there have been remarkable advancements in the field of artificial intelligence (AI) techniques, particularly in the medical domain. These AI techniques have demonstrated the ability to significantly enhance various medical tasks, such as tumor detection, classification, and prognosis prediction. Increasing evidence supports the ability of AI to facilitate precise diagnosis of PCa and assist in therapeutic decisions. Compared with doctors, AI has the potential to identify not only holistic tumor morphology but also task-specific and granular radiological patterns that cannot be detected by the naked eye. Therefore, AI has great potential to reduce inconsistencies between observers and improve diagnostic accuracy. Previous AI studies at our institution have developed deep learning-based AI models trained on MR images that achieve good performance in the detection and localization of clinically significant prostate cancer (csPCa). Furthermore, the trained AI algorithms were embedded into proprietary structured reporting software, and radiologists simulated their real-life work scenarios to interpret and report the PI-RADS category of each patient using this AI-based software. However, the data is mostly retrospective. The capability of detecting the suspicious lesions on MRI, guiding the prostate targeted biopsy, and optimizing the biopsy scheme warrants further investigation.

The goal of this real-world prospective diagnostic study is to comprehensively evaluate the value of MRI artificial intelligence (MRI-AI) in assisting the diagnosis of prostate cancer (PCa). The main questions it aims to answer are:

Does MRI-AI promote the accurate diagnosis and treatment of prostate cancer? What's the capability of prostate MRI-AI in calculating the prostate volumn? What's the value of prostate MRI-AI assistant diagnosis system in detecting the suspicious lesions on MRI and guiding prostate targeted biopsy? What's the value of prostate MRI-AI assistant diagnosis system in predicting the pathological results of prostate targeted biopsy? Researchers will compare the cancer detection rates of suspicious lesions detected by MRI-AI and senior radiologists.

Participants will:

Receive combination of systematic biopsy and targeted biopsy.

• Study Type: Interventional
• Enrollment (Actual): 365
• Phase: Not Applicable

Contacts and Locations

Study Locations

• China
  • Beijing Municipality
    • Beijing, Beijing Municipality, China, 100034
      • Peking University First Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• The age of the patient is between 45 and 85.
• Patients with complete magnetic resonance imaging (MRI) data, qualified image quality control.
• Patients were in accordance with the indication of prostate biopsy, including patients with suspicious prostate nodes found by digital rectal examination (DRE), the suspicious lesions found by transrectal ultrasound (TRUS) or MRI, total prostate-specific antigen (tPSA) >10ng/mL, tPSA 4-10ng/mL with free-to-total PSA ratio (f/tPSA) <0.16 or PSA density (PSAD) >0.15.
• Patients had no history of prior prostate surgery or biopsy.
• The PSA of patients should be ≤20 ng/mL.
• The prostate biopsy pathological results of above lesions were complete. The time interval between targeted prostate biopsy and prostate MRI examination should not exceed one month.
• Patients with complete clinical information.

Exclusion Criteria:

• The clinicopathological information and MRI data was unqualified or incomplete.
• Patients had received radiotherapy, chemotherapy, androgen deprivation therapy, or surgery treatment before prostate MRI examination or prostate biopsy.
• Patients received prior prostate biopsy.
• Patients had contraindications to MRI or prostate biopsy.
• Patients were not in accordance with the indication of prostate biopsy.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: Patients with the indication of prostate biopsy; The trained AI algorithms were embedded into proprietary structured reporting software. Before prostate biopsy, the MR images of patients were uploaded to the AI software. The prostate gland and suspicious lesions were annotated and highlighted by AI software. Urogenital radiologists who were blinded to MRI-AI reports independently reviewed the MR images, annotated the suspicious lesions. Then the urologists read both the MRI-AI reports and urogenital radiologist's reports, and conducted 3-5 core targeted biopsy (TB) at each suspicious lesion found by MRI-AI and urogenital radiologists, followed by 12 core systematic biopsy (SB).

Diagnostic test: Combination of targeted biopsy and systematic biopsy; Before prostate biopsy, the MR images of patients were independently reviewed by MRI-AI and urogenital radiologists. Then the images with suspicious lesions highlighted by MRI-AI and urogenital radiologists. Urologists conducted targeted biopsies for all suspicious lesions and systematic biopsies. Biopsies were performed under the guidance of transrectal ultrasound (TRUS) through the transrectal or transperineal route.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The clinically significant prostate cancer (csPCa) detection rate for suspicious lesions found by MRI-AI and urogenital radiologists	csPCa was defined as PCa with a grade group ≥ 2 or GS ≥ 3+4. The reference standard was the pathological results of targeted biopsies for the suspicious lesions.	One month after the biopsy procedure.
High-grade PCa detection rate	High-grade PCa was defined as PCa with a grade group ≥3 or GS ≥ 4+3. The reference standard was the pathological results of targeted biopsies for the suspicious lesions.	One month after the biopsy procedure.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The PCa detection rate	The PCa detection rate for the suspicious lesions found by MRI-AI and urogenital radiologists.	One month after the biopsy procedure.
Diagnostic performance	Diagnostic performance assessment includes accuracy, sensitivity, specificity, negative predicative value, and positive predicative value	One month after the biopsy procedure
clinically insignificant PCa (ciPCa) detection rate	ciPCa was defined as PCa with a grade group=1 or GS=3+3. The reference standard was the pathological results of targeted biopsies for the suspicious lesions.	One month after the biopsy procedure.

Collaborators and Investigators

• Sponsor: Peking University First Hospital
• Investigators: Principal Investigator: Yi LIU, Peking University First Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2024
• Primary Completion (Actual): June 30, 2025
• Study Completion (Actual): August 31, 2025

Study Registration Dates

• First Submitted: August 26, 2024
• First Submitted That Met QC Criteria: August 26, 2024
• First Posted (Actual): August 28, 2024

Study Record Updates

• Last Update Posted (Actual): May 6, 2026
• Last Update Submitted That Met QC Criteria: May 5, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Prostate cancer; Diagnosis; Artificial intelligence; Targeted biopsy; Diagnostic study
• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Pathologic Processes; Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Prostatic Diseases; Male Urogenital Diseases; Pathological Conditions, Signs and Symptoms; Prostatic Neoplasms; Disease
• Other Study ID Numbers: AITB-003

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No