The Application of Multimodal Artificial Intelligence Systems in Prostate Cancer Diagnosis and Prognosis Analysis

Naval Military Medical University (Second Military Medical University)

Status: Completed

Conditions: Benign Prostatic Hyperplasia; Healthy People; Prostate Cancer

Treatments: Diagnostic Test: Multi-modal artificial intelligence model (BEAM)

Study type: Observational

Funder types: Other

Identifiers

NCT06589154

M_PCa

Details and patient eligibility

About

Prostate-specific antigen (PSA) testing has limited specificity for prostate cancer diagnosis, leading to a high rate of unnecessary biopsies. This multi-center study aims to develop and validate a non-invasive, multi-modal artificial intelligence model that combines cell-free DNA (cfDNA) profiles with multi-parametric MRI (mpMRI). The primary goal is to improve the accuracy of prostate cancer detection and risk stratification, particularly for men with PSA levels in the 4-10 ng/mL "gray zone," thereby providing a robust tool to guide clinical decision-making and reduce avoidable invasive procedures.

Full description

Prostate cancer is a leading cause of cancer morbidity in men globally. The current diagnostic pathway, heavily reliant on PSA levels, is particularly challenging in the 4-10 ng/mL "gray zone," where its inability to reliably distinguish benign conditions from cancer results in a substantial number of unnecessary biopsies and the overtreatment of indolent disease.

While advanced non-invasive methods like cfDNA analysis and mpMRI have shown individual promise, each possesses inherent limitations when used as a standalone tool. cfDNA assays can lack sensitivity due to low tumor fraction, and mpMRI interpretation is subject to variability and has suboptimal accuracy. This study hypothesizes that a synergistic fusion of these complementary data modalities-integrating the systemic molecular information from cfDNA with the localized anatomical and functional data from mpMRI-can overcome these limitations.

To test this hypothesis, we developed a multimodal Model, an end-to-end deep learning framework. This study was designed to rigorously develop and validate the BEAM model across a large, multi-center population, including a retrospective discovery cohort and two prospective validation cohorts. The ultimate goal is to establish a powerful, non-invasive tool that can accurately detect prostate cancer and, critically, stratify patients by risk of clinically significant disease, thereby personalizing patient management.

Enrollment: 1,651 patients

Sex: Male

Ages: 18 to 80 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Men aged 18-80 years with a clinical indication for prostate or pelvic magnetic resonance (MR) examination.
• Patients with normal prostate, benign prostatic hyperplasia, or prostate cancer.
• First visit on January 1, 2014, or later.

Exclusion criteria

• Diagnosis of any other malignancy within the previous 5 years.
• Prior transurethral resection or enucleation of the prostate before imaging.
• Any condition deemed by the investigator to make the patient unsuitable for study participation.

Trial design

1,651 participants in 3 patient groups

Discovery cohort

Description:

Participants with PSA levels \>4 ng/mL and had undergone prostatic biopsy and mpMR according to the investigators retrospectively.

Treatment:

Diagnostic Test: Multi-modal artificial intelligence model (BEAM)

Prospective internal validation cohort

Description:

Patients who are scheduled for prostate biopsy and mpMR, with PSA levels in the 4-10 ng/mL gray zone, will be consented and enrolled in this group prospectively.

Treatment:

Diagnostic Test: Multi-modal artificial intelligence model (BEAM)

Prospective external validation cohort

Description:

Patients who are scheduled for prostate biopsy and mpMR, with PSA levels in the 4-10 ng/mL gray zone, will be consented and enrolled in this group prospectively.

Treatment:

Diagnostic Test: Multi-modal artificial intelligence model (BEAM)