NCT06589154

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 1,651 (actual)
• Sponsor: Shanghai Changzheng Hospital · Academic / Other
• Sex: Male
• Age: 18 Years – 80 Years
• Healthy volunteers: Accepted

Summary

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.

Detailed 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.

Conditions

• Healthy People
• Benign Prostatic Hyperplasia
• Prostate Cancer

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Multi-modal artificial intelligence model (BEAM)	Data from mpMRI and cfDNA analysis will be integrated and processed by deep learning. The model's output will be compared against the final pathological diagnosis from the prostate biopsy to evaluate its performance.

Timeline

• Start date: 2024-10-10
• Primary completion: 2025-07-30
• Completion: 2025-07-30
• First posted: 2024-09-19
• Last updated: 2025-09-02

Locations

10 sites across 1 country: China