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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

1651 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-10-10

Study Completion Date

2025-07-30

Brief Summary

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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.

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Detailed Description

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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

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Healthy People Benign Prostatic Hyperplasia Prostate Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Discovery cohort

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

Multi-modal artificial intelligence model (BEAM)

Intervention Type DIAGNOSTIC_TEST

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.

Prospective internal validation cohort

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.

Multi-modal artificial intelligence model (BEAM)

Intervention Type DIAGNOSTIC_TEST

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.

Prospective external validation cohort

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.

Multi-modal artificial intelligence model (BEAM)

Intervention Type DIAGNOSTIC_TEST

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.

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

MALE

Accepts Healthy Volunteers

Yes