Benign Prostatic Hyperplasia, Healthy People, Prostate Cancer
Conditions
Brief 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.
Interventions
DIAGNOSTIC_TESTMulti-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.
Sponsors
Shanghai Changzheng Hospital
Ningbo No. 1 Hospital
The First Affiliated Hospital of Soochow University
The First Affiliated Hospital of Guangzhou Medical University
Jiangsu Provincial People's Hospital
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Zhongda Hospital
Northern Jiangsu People's Hospital
Changhai Hospital
West China Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
MALE
Age
18 Years to 80 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Sensitivity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant) | Through completion of study and all data analysis which may take up to one year. |
| Specificity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant) | Through completion of study and all data analysis which may take up to one year. |
| ROC value of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant) | Through completion of study and all data analysis which may take up to one year. |
Secondary
| Measure | Time frame |
|---|---|
| ROC value of a Prostate Cancer Multimodal Model in Predicting the Pathological Outcomes of Gleason Score Categories (≤6, 7, ≥8) in Men Underwent for Prostate Biopsy | Through completion of study and all data analysis which may take up to one year. |
| Sensitivity of a Prostate Cancer Multimodal Model in Predicting the Pathological Outcomes of Gleason Score Categories (≤6, 7, ≥8) in Men Underwent for Prostate Biopsy | Through completion of study and all data analysis which may take up to one year. |
| Specificity of a Prostate Cancer Multimodal Model in Predicting the Pathological Outcomes of Gleason Score Categories (≤6, 7, ≥8) in Men Underwent for Prostate Biopsy | Through completion of study and all data analysis which may take up to one year. |
| ROC value of a Prostate Cancer Multimodal Model in Predicting Clinically Significant Prostate Cancer (csPCa) in Men Underwent Prostate Biopsy | Through completion of study and all data analysis which may take up to one year. |
| Sensitivity of a Prostate Cancer Multimodal Model in Predicting Clinically Significant Prostate Cancer (csPCa) in Men Underwent Prostate Biopsy | Through completion of study and all data analysis which may take up to one year. |
| Specificity of a Prostate Cancer Multimodal Model in Predicting Clinically Significant Prostate Cancer (csPCa) in Men Underwent Prostate Biopsy | Through completion of study and all data analysis which may take up to one year. |
Countries
China
Outcome results
None listed