The Application of Multimodal Artificial Intelligence Systems in Prostate Cancer Diagnosis and Prognosis Analysis
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Shanghai Changzheng Hospital
- Enrollment
- 1,651
- Locations
- 16
- Primary Endpoint
- Sensitivity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant)
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to 80 Years (Adult, Older Adult)
- Sex
- Male
- Accepts 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.
Arms & Interventions
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.
Intervention: Multi-modal artificial intelligence model (BEAM) (Diagnostic Test)
Discovery cohort
Participants with PSA levels >4 ng/mL and had undergone prostatic biopsy and mpMR according to the investigators retrospectively.
Intervention: Multi-modal artificial intelligence model (BEAM) (Diagnostic Test)
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.
Intervention: Multi-modal artificial intelligence model (BEAM) (Diagnostic Test)
Outcomes
Primary Outcomes
Sensitivity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant)
Time Frame: 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)
Time Frame: 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)
Time Frame: Through completion of study and all data analysis which may take up to one year.
Secondary Outcomes
- 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.)
Investigators
Ren Shancheng
Professor, Chief of Urology
Shanghai Changzheng Hospital