Healthy PeopleNCT06589154
The Application of Multimodal Artificial Intelligence Systems in Prostate Cancer Diagnosis and Prognosis Analysis
An observational study of Healthy People and Benign Prostatic Hyperplasia, run by Shanghai Changzheng Hospital at 10 sites in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 80 years, men onlyHealthy volunteers can take part
- Conditions the study is for
- Healthy People, Benign Prostatic Hyperplasia, Prostate Cancer
- What is being tested?
- Multi-modal artificial intelligence model (BEAM)
- How many people
- 1,651 participants
- Where
- 10 sitesChina
- Study dates
- 10 Oct 2024to 30 Jul 2025
- How long will I take part?
- 2 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- Men only
- Healthy volunteers accepted
- Adult, Older adult
Study population: People who are required to undergo prostatic or pelvic magnetic resonance (MR) examination
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
10 sites in 1 country.
10 sites in 1 country
China10 sites
Cancer Hospital, Chinese Academy of Medical Sciences
Beijing, Beijing Municipality, 100021
West China Hospital, Sichuan University
Chengdu, Sichuan, 610041
The First Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510120
Jiangsu Provincial People's Hospita
Nanjing, Jiangsu, 210029
Zhongda Hospital, Southeast University
Nanjing, Jiangsu
Ningbo No. 1 Hospita
Ningbo, Zhejiang, 315010
Changhai Hospital
Shanghai, Shanghai Municipality, 200433
Shanghai Changzheng Hospital
Shanghai, Shanghai Municipality, 201209
The First Affiliated Hospital of Soochow University
Suzhou, Jiangsu, 215006
Northern Jiangsu People's Hospita
Yangzhou, Jiangsu, 225001
About this study
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.
Read the 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.
- Being tested
- Multi-modal artificial intelligence model (BEAM)
- Medical subject headings
- Prostatic Hyperplasia, Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multi-modal artificial intelligence model (BEAM)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.
| Study group | Role | What they receive |
|---|---|---|
| Discovery cohort | Participants with PSA levels >4 ng/mL and had undergone prostatic biopsy and mpMR according to the investigators retrospectively. Receives: Diagnostic Test: Multi-modal artificial intelligence model (BEAM) | |
| 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. Receives: Diagnostic Test: Multi-modal artificial intelligence model (BEAM) | |
| 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. Receives: Diagnostic Test: Multi-modal artificial intelligence model (BEAM) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant)
Measured: 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)
Measured: 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)
Measured: Through completion of study and all data analysis which may take up to one year.
Secondary outcomes (6)
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
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: Through completion of study and all data analysis which may take up to one year.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start10 Oct 2024
- Primary completion30 Jul 2025
- Study completion30 Jul 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Healthy PeopleStudies of the same condition, open and under way.
- More trials run by Shanghai Changzheng HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,651 participants (actual)
- Target duration
- 2 Months
Sponsor and investigators
- Lead sponsor
- Shanghai Changzheng Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- 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
- Responsible party
- Principal investigator, Ren Shancheng, Shanghai Changzheng Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06589154
- Sponsor's protocol ID
- M_PCa
- Status last verified
- Jul 2026
- First submitted
- 6 Sep 2024
- Last updated
- 23 Jul 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Application of Multimodal Artificial Intelligence Systems in Prostate Cancer Diagnosis and Prognosis Analysis
- ClinicalTrials.gov ID
- NCT06589154
- Acronym / sponsor ID
- M_PCa
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,651 participants (actual)
- Lead sponsor
- Shanghai Changzheng HospitalUniversity, hospital or other
- Collaborators
- 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 start
- 10 Oct 2024 (actual)
- Primary completion
- 30 Jul 2025 (actual)
- Study completion
- 30 Jul 2025 (actual)
- First posted
- 19 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 2 Months
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Discovery cohort | – | Diagnostic Test: Multi-modal artificial intelligence model (BEAM)Participants with PSA levels >4 ng/mL and had undergone prostatic biopsy and mpMR according to the investigators retrospectively. |
| Prospective internal validation cohort | – | Diagnostic Test: Multi-modal artificial intelligence model (BEAM)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. |
| Prospective external validation cohort | – | Diagnostic Test: Multi-modal artificial intelligence model (BEAM)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. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- Male only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- People who are required to undergo prostatic or pelvic magnetic resonance (MR) examination
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Diagnostic test | Discovery cohort, Prospective external validation cohort, Prospective internal validation cohort |
Primary outcome
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.
6 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: Through completion of study and all data analysis which may take up to one year.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jul 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
10 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Healthy People and Benign Prostatic Hyperplasia, run by Shanghai Changzheng Hospital at 10 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 23 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 10 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jul 2026; AzTrials last refreshed its copy on 6 Nov 2026.