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| Name | Class |
|---|---|
| Ningbo No. 1 Hospital | OTHER |
| The First Affiliated Hospital of Soochow University | OTHER |
| The First Affiliated Hospital of Guangzhou Medical University | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Discovery cohort | Participants with PSA levels >4 ng/mL and had undergone prostatic biopsy and mpMR according to the investigators retrospectively. |
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| 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. |
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| 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | 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. |
| Measure | Description | 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 |
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Inclusion Criteria:
Exclusion Criteria:
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People who are required to undergo prostatic or pelvic magnetic resonance (MR) examination
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cancer Hospital, Chinese Academy of Medical Sciences | Beijing | Beijing Municipality | 100021 | China | ||
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| Jiangsu Provincial People's Hospital |
| OTHER |
| Cancer Institute and Hospital, Chinese Academy of Medical Sciences | OTHER |
| Zhongda Hospital | OTHER |
| Northern Jiangsu People's Hospital | OTHER |
| Changhai Hospital | OTHER |
| West China Hospital | OTHER |
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| 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. |
| The First Affiliated Hospital of Guangzhou Medical University |
| Guangzhou |
| Guangdong |
| 510120 |
| China |
| Jiangsu Provincial People's Hospita | Nanjing | Jiangsu | 210029 | China |
| Zhongda Hospital, Southeast University | Nanjing | Jiangsu | China |
| The First Affiliated Hospital of Soochow University | Suzhou | Jiangsu | 215006 | China |
| Northern Jiangsu People's Hospita | Yangzhou | Jiangsu | 225001 | China |
| Changhai Hospital | Shanghai | Shanghai Municipality | 200433 | China |
| Shanghai Changzheng Hospital | Shanghai | Shanghai Municipality | 201209 | China |
| West China Hospital, Sichuan University | Chengdu | Sichuan | 610041 | China |
| Ningbo No. 1 Hospita | Ningbo | Zhejiang | 315010 | China |
| ID | Term |
|---|---|
| D011470 | Prostatic Hyperplasia |
| D011471 | Prostatic Neoplasms |
| ID | Term |
|---|---|
| D011469 | Prostatic Diseases |
| D005832 | Genital Diseases, Male |
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |
| D005834 | Genital Neoplasms, Male |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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