Using Scans and Pathology Slides in a Computer Model to Predict Serious Prostate Cancer
Builds a computer model from scans and tissue slides to predict significant prostate cancer.
Plain-language title generated from the registry record
Registry title: Multimodal Imaging and Digital Pathology for Prostate Cancer Prediction
Key facts
- Where
- Liuzhou, ChinaSee the site
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 40 and 90 years old?
Source: NCT07614256, who can take part
Where in China
- Liuzhou People's Hospital Affiliated to Guangxi Medical UniversityLiuzhou, ChinaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Guangxi Medical University
- Trial code
- NCT07614256
- Record updated
- 29 May 2026
- Original description
This is a multicenter observational study. A deep learning model integrated with multimodal imaging and digital pathology spatial registration is built based on preoperative multiparametric magnetic resonance imaging, transrectal ultrasound and postoperative digital pathological whole slide images. The study is designed to achieve accurate prediction of clinically significant prostate cancer and non-invasive risk stratification. Unnecessary prostate biopsy and overdiagnosis can be reduced to support the optimization of clinical diagnosis and treatment strategies.Read full original descriptionShow less
A Multicenter Study of a Deep Learning Model Based on Spatial Registration of Multimodal Imaging and Digital Pathology for Predicting Clinically Significant Prostate Cancer
This is a multicenter observational study. A deep learning model integrated with multimodal imaging and digital pathology spatial registration is built based on preoperative multiparametric magnetic resonance imaging, transrectal ultrasound and postoperative digital pathological whole slide images. The study is designed to achieve accurate prediction of clinically significant prostate cancer and non-invasive risk stratification. Unnecessary prostate biopsy and overdiagnosis can be reduced to support the optimization of clinical diagnosis and treatment strategies.
Inclusion: Subjects who are scheduled to undergo or have undergone prostate biopsy or radical prostatectomy; Subjects who have completed standard-of-care preoperative multiparametric MRI (mpMRI) and transrectal ultrasound (TRUS) examinations; Subjects with complete pathological diagnosis results available; Age between 40 and 90 years; Able and willing to provide written informed consent (for prospective cohort participants only).
Exclusion: Prior history of pelvic radiation therapy or radical prostatectomy; Concurrent other primary malignant tumors; Contraindications to MRI examination (e.g., incompatible metallic implants, severe claustrophobia); Severe systemic diseases that may affect the evaluation of the prostate; Incomplete or poor-quality mpMRI or TRUS images (e.g., motion artifacts, insufficient sequences); Subjects with incomplete clinical or pathological data.