Prostate Cancer (Diagnosis)Clinically Significant Prostate Cancer
Investigational drug(s) / intervention(s)
No Intervention: Observational Cohort
No Intervention: Observational Cohort: This is an observational study. No new treatment, drug, device, or procedure is being administered to participants. Only standard-of-care clinical data, imaging, and pathology records are collected and analyzed.
Study summary
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.
Eligibility
Sex
MALE
Min age
40 Years
Max age
90 Years
Healthy volunteers
No
Inclusion Criteria:
1. Subjects who are scheduled to undergo or have undergone prostate biopsy or radical prostatectomy.
2. Subjects who have completed standard-of-care preoperative multiparametric MRI (mpMRI) and transrectal ultrasound (TRUS) examinations.
3. Subjects with complete pathological diagnosis results available.
4. Age between 40 and 90 years.
5. Able and willing to provide written informed consent (for prospective cohort participants only).
Exclusion Criteria:
1. Prior history of pelvic radiation therapy or radical prostatectomy.
2. Incomplete or poor-quality mpMRI or TRUS images (e.g., motion artifacts, insufficient sequences).
3. Concurrent other primary malignant tumors.
4. Severe systemic diseases that may affect the evaluation of the prostate.
5. Subjects with incomplete clinical or pathological data.
6. Contraindications to MRI examination (e.g., incompatible metallic implants, severe claustrophobia).
Primary outcome measure(s)
Area Under the Receiver Operating Characteristic Curve (AUC) for predicting clinically significant prostate cancer (csPCa) — Baseline (at the time of imaging/pathology data collection) The diagnostic performance of the multimodal deep learning model in predicting clinically significant prostate cancer using preoperative imaging data from this prospective and retrospective multicenter cohort. The AUC will be calculated to evaluate the model's discriminative ability.
Trial sites (1)
Facility
City
Region
Status
Liuzhou People's Hospital Affiliated to Guangxi Medical University
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
We use cookies to analyse site traffic and improve your experience. With your consent, we may also use cookies for advertising. You can change your choice at any time on our Cookie Policy page. See also our Privacy Policy.