Multimodal Imaging and Digital Pathology for Prostate Cancer Prediction
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexMale
Age40-90
SponsorGuangxi Medical University
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
Prior history of pelvic radiation therapy or radical prostatectomy.
Incomplete or poor-quality mpMRI or TRUS images (e.g., motion artifacts, insufficient sequences).
Concurrent other primary malignant tumors.
Severe systemic diseases that may affect the evaluation of the prostate.
Trial population
This is a prospective and retrospective multicenter cohort study. The study population consists of consecutive male subjects aged 40-90 years who are scheduled to undergo or have undergone prostate biopsy or radical prostatectomy, with complete standard-of-care preoperative multiparametric MRI (mpMRI), transrectal ultrasound (TRUS) images, and corresponding pathological diagnosis results. The collected data include: 1. Preoperative mpMRI and TRUS images 2. Digital whole-slide images of prostate biopsy specimens 3. Digital whole-slide images of radical prostatectomy specimens (if performed) The prospective cohort will include newly enrolled subjects who provide written informed consent, while the retrospective cohort will include historical subjects with complete imaging, pathology slide, and clinical data from participating centers.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
No Intervention: Observational Cohort
Other interventionThis 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.
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
Area Under the Receiver Operating Characteristic Curve (AUC) for predicting clinically significant prostate cancer (csPCa)
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.
Time frame
Baseline (at the time of imaging/pathology data collection)Sponsors and contacts
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Source ClinicalTrials.gov