Clinically Significant Prostate Cancer, Prostate Cancer (Diagnosis)
Conditions
Keywords
Prostate cancer, Clinically significant prostate cancer, Multiparametric Magnetic Resonance Imaging, Transrectal ultrasound, Digital pathology, Deep learning, Artificial intelligence, Spatial registration, Risk stratification
Brief 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.
Detailed description
This prospective and retrospective multicenter observational study enrolls patients with suspected prostate cancer who receive standardized preoperative multiparametric magnetic resonance imaging, transrectal ultrasound examination, followed by prostate biopsy or radical prostatectomy. Complete clinical data including age, BMI, prostate specific antigen indicators, PI-RADS v2.1 scores, Gleason score and ISUP grading are collected from all eligible participants. Biomechanically constrained non-rigid spatial registration technique is applied to achieve precise alignment between preoperative multimodal images and postoperative digital pathological whole slide images using high-quality multicenter datasets. A transformer-based multimodal deep learning fusion model is developed to analyze correlations between macroscopic imaging features and microscopic pathological heterogeneity, thereby establishing an interpretable artificial intelligence framework for clinically significant prostate cancer prediction. Comprehensive model validation is conducted via internal cross-validation, external multicenter independent verification and international public datasets. Decision curve analysis and clinical impact curve are applied to assess clinical applicability. The model serves as an intelligent auxiliary tool to refine biopsy strategies, avoid redundant puncture and excessive treatment, and facilitate early precise diagnosis and risk stratification of prostate cancer.
Interventions
OTHERNo 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.
Sponsors
Guangxi Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
MALE
Age
40 Years to 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).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Contacts
CONTACTCaigou Shi, MD
PRINCIPAL_INVESTIGATORFubo Wang, MD
Guangxi Medical University
Outcome results
None listed