A Diagnostic Prediction Model for Prostate Cancer in Patients With PI-RADS Score 3

Completed

• Conditions: Prostate Cancer
• Interventions: Procedure: prostate biopsy

• Registration Number: NCT06507462
• Lead Sponsor: Anhui Provincial Hospital

Brief Summary

The goal of this observational study is to construct a predictive model for improving the diagnostic accuracy in patients with PI-RADS score of 3. The main aims of this study are:

* Construct a diagnostic model of patients with PI-RADS of score.

* Internal and external validation of the model.

* Decision curve analysis. The data of participants was collected retrospectively.

Detailed Description

For patients with a PI-RADS score of 3, the diagnosis of prostate cancer is still use prostate biopsy, but the detection rates of prostate cancer and clinically significant prostate cancer are approximately 30% and 15%. It can be seen that most patients with PI-RADS 3 undergo unnecessary prostate biopsy and bear the risk of complications such as urinary tract infection. This makes most patients with PI-RADS 3 choose to refuse invasive prostate biopsy. Although researchers are committed to exploring biomarkers with high sensitivity and specificity, the application of biomarkers alone often cannot achieve the expected results. At present, the guidelines have recommended the use of diagnostic prediction models to assess patients' prostate cancer risk. Doctors and patients use diagnostic models to assess the risk of prostate cancer before prostate biopsy. For patients with a low probability of cancer, biopsy can be temporarily avoided, which to a certain extent reduces the phenomenon of prostate cancer overdiagnosis. This study plans to work with multiple medical centers to conduct statistical analysis based on existing prostate cancer screening markers combined with patients' clinical data such as prostate volume, prostate-specific antigen density, apparent diffusion coefficient, PI-RADS score and postoperative Gleason score, and then construct a prostate cancer diagnostic model to improve the diagnostic accuracy of prostate cancer for patients with PI-RADS score of 3. This will be of great significance for improving the early diagnosis of patients with PI-RADS 3 and reducing unnecessary prostate puncture biopsies.

Study Timeline

• Study Start: 2021-01-01
• Study First Submitted: 2024-07-04
• Study First Submitted QC: 2024-07-11
• Study First Posted: 2024-07-18
• Primary Completion: 2024-10-31
• Study Completion: 2024-10-31
• Status Verified: 2025-02
• Last Update Submitted: 2025-02-10
• Last Update Posted: 2025-02-11

Recruitment & Eligibility

• Status: COMPLETED
• Sex: Male
• Target Recruitment: 460

Inclusion Criteria

1. Patients with clinically suspected prostate cancer (abnormal PSA level or DRE);
2. All patients have undergone mpMRI and have complete imaging data;
3. The PI-RADS score of patients was 3;
4. Prostate biopsy was performed and has clear pathological results.

Exclusion Criteria

1. The patient's serum tPSA is <4ng/ml or >100ng/ml;
2. Repeated prostate biopsy;
3. The patient's clinical, imaging, or pathological data are incomplete.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Patients with clinical suspicion of prostate cancer and have PI-RADS score of 3	prostate biopsy	Patients with clinical suspicion of prostate cancer (see inclusion criteria) and have PI-RADS score (results of mpMRI) of 3.

Primary Outcome Measures

Name	Time	Method
Multivariate logistic regression analyses and calculate the odds ratios (95% confidence interval ) of the clinical variables for clinically significant prostate cancer	through study completion, an average of 3 months	The clinically significant prostate cancer was defined as Gleason score ≥ 3+4

Secondary Outcome Measures

Name	Time	Method
Validation by calculating the C-statistics, drawing ROC curves (AUC values) and calibration curves.	through study completion, an average of 3 months	Evaluate the discrimination and calibration of the model constructed by logistic regression analyses

Trial Locations

Locations (3)

Department of Urology, The First Affiliated Hospital of USTC; 🇨🇳 Hefei, Anhui, China

Department of Urology, The First Affiliated Hospital of Wannan Medical College; 🇨🇳 Wuhu, Anhui, China

Department of Urology, The First Affiliated Hospital of Bengbu Medical University; 🇨🇳 Bengbu, Anhui, China