Clinical trial NCT06507462: A Diagnostic Prediction Model for Prostate Cancer in Patients With PI-RADS Score 3
Official title: Construction and Validation of a Diagnostic Prediction Model for Prostate Cancer in Patients With PI-RADS Score 3: a Multicenter, Retrospective Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Prostate Cancer
Keywords: Prostate cancer
Predictive model
Diagnosis
Prostate Imaging-Reporting and Data System
Lead sponsor: Anhui Provincial Hospital
Sponsor class: OTHER_GOV
First posted: 2024-07-18
Enrollment: 460
Sex: MALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: This is a multicenter retrospective study. All enrolled patients were clinically suspected of prostate cancer (abnormal serum PSA level or DRE), had an indication for prostate biopsy and eventually underwent prostate biopsy. Before biospy, patients also need to complete mpMRI examination, the examination sequence includes at least multi-plane T2WI and multi-b value DWI, and the final patient PI-RADS score needs to be 3.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Multivariate logistic regression analyses and calculate the odds ratios (95% confidence interval ) of the clinical variables for clinically significant prostate cancer
timeFrame: through study completion, an average of 3 months
Secondary outcomes:
measure: Validation by calculating the C-statistics, drawing ROC curves (AUC values) and calibration curves.
timeFrame: through study completion, an average of 3 months
Other outcomes:
Locations:
city: Bengbu
country: China
facility: Department of Urology, The First Affiliated Hospital of Bengbu Medical University
state: Anhui
city: Hefei
country: China
facility: Department of Urology, The First Affiliated Hospital of USTC
state: Anhui
city: Wuhu
country: China
facility: Department of Urology, The First Affiliated Hospital of Wannan Medical College
state: Anhui
Study officials:
affiliation: The First Affiliated Hospital of USTC
name: Jun Xiao, Pro.
role: PRINCIPAL_INVESTIGATOR
References: