A multicenter retrospective study on evaluation of predicative factors for positive biopsy of prostate cancer in real-world setting. (2nd September 2021)

• Record Type: Journal Article
• Authors: Xu, Ben; Li, Gonghui; Kong, Chuize; Chen, Ming; Hu, Bin; Jiang, Qing; Li, Ningchen; Zhou, Liqun
• Is Part Of: Current medical research and opinion. Volume 37:Number 9(2021)
• Page Start: 1617
• Page End: 1625
• Publication Date: 2021-09-02
• Subjects: Prostate cancer -- prostate biopsy -- prostate specific antigen -- risk prediction -- MP-MRI; Clinical medicine -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1080/03007995.2021.1949270
• Languages: English
• ISSNs: 0300-7995

Abstract

Abstract: Objective: This study aimed to evaluate the predictors for positive biopsy in prostate cancer (PCa) patients and develop a risk-stratification score model for positive biopsy rate in patients with prostate specific antigen (PSA) in the gray zone. Methods: In this retrospective, multicenter, real-world study, Chinese patients receiving prostate biopsy for the first time were included. The study evaluated the positive biopsy rate, predictors for positive biopsy and a risk prediction model for PSA 4–10 ng/mL PCa was developed. The univariate and multivariate logistic regression analyses were used to identify the risk factors. Results: A total of 2426 patients were included in the study. The biopsy positive rate was 47.57%, 25.77%, and 60.57% among overall patients, total PSA (t-PSA) 4–10 ng/mL patients, and PSA > 10 ng/mL patients respectively. Elderly age 60–74, ≥75, multi parametric magnetic resonance imaging (MP-MRI), pre-operative PSA > 10 and PSA density (PSAD) significantly increased the positive rate in overall population, and elderly age, MP-MRI, positive digital rectal examination and f-PSA were significant predictors for positive biopsy in PSA 4–10 ng/mL population. A risk prediction model for positive biopsy rate in patients with PSA in the gray zone was developed. Area under curve (AUC) was associated with low accuracy for all the variables used such as tPSA (0.53), PSAD (0.57), frequency of puncture (0.53) and MP-MRI (0.64) in prediction of biopsy positive rate. Conclusion: Our study evaluated the significant predicative factors for positive biopsy and the PCa risk prediction model developed might help Clinicians to avoid unnecessary biopsy in patients with PSA in gray zone.