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Multicenter Study
A multicenter retrospective study on evaluation of predicative factors for positive biopsy of prostate cancer in real-world setting.
- Ben Xu, Gonghui Li, Chuize Kong, Ming Chen, Bin Hu, Qing Jiang, Ningchen Li, and Liqun Zhou.
- Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Center, Beijing, China.
- Curr Med Res Opin. 2021 Sep 1; 37 (9): 1617-1625.
ObjectiveThis 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.MethodsIn 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.ResultsA 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.ConclusionOur 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.
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