JiaLe Sun1, ZhiYu Zhang1, Jun OuYang2. 1. Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China. 2. Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China. Electronic address: ouyangjun99@sina.com.
Abstract
OBJECTIVE: To determine whether Prostate Imaging-Reporting and Data System version 2 (PIRADS v2) and neutrophil-to-lymphocyte ratio(NLR) improve the detection of clinically significant prostate cancer(csCaP) in men with prostate-specific antigen (PSA) <10 ng/ml at first biopsy. METHODS: Univariable and multivariable binary logistic regression analysis were used to screen for independent risk factors of csCaP. The multivariable model based on the risk factors was to build the nomogram predicting csCaP and assessed by receiver operator characteristic curve analysis, calibration plot, and decision curve analysis. RESULTS: This retrospective study included 335 men with PSA < 10 ng/ml who underwent initial biopsy. A total of 78 (23.3%) men had csCaP. The nomogram was built based on the multivariable model including age, digital rectal examination, free prostate-specific antigen, PIRADS v2, and NLR. It had high area under the curve of 0.876 and was well calibrated in internal validation. Decision curve analysis also demonstrated that it would improve the prediction of csCaP. CONCLUSION: PIRADS v2 and NLR improve the detection of csCaP in men with PSA < 10 ng/ml at first biopsy. Due to lack of external validation, relatively small cohort and homogenous population, the study has several limitations. Despite of this, the nomogram based on our study is a promising tool for patients to understand their risk of csCaP and for urologists to make clinical decisions.
OBJECTIVE: To determine whether Prostate Imaging-Reporting and Data System version 2 (PIRADS v2) and neutrophil-to-lymphocyte ratio(NLR) improve the detection of clinically significant prostate cancer(csCaP) in men with prostate-specific antigen (PSA) <10 ng/ml at first biopsy. METHODS: Univariable and multivariable binary logistic regression analysis were used to screen for independent risk factors of csCaP. The multivariable model based on the risk factors was to build the nomogram predicting csCaP and assessed by receiver operator characteristic curve analysis, calibration plot, and decision curve analysis. RESULTS: This retrospective study included 335 men with PSA < 10 ng/ml who underwent initial biopsy. A total of 78 (23.3%) men had csCaP. The nomogram was built based on the multivariable model including age, digital rectal examination, free prostate-specific antigen, PIRADS v2, and NLR. It had high area under the curve of 0.876 and was well calibrated in internal validation. Decision curve analysis also demonstrated that it would improve the prediction of csCaP. CONCLUSION: PIRADS v2 and NLR improve the detection of csCaP in men with PSA < 10 ng/ml at first biopsy. Due to lack of external validation, relatively small cohort and homogenous population, the study has several limitations. Despite of this, the nomogram based on our study is a promising tool for patients to understand their risk of csCaP and for urologists to make clinical decisions.