New model of PIRADS and adjusted prostatespecific antigen density of peripheral zone improves the detection rate of initial prostate biopsy: a diagnostic study.
- Author:
Chen HUANG
1
;
Zong-Qiang CAI
1
;
Feng QIU
1
;
Jin-Xian PU
1
;
Qi-Lin XI
1
;
Xue-Dong WEI
1
;
Xi-Ming WANG
2
;
Xiao-Jun ZHAO
1
;
Lin-Chuan GUO
3
;
Jian-Quan HOU
1
;
Yu-Hua HUANG
1
Author Information
- Publication Type:Journal Article
- Keywords: Prostate Imaging Reporting and Data System; adjusted prostate-specific antigen density of peripheral zone; biopsy; diagnosis; prostate cancer
- MeSH: Male; Humans; Prostate/pathology*; Prostate-Specific Antigen/analysis*; Prostatic Neoplasms/diagnostic imaging*; Biopsy; Nomograms; Retrospective Studies
- From: Asian Journal of Andrology 2023;25(1):126-131
- CountryChina
- Language:English
- Abstract: This study explored a new model of Prostate Imaging Reporting and Data System (PIRADS) and adjusted prostate-specific antigen density of peripheral zone (aPSADPZ) for predicting the occurrence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The demographic and clinical characteristics of 853 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), PSAD of peripheral zone (PSADPZ), aPSADPZ, and peripheral zone volume ratio (PZ-ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with the calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves. The AUCs of PSA, PSAD, PSADPZ, aPSADPZ, and PZ-ratio were 0.669, 0.762, 0.659, 0.812, and 0.748 for PCa diagnosis, while 0.713, 0.788, 0.694, 0.828, and 0.735 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of PCa or csPCa. The new model significantly improved the diagnostic accuracy of PCa (0.945 vs 0.830, P < 0.01) and csPCa (0.937 vs 0.845, P < 0.01) compared with the base model. In addition, the number of patients with PCa and csPCa predicted by the new model was in good agreement with the actual number of patients with PCa and csPCa in high-risk threshold. This study demonstrates that aPSADPZ has a higher predictive accuracy for PCa diagnosis than the conventional indicators. Combining aPSADPZ with PIRADS can improve PCa diagnosis and avoid unnecessary biopsies.
