New model of PIRADS and adjusted prostate-specific antigen density of peripheral zone improves the detection rate of initial prostate biopsy: a diagnostic study

Huang, Chen; Cai, Zongqiang; Qiu, Feng; Pu, Jinxian; Xi, Qilin; Wei, Xuedong; Wang, Ximing; Zhao, Xiaojun; Guo, Linchuan; Hou, Jianquan; Huang, Yuhua

doi:10.4103/aja202218

Cited by 2 publications

(1 citation statement)

References 22 publications

(25 reference statements)

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“…8 9 In recent years, new clinical predictive models for the diagnosis of PCa, including information from multiparametric magnetic resonance imaging (mpMRI), have been constructed. 10 11 12 Although these models have a better diagnostic performance, their efficiency in the Chinese population still needs to be further assessed.…”

Section: Introductionmentioning

confidence: 99%

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data

Wang,

Yuan,

Liu

et al. 2023

Asian Journal of Andrology

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The overdiagnosis of prostate cancer (PCa) caused by nonspecific elevation serum prostate-specific antigen (PSA) and the overtreatment of indolent PCa have become a global problem that needs to be solved urgently. We aimed to construct a prediction model and provide a risk stratification system to reduce unnecessary biopsies. In this retrospective study, clinical data of 1807 patients from three Chinese hospitals were used. The final model was built using stepwise logistic regression analysis. The apparent performance of the model was assessed by receiver operating characteristic curves, calibration plots, and decision curve analysis. Finally, a risk stratification system of clinically significant prostate cancer (csPCa) was created, and diagnosis-free survival analyses were performed. Following multivariable screening and evaluation of the diagnostic performances, a final diagnostic model comprised of the PSA density and Prostate Imaging-Reporting and Data System (PI-RADS) score was established. Model validation in the development cohort and two external cohorts showed excellent discrimination and calibration. Finally, we created a risk stratification system using risk thresholds of 0.05 and 0.60 as the cut-off values. The follow-up results indicated that the diagnosis-free survival rate for csPCa at 12 months and 24 months postoperatively was 99.7% and 99.4%, respectively, for patients with a risk threshold below 0.05 after the initial negative prostate biopsy, which was significantly better than patients with higher risk. Our diagnostic model and risk stratification system can achieve a personalized risk calculation of csPCa. It provides a standardized tool for Chinese patients and physicians when considering the necessity of prostate biopsy.

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Section: Introductionmentioning

confidence: 99%

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data

Wang,

Yuan,

Liu

et al. 2023

Asian Journal of Andrology

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A novel clinically significant prostate cancer prediction system with multiparametric MRI and PSA: P.Z.A. score

Chen,

Zhang,

Jin

et al. 2023

BMC Cancer

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Purpose This study aims to establish and validate a new diagnosis model called P.Z.A. score for clinically significant prostate cancer (csPCa). Methods The demographic and clinical characteristics of 956 patients were recorded. Age, prostate-specific antigen (PSA), free/total PSA (f/tPSA), PSA density (PSAD), peripheral zone volume ratio (PZ-ratio), and adjusted PSAD of PZ (aPSADPZ) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The nomogram was established, and discrimination abilities of the new nomogram were verified with a calibration curve and area under the ROC curve (AUC). The clinical benefits of P.Z.A. score were evaluated by decision curve analysis and clinical impact curves. External validation of the model using the validation set was also performed. Results The AUCs of aPSADPZ, age, PSA, f/tPSA, PSAD and PZ-ratio were 0.824, 0.672, 0.684, 0.715, 0.792 and 0.717, respectively. The optimal threshold of P.Z.A. score was 0.41. The nomogram displayed excellent net benefit and better overall calibration for predicting the occurrence of csPCa. In addition, the number of patients with csPCa predicted by P.Z.A. score was in good agreement with the actual number of patients with csPCa in the high-risk threshold. The validation set provided better validation of the model. Conclusion P.Z.A. score (including PIRADS(P), aPSADPZ(Z) and age(A)) can increase the detection rate of csPCa, which may decrease the risk of misdiagnosis and reduce the number of unnecessary biopsies. P.Z.A. score contains data that is easy to obtain and is worthy of clinical replication.

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New model of PIRADS and adjusted prostate-specific antigen density of peripheral zone improves the detection rate of initial prostate biopsy: a diagnostic study

Cited by 2 publications

References 22 publications

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data

A novel clinically significant prostate cancer prediction system with multiparametric MRI and PSA: P.Z.A. score

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