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

Authors :
Zongxin Chen
Jun Zhang
Di Jin
Xuedong Wei
Feng Qiu
Ximing Wang
Xiaojun Zhao
Jinxian Pu
Jianquan Hou
Yuhua Huang
Chen Huang
Source :
BMC Cancer, Vol 23, Iss 1, Pp 1-9 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract 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.

Details

Language :
English
ISSN :
14712407
Volume :
23
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Cancer
Publication Type :
Academic Journal
Accession number :
edsdoj.5817e07e0d614643bbf536ad651793b7
Document Type :
article
Full Text :
https://doi.org/10.1186/s12885-023-11306-2