一种基于多参数 MRI 和 PSA 的新型具有临床意义的前列腺癌预测系统：P.Z.A.评分。

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

机构信息

Department of Urology, The First Affiliated Hospital of Soochow University, 899 pinghai road, Suzhou, 215006, China.

Department of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.

出版信息

BMC Cancer. 2023 Nov 23;23(1):1138. doi: 10.1186/s12885-023-11306-2.

DOI:10.1186/s12885-023-11306-2

PMID:37996859

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10668430/

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.

摘要

目的

本研究旨在建立和验证一种新的诊断模型，称为 P.Z.A. 评分，用于临床显著前列腺癌（csPCa）。

方法

记录了 956 名患者的人口统计学和临床特征。计算年龄、前列腺特异性抗原（PSA）、游离/总 PSA（f/tPSA）、PSA 密度（PSAD）、外周带体积比（PZ-ratio）和调整后的 PZ 区 PSA（aPSADPZ），并进行接受者操作特征（ROC）曲线分析。建立列线图，并通过校准曲线和 ROC 曲线下面积（AUC）验证新列线图的判别能力。通过决策曲线分析和临床影响曲线评估 P.Z.A. 评分的临床获益。还使用验证集对模型进行了外部验证。

结果

aPSADPZ、年龄、PSA、f/tPSA、PSAD 和 PZ-ratio 的 AUC 分别为 0.824、0.672、0.684、0.715、0.792 和 0.717。P.Z.A. 评分的最佳阈值为 0.41。列线图显示了预测 csPCa 发生的优异净获益和更好的整体校准。此外，P.Z.A. 评分预测的 csPCa 患者数量与高危阈值内实际的 csPCa 患者数量吻合良好。验证集为模型提供了更好的验证。