对PSA水平为0 - 10 ng/mL的土耳其男性前列腺癌预测方法的比较。

Comparison of methods for prediction of prostate cancer in Turkish men with PSA levels of 0-10 ng/mL.

作者信息

Gulkesen K H, Koksal I T, Bilge U, Saka O

机构信息

Akdeniz University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Antalya, Turkey.

出版信息

J BUON. 2010 Jul-Sep;15(3):537-42.

PMID:20941824

Abstract

PURPOSE

Several concepts to improve the diagnostic accuracy of prostate specific antigen (PSA) for prediction of prostate cancer have been studied. The aim of this study was to examine and compare the methods used for improving the diagnostic accuracy of PSA in a country with low incidence of prostate cancer.

METHODS

997 patients with prostate biopsy were included into study. Predictive models using PSA, PSA density (PSAD), free PSA/total PSA (f/tPSA), binary logistic regression (LR) analysis, artificial neural networks (ANNs), and decision trees (DTs) have been developed. For LR, ANNs and DTs, a validation group consisting of 241 cases was reserved.

RESULTS

193 (19%) biopsies out of 997 showed prostatic cancer. Median PSAD in patients with malignant and benign lesions were 0.21 and 0.16, respectively (p<0.001). According to 25% f/tPSA cut-off level, 18.4% of the patients with PSA<25% and 16.0% of the patients with PSA>25% had prostate cancer (p=0.423). Receiver operating characteristics (ROC) area under the curve (AUC) values for PSA, PSA density, f/tPSA, LR, ANNs, and DTs were 0.587, 0.625, 0.560, 0.678, 0.644, and 0.698, respectively. ROC AUCs in the validation group for LR, ANNs and DTs were 0.717, 0.516 and 0.629 respectively.

CONCLUSIONS

For cases with f/tPSA<25%, no increased probability for prostatic carcinoma was observed. Multivariate models have higher AUCs than PSA, PSAD or f/tPSA. LR, DTs and ANNs showed similar results, however application of ANNs to the validation group produced a significantly lower AUC, limiting the value of ANNs in this situation.

摘要

目的

已对多种提高前列腺特异性抗原（PSA）预测前列腺癌诊断准确性的概念进行了研究。本研究的目的是在前列腺癌低发国家检验和比较用于提高PSA诊断准确性的方法。

方法

997例接受前列腺活检的患者纳入研究。已开发出使用PSA、PSA密度（PSAD）、游离PSA/总PSA（f/tPSA）、二元逻辑回归（LR）分析、人工神经网络（ANN）和决策树（DT）的预测模型。对于LR、ANN和DT，保留了一个由241例病例组成的验证组。

结果

997例活检中有193例（19%）显示为前列腺癌。恶性和良性病变患者的PSAD中位数分别为0.21和0.16（p<0.001）。根据25%的f/tPSA临界值水平，PSA<25%的患者中有18.4%以及PSA>25%的患者中有16.0%患有前列腺癌（p=0.423）。PSA、PSA密度、f/tPSA、LR、ANN和DT的曲线下面积（AUC）值分别为0.587、0.625、0.560、0.678、0.644和0.698。验证组中LR、ANN和DT的ROC AUC分别为0.717、0.516和0.629。