Stephan Carsten, Xu Chuanliang, Cammann Henning, Graefen Markus, Haese Alexander, Huland Hartwig, Semjonow Axel, Diamandis Eleftherios P, Remzi Mesut, Djavan Bob, Wildhagen Mark F, Blijenberg Bert G, Finne Patrik, Stenman Ulf-Hakan, Jung Klaus, Meyer Hellmuth-Alexander
Department of Urology, Charité-Universitätsmedizin Berlin, CCM, Berlin, Germany.
World J Urol. 2007 Mar;25(1):95-103. doi: 10.1007/s00345-006-0132-9. Epub 2007 Feb 28.
Use of percent free PSA (%fPSA) and artificial neural networks (ANNs) can eliminate unnecessary prostate biopsies. In a total of 4,480 patients from five centers with PSA concentrations in the range of 2-10 ng/ml an IMMULITE PSA-based ANN (iANN) was compared with other PSA assay-adapted ANNs (nANNs) to investigate the impact of different PSA assays. ANN data were generated with PSA, fPSA (assays from Abbott, Beckman, DPC, Roche or Wallac), age, prostate volume, and DRE status. In 15 different ROC analyses, the area under the curve (AUC) in the PSA ranges 2-4, 2-10, and 4-10 ng/ml for the nANN was always significantly larger than the AUC for %fPSA or PSA. The nANN and logistic regression models mostly also performed better than the iANN. Therefore, for each patient population, PSA assay-specific ANNs should be used to optimize the ANN outcome in order to reduce the number of unnecessary biopsies.
使用游离前列腺特异性抗原百分比(%fPSA)和人工神经网络(ANNs)可以避免不必要的前列腺活检。在来自五个中心的总共4480名前列腺特异性抗原(PSA)浓度在2至10 ng/ml之间的患者中,将基于免疫发光法PSA的人工神经网络(iANN)与其他适应PSA检测的人工神经网络(nANNs)进行比较,以研究不同PSA检测方法的影响。人工神经网络数据由PSA、fPSA(来自雅培、贝克曼、DPC、罗氏或瓦里安的检测方法)、年龄、前列腺体积和直肠指检结果生成。在15项不同的受试者工作特征(ROC)分析中,对于nANN,在PSA范围为2至4、2至10和4至10 ng/ml时,曲线下面积(AUC)始终显著大于%fPSA或PSA的AUC。nANN和逻辑回归模型大多也比iANN表现更好。因此,对于每个患者群体,应使用特定于PSA检测方法的人工神经网络来优化人工神经网络的结果,以减少不必要活检的数量。