首页> 外文期刊>Urology >An artificial neural network to predict the outcome of repeat prostate biopsies.
【24h】

An artificial neural network to predict the outcome of repeat prostate biopsies.

机译:人工神经网络可预测重复进行前列腺活检的结果。

获取原文
获取原文并翻译 | 示例
           

摘要

OBJECTIVES: To develop an advanced artificial neural network (ANN) to predict the presence of prostate cancer (PCa) and to predict the outcome of repeat prostate biopsies. The predictive accuracy was compared with the accuracy obtained using standard cutoffs for the free/total (f/t) prostate-specific antigen (PSA) ratio, PSA density (PSAD), PSA density of the transition zone (PSA-TZ), and the total and transition zone volumes. Clinical and biochemical diagnostic tests have been shown to improve PCa detection. When these tests are combined using an ANN, significant increases in specificity at high sensitivity are observed. METHODS: The Vienna-based multicenter European referral database for early PCa detection of 820 men with a PSA level between 4 and 10 ng/mL was used. The presence of PCa was determined using transrectal ultrasound-guided octant needle repeat biopsy. Variables in the database consisted of age, PSA, f/t PSA ratio, digital rectal examination findings, PSA velocity, and the transrectal ultrasound-guided variables of prostate volume, transition zone volume, PSAD, and PSA-TZ. The ANN used in the analysis was an advanced multilayer perceptron selected for accuracy by a genetic algorithm. RESULTS: The repeat biopsy PCa detection rate was 10% (n = 83). At 95% sensitivity, the specificity for ANN was 68% compared with 54%, 33.5%, 21.4%, 14.7%, and 8.3% for multivariate logistic regression analysis, f/t PSA ratio, PSA-TZ, PSAD, and total PSA, respectively. The ANN reduced unnecessary repeat biopsies by 68% in this study. The area under the curve was 83% for the ANN versus 79%, 74.5%, 69.1%, 61.8%, and 60.5% for multivariate analysis, f/t PSA ratio, PSA-TZ, PSAD, and total PSA, respectively. CONCLUSIONS: The current ANN found a strong pattern predictive of PCa in patients with a negative initial biopsy. By combining the individual clinical and biochemical markers into the ANN, 68% specificity at 95% sensitivity was achieved. The ANN allows more accurate and individual counseling of patients with a negative initial biopsy.
机译:目的:开发先进的人工神经网络(ANN)来预测前列腺癌(PCa)的存在并预测重复进行前列腺活检的结果。将预测准确性与使用标准阈值获得的准确性进行比较,以自由/总(f / t)前列腺特异性抗原(PSA)比率,PSA密度(PSAD),过渡区的PSA密度(PSA-TZ)和总和过渡区域的体积。临床和生化诊断测试已显示可改善PCa检测。当使用人工神经网络将这些测试结合使用时,可以观察到高灵敏度下特异性的显着提高。方法:使用基于维也纳的欧洲多中心转诊数据库对PSA水平在4至10 ng / mL之间的820名男性进行早期PCa检测。使用经直肠超声引导的八分形针重复活检确定PCa的存在。数据库中的变量包括年龄,PSA,f / t PSA比,直肠指检发现,PSA速度以及经直肠超声引导的前列腺体积,过渡带体积,PSAD和PSA-TZ变量。分析中使用的人工神经网络是一种先进的多层感知器,通过遗传算法选择了其准确性。结果:重复活检PCa检出率为10%(n = 83)。灵敏度为95%时,ANN的特异性为68%,而多元logistic回归分析,f / t PSA比,PSA-TZ,PSAD和总PSA的特异性分别为54%,33.5%,21.4%,14.7%和8.3%。 , 分别。在这项研究中,人工神经网络使不必要的重复活检减少了68%。 ANN的曲线下面积为83%,而多元分析的f / t PSA比率,PSA-TZ,PSAD和总PSA分别为79%,74.5%,69.1%,61.8%和60.5%。结论:目前的人工神经网络发现初始活检阴性的患者对PCa的预测很强。通过将各个临床和生化标记物组合到人工神经网络中,在95%的灵敏度下可获得68%的特异性。 ANN可以为初次活检阴性的患者提供更准确,更个性化的咨询。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号