Zlotta Alexandre R, Remzi Mesut, Snow Peter B, Schulman Claude C, Marberger Michael, Djavan Bob
Department of Urology, Erasme Hospital, University Clinics of Brussels, Brussels, Belgium.
J Urol. 2003 May;169(5):1724-8. doi: 10.1097/01.ju.0000062548.28015.f6.
An artificial neural network was developed to improve the prediction of pathological stage before radical prostatectomy based on variables available at biopsy and clinical parameters.
We used the prospectively accrued European prostate cancer detection data base to train an artificial neural network to predict pathological stage in 200 men with serum prostate specific antigen (PSA) 10 ng./ml. or less who underwent radical prostatectomy. Variables included in the artificial neural network were patient age, serum PSA, free-to-total PSA ratio, PSA velocity, transrectal ultrasound calculated total and transition zone volumes with their associated PSA parameters (transition zone PSA density and PSA density), digital rectal examination and Gleason score on biopsy. Two multilayer perceptron neural networks were trained on the remaining variables. Data on the 200 patients were divided randomly into a training set, a test set and a validation or prospective set.
Overall classification accuracy of the artificial neural network was 92.7% and 84.2% for organ confined and advanced prostate cancer staging, respectively. For preoperatively predicting local versus advanced stage the area under the ROC curve for the artificial neural network was significantly larger (0.91) compared with logistic regression analysis (0.83), Gleason score (0.69), PSA density (0.68), prostate transition zone volume (0.63) and serum PSA (0.62) (all p <0.01).
The artificial neural network outperformed logistic regression analysis and correctly predicted pathological stage in more than 90% of the validation patients with serum PSA 10 ng./ml. or less based on clinical, biochemical and biopsy data. In the future artificial neural networks may represent a significant step for improved staging of prostate cancer when counseling patients referred for radical prostatectomy or other curative treatments.
开发一种人工神经网络，以基于活检时可用的变量和临床参数来改善根治性前列腺切除术之前病理分期的预测。
我们使用前瞻性收集的欧洲前列腺癌检测数据库来训练人工神经网络，以预测200例血清前列腺特异性抗原（PSA）为10 ng/ml或更低且接受根治性前列腺切除术的男性的病理分期。人工神经网络中包含的变量有患者年龄、血清PSA、游离PSA与总PSA比值、PSA速率、经直肠超声计算的总体积和移行区体积及其相关的PSA参数（移行区PSA密度和PSA密度）、直肠指诊以及活检时的Gleason评分。在其余变量上训练了两个多层感知器神经网络。将这200例患者的数据随机分为训练集、测试集和验证集或前瞻性集。
对于局限于器官的前列腺癌分期和进展期前列腺癌分期，人工神经网络的总体分类准确率分别为92.7%和84.2%。对于术前预测局部与进展期，人工神经网络的ROC曲线下面积（0.91）显著大于逻辑回归分析（0.83）、Gleason评分（0.69）、PSA密度（0.68）、前列腺移行区体积（0.63）和血清PSA（0.62）（所有p<0.01）。
人工神经网络的表现优于逻辑回归分析，并基于临床、生化和活检数据在90%以上血清PSA为10 ng/ml或更低的验证患者中正确预测了病理分期。未来，在为接受根治性前列腺切除术或其他根治性治疗的患者提供咨询时，人工神经网络可能代表着前列腺癌分期改善的重要一步。
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