Chun Felix K-H, Graefen Markus, Briganti Alberto, Gallina Andrea, Hopp Julia, Kattan Michael W, Huland Hartwig, Karakiewicz Pierre I
Cancer Prognostics and Health Outcomes Unit, University of Montreal, Montreal, Quebec, Canada.
Eur Urol. 2007 May;51(5):1236-40; discussion 1241-3. doi: 10.1016/j.eururo.2006.07.021. Epub 2006 Aug 4.
Nomograms and artificial neural networks (ANNs) represent alternative methodologic approaches to predict the probability of prostate cancer on initial biopsy. We hypothesized that, in a head-to-head comparison, one of the approaches might demonstrate better accuracy and performance characteristics than the other.
A previously published nomogram, which relies on age, digital rectal examination, serum prostate-specific antigen (PSA), and percent-free PSA, and an ANN, which relies on the same predictors plus prostate volume, were applied to a cohort of 3980 men, who were subjected to multicore systematic prostate biopsy. The accuracy and the performance characteristics were compared between these two approaches.
The accuracy of the nomogram was 71% versus 67% for the ANN (p=0.0001). Graphical exploration of the performance characteristics demonstrated virtually perfect predictions for the nomogram. Conversely, the ANN underestimated the observed rate of prostate cancer.
A 4% increase in predictive accuracy implies that the use of the nomogram instead of the ANN will result in 40 additional patients who will be correctly classified between benign and cancer.
列线图和人工神经网络（ANN）是预测初次活检时前列腺癌概率的两种不同方法。我们假设，在直接比较中，其中一种方法可能比另一种方法具有更高的准确性和性能特征。
将一个先前发表的、基于年龄、直肠指检、血清前列腺特异性抗原（PSA）和游离PSA百分比的列线图，以及一个基于相同预测指标外加前列腺体积的人工神经网络，应用于3980名接受多芯系统性前列腺活检的男性队列。比较这两种方法的准确性和性能特征。
列线图的准确率为71%，而人工神经网络为67%（p = 0.0001）。对性能特征的图形探索显示列线图的预测几乎完美。相反，人工神经网络低估了观察到的前列腺癌发生率。
预测准确率提高4%意味着使用列线图而非人工神经网络将使另外40名患者在良性和癌症之间得到正确分类。
