Batuello J T, Gamito E J, Crawford E D, Han M, Partin A W, McLeod D G, ODonnell C
Artificial Neural Networks in CaP Project and the, Denver, Colorado 80209, USA
Urology. 2001 Mar;57(3):481-5. doi: 10.1016/s0090-4295(00)01039-6.
To develop an artificial neural network (ANN) model to predict lymph node (LN) spread in men with clinically localized prostate cancer and to describe a clinically useful method for interpreting the ANN's output scores.
A simple, feed-forward ANN was trained and validated using clinical and pathologic data from two institutions (n = 6135 and n = 319). The clinical stage, biopsy Gleason sum, and prostate-specific antigen level were the input parameters and the presence or absence of LN spread was the output parameter. Patients with similar ANN outputs were grouped and assumed to be part of a cohort. The prevalence of LN spread for each of these patient cohorts was plotted against the range of ANN outputs to create a risk curve.
The area under the receiver operating characteristic curve for the first and second validation data sets was 0.81 and 0.77, respectively. At an ANN output cutoff of 0.3, the sensitivity achieved for each validation set was 63.8% and 44.4%; the specificity was 81.5% and 81.3%; the positive predictive value was 13.6% and 6.5%; and the negative predictive value was 98.0% and 98.1%, respectively. The risk curve showed a nearly linear increase (best fit R(2) = 0.972) in the prevalence of LN spread with increases in raw ANN output.
The ANN's performance on the two validation data sets suggests a role for ANNs in the accurate clinical staging of patients with prostate cancer. The risk curve provides a clinically useful tool that can be used to give patients a realistic assessment of their risk of LN spread.
开发一种人工神经网络（ANN）模型，以预测临床局限性前列腺癌男性患者的淋巴结（LN）转移情况，并描述一种临床上有用的方法来解释ANN的输出分数。
使用来自两个机构的临床和病理数据（n = 6135和n = 319）对一个简单的前馈ANN进行训练和验证。临床分期、活检Gleason评分总和以及前列腺特异性抗原水平为输入参数，LN转移的有无为输出参数。将具有相似ANN输出的患者分组，并假定为一个队列的一部分。将这些患者队列中每一组的LN转移患病率与ANN输出范围进行绘制，以创建风险曲线。
第一个和第二个验证数据集的受试者工作特征曲线下面积分别为0.81和0.77。在ANN输出截断值为0.3时，每个验证集的敏感性分别为63.8%和44.4%；特异性分别为81.5%和81.3%；阳性预测值分别为13.6%和6.5%；阴性预测值分别为98.0%和98.1%。风险曲线显示，随着原始ANN输出的增加，LN转移患病率几乎呈线性增加（最佳拟合R(2)=0.972）。
ANN在两个验证数据集上的表现表明其在前列腺癌患者准确临床分期中具有作用。风险曲线提供了一种临床上有用的工具，可用于让患者对其LN转移风险进行实际评估。
