Ronco A L, Fernández R
National Cancer Registry, Montevideo, Uruguay.
Ultrasound Med Biol. 1999 Jun;25(5):729-33. doi: 10.1016/s0301-5629(99)00011-3.
To improve ultrasonographic diagnosis of prostate cancer, the authors evaluated the performance of an optimized backpropagation artificial neural network (ANN) in predicting an outcome (cancer-not cancer) from recorded information on patients admitted for transrectal ultrasonography (TRUS) performed in our Center. A total of 442 cases with complete information were selected for the study. After preselecting 17 variables (age, PSA, previous clinical diagnosis, and 14 ultrasonographic ones) through univariate analysis, a randomly selected subset of data (50%) was used to train ANNs, and the other subset (50%) was used to test the different models. The ANN achieved up to 81.82% of positive predictive value and up to 96.95% of negative predictive value vs. 67.18% and 90.97%, respectively, when compared with those obtained with logistic regression. Results and possible future practical applications are further discussed.
为提高前列腺癌的超声诊断水平，作者评估了优化后的反向传播人工神经网络（ANN）在根据本中心经直肠超声检查（TRUS）记录的患者信息预测结果（癌症或非癌症）方面的性能。本研究共选取了442例信息完整的病例。通过单因素分析预先选择17个变量（年龄、前列腺特异性抗原、既往临床诊断以及14个超声变量）后，随机选取的数据子集（50%）用于训练人工神经网络，另一个子集（50%）用于测试不同模型。与逻辑回归相比，人工神经网络的阳性预测值高达81.82%，阴性预测值高达96.95%，而逻辑回归的阳性预测值和阴性预测值分别为67.18%和90.97%。文中进一步讨论了研究结果及未来可能的实际应用。
