Naguib R N, Robinson M C, Neal D E, Hamdy F C
Department of Electrical and Electronic Engineering, University of Newcastle upon Tyne, UK.
Br J Cancer. 1998 Jul;78(2):246-50. doi: 10.1038/bjc.1998.472.
Prostate cancer is the second most common malignancy in men in the UK. The disease is unpredictable in its behaviour and, at present, no single investigative method allows clinicians to differentiate between tumours that will progress and those that will remain quiescent. There is an increasing need for novel means to predict prognosis and outcome of the disease. The aim of this study was to assess the value of artificial neural networks in predicting outcome in prostate cancer in comparison with statistical methods, using a combination of conventional and experimental biological markers. Forty-one patients with different stages and grades of prostate cancer undergoing a variety of treatments were analysed. Artificial neural networks were used as follows: eight input neurons consisting of six conventional factors (age, stage, bone scan findings, grade, serum PSA, treatment) and two experimental markers (immunostaining for bcl-2 and p53, which are both apoptosis-regulating genes). Twenty-one patients were used for training and 20 for testing. A total of 80% of the patients were correctly classified regarding outcome using the combination of factors. When both bcl-2 and p53 immunoreactivity were excluded from the analysis, correct prediction of the outcome was achieved in only 60% of the patients (P = 0.0032). This study was able to demonstrate the value of artificial neural networks in the analysis of prognostic markers in prostate cancer. In addition, the potential for using this technology to evaluate novel markers is highlighted. Further large-scale analyses are required to incorporate this methodology into routine clinical practice.
前列腺癌是英国男性中第二常见的恶性肿瘤。该疾病的行为难以预测，目前，没有单一的检测方法能让临床医生区分哪些肿瘤会进展，哪些会保持静止。预测该疾病预后和结果的新方法需求日益增加。本研究的目的是与统计方法相比，评估人工神经网络在预测前列腺癌结果方面的价值，使用传统和实验性生物标志物的组合。分析了41例接受各种治疗的不同分期和分级的前列腺癌患者。人工神经网络的使用如下：八个输入神经元，由六个传统因素（年龄、分期、骨扫描结果、分级、血清前列腺特异抗原、治疗）和两个实验性标志物（bcl-2和p53免疫染色，二者均为凋亡调节基因）组成。21例患者用于训练，20例用于测试。使用因素组合时，共有80%的患者在结果分类上正确。当从分析中排除bcl-2和p53免疫反应性时，仅60%的患者实现了结果的正确预测（P = 0.0032）。本研究能够证明人工神经网络在前列腺癌预后标志物分析中的价值。此外，突出了使用该技术评估新标志物的潜力。需要进一步的大规模分析以将该方法纳入常规临床实践。
