Lai Kuang-Chi, Chiang Hung-Chih, Chen Wen-Chi, Tsai Fuu-Jen, Jeng Long-Bin
Department of Surgery, China Medical University Hospital, No.2 Yuh-Der Road, Taichung, Taiwan.
Hepatogastroenterology. 2008 Sep-Oct;55(86-87):1859-63.
BACKGROUND/AIMS: Primary gastric cancer is a multi-factorial disease comprising many low-penetrance clinicopathological factors and genetic predisposition. Preoperative prediction of tumor staging can be made by artificial neural network (ANN)-based study using clinic-pathological datasets and genetic susceptibility testing.
A hospital-based, retrospective, randomized control study was conducted for 121 patients who had recently developed primary gastric cancer. Clinical data and pathological findings were collected and genetic polymorphisms of candidate genes were evaluated. ANN-based study was conducted to predict tumor staging and to evaluate the relative impact of each factor.
The best training method was the Quick method, which had an accuracy of 81.82%. The most important factors associated with tumor staging were age and polymorphisms of genes p21, IL-1, IL-4 and p53.
Analysis of genetic polymorphisms of candidate genes by ANN using clinicopathological datasets is a promising method for predicting human gastric cancer staging. This strategy can identify the important genetic, clinical and pathological factors, determine their relative impact, and aid in the development of a prognostic staging system that is useful in individualized patient care.
背景/目的：原发性胃癌是一种多因素疾病，包含许多低外显率的临床病理因素和遗传易感性。术前肿瘤分期的预测可通过基于人工神经网络（ANN）的研究，利用临床病理数据集和基因易感性检测来进行。
对121例近期诊断为原发性胃癌的患者进行了一项基于医院的回顾性随机对照研究。收集临床数据和病理结果，并评估候选基因的基因多态性。进行基于人工神经网络的研究以预测肿瘤分期并评估各因素的相对影响。
最佳训练方法是快速方法，其准确率为81.82%。与肿瘤分期相关的最重要因素是年龄以及基因p21、白细胞介素-1、白细胞介素-4和p53的多态性。
利用临床病理数据集通过人工神经网络分析候选基因的基因多态性是预测人类胃癌分期的一种有前景的方法。该策略可以识别重要的遗传、临床和病理因素，确定它们的相对影响，并有助于开发一种对个体化患者护理有用的预后分期系统。
