组织芯片技术与人工智能神经网络在大肠肿瘤诊断中的应用  

Combined application of tissue microarray technique and artificial neural networks in colon tumor diagnosis

作　　者：孟潘庆[1] 贾玉生[1] 郑树[2] 余捷凯[2] 

机构地区：[1]泰安市中心医院肿瘤外科,山东泰安271000 [2]浙江大学医学院附属第二医院肿瘤研究所,浙江杭州310009

出　　处：《中华肿瘤防治杂志》2007年第17期1324-1327,共4页Chinese Journal of Cancer Prevention and Treatment

摘　　要：目的:构建组织原位检测指标预测诊断大肠肿瘤的人工智能神经网络(ANN)模型,探讨组织芯片技术与ANN结合应用的可行性。方法:应用组织芯片技术检测ST13等8种组织原位检测指标在大肠肿瘤演进过程各阶段的表达,同时采用ANN构建相应的诊断模型。结果:采用Matlab6.5软件中提供的Kruskal-wal-lisH秩和检验函数,对这8种指标在正常大肠组织、大肠腺瘤和大肠癌组织中的阳性表达差异进行统计学检验,其中ST13、Bcl-2、Survivin和HSF1 mRNA的表达,差异有统计学意义,P<0.01;将8种指标随机组合,分别构建相应的ANN诊断模型,评价其各自的诊断效率,发现ST13、Bcl-2、Survivin与HSF1 mRNA组合的ANN-BP模型预测准确率最高,其对正常大肠组织、大肠腺瘤和大肠癌训练集的预测准确率分别高达92.895%、94.163%和92.013%,对该ANN-BP网络诊断模型的盲法测试结果也分别高达85.714%、79.412%和72%。结论:组织芯片技术与ANN相结合,可以大大提高组织原位检测指标对大肠肿瘤的预测诊断效率。OBJECTIVE： To establish the colon tumour diag-nostic models of 8 tumor related markers in tissue in situ by artifi-cial neural network （ANN） and evaluate the feasibility of combined application of tissue microarray （TMA） technique and ANN. METHODS： Sever kinds of tumor related proteins （ST13 and so on） and HSF1 mRNA were detected by means of TMA technique, and the diagnostic models were established by ANN-BP. RESULTS： By means of Kruskal-wallis H test available in Matlab 6.5, the expres-sion of every one of 8 tumour related markers （proteins/mRNA） was e-valuated in healthy colon, colon adenoma and colon carcinoma, respec- tively, and the result showed that the expression in these 3 tissues of ST13, Bcl-2, Survivin and HSF1 mRNA were significantly different, P〈0. 01 ; then the random assortments of the 8 tumour relate markers were used to establish different diagnostic models, whose diagnostic sensibilities were evaluated by training and blinding test sets respective-ly. The diagnostic model established by the group of ST13, Bcl-2, Sur-vivin and HSF1 mRNA was found to be the best one among all the ran-dom groups. Its training set predicted veracities were 92.895% for healthy colon tissue, 94.163% for colon adenoma, and 92.013% for colon carcinoma, meanwhile its blinding test set predicted veracities were 85.714%, 79.412% and 72%, respectively. CONCLUSION： Combined application of TMA technique and ANN can enhance the di-agnostic efficiency of the tumor related markers in colon tissue in situ dramatically.

关 键 词：结肠直肠肿瘤 微阵列分析 神经网络(计算机) 

分 类 号：R735.3[医药卫生—肿瘤]