Application Of Back-propagation Neural Network For Prognostic Analysis Of Colorectal Cancer

Objectives:To explore prognostic influence factors of colorectal cancer; BP neural network was used to predict survival time of patients with colorectal cancer; To cpmpare the forecasting performance between BP neutal network and Logistic Regression in prognostic analysis of colorectal cancer.Method:We adopted consulting disease-cases method to collect data; SP immunohistochemical method was used to detect gene expression products; The Cox proportional hazards regression model was used to detect prognostic influence factors of colorectal cancer; We could evaluate the forecasting performance of BP neural network by comparing the actual survival time and the predicting survival time; Through the comparison of area under the ROC curve between BP neural network and Logistic Regression, we could judge the forecasting performance between the both methods in prognostic analysis of colorectal cancer.Results:Using Cox multivariate analysis,the main prognostic influence factors of colorectal cancer are Dukes stage (β=1.197 P<0.001,RR=3.309), P16(β=-0.805, P<0.001,RR=0.447), MMP-9(β=0.459, P=0.017,RR=1.582), P53(β=1.799, P<0.001,RR=6.042) nm23-H1(β=-0.740, P<0.001,RR=0.477); The diffence between the actual survival time and the predicting survival time by BP neural network is not statistically significant (t= 0.996, P= 0.327, R2= 0.663); BP nueral network is superior to Logistic regression in the both model-fit and prediction. In the model-fit, between the comparison of the area under the ROC curve, Z-1.75, P= 0.04006, the difference is statistically significant according to (?)=0.05. In the prediction, the accuracy rate by BP nueral network is 92.5%(37/40),but the correct rate of Logistic regression is 82.5%(33/40).Conclusions:Dukes stage,P16,MMP-9,P53,nm23-H1 are the main prognostic influence factors of colorectal cancer, They can help clinicians to judge the prognosis of the patients and choose the right treatment options. The BP neural network can predict survival time of patients with colorectal cancer. BP neural network offers a new way to predict the survival time,besides, BP neural network is superior to Logistic regression in the both model-fit and prediction of colorectal cancer. The Medical phenomenon is very complex. Besides,the relation between dependent variable and independent variable may be non-linear or the interaction between variables may exist,and ao on. At the same time, the traditional statistical methods have certain limitations. BP neural network has fewer limits to data and has a good non-linear processing. So it can be applied and promoted in the medical field.