Artificial Neural Networking Model for the Prediction of Early Occlusion of Bilateral Plastic Stent Placement for Inoperable Hilar Cholangiocarcinoma

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Abstract

Background:

This study aimed to determine whether the back-propagation artificial neural network (BP-ANN) model could be constructed to accurately in predicting early occlusion of bilateral plastic stent placement for inoperable hilar cholangiocarcinoma (HCA).

Methods:

A total of 288 patients from the An Hui provincial Hospital were randomly divided into the training cohort (80%) and the internal testing cohort (20%). The predictive accuracy of the BP-ANN for predicting early occlusion of bilateral plastic stent placement of inoperable HCA was measured by the area under the curve (AUC) on receiver operating characteristic (ROC) curve analysis. The results were compared with those obtained using the conventional multivariate logistic regression analysis.

Results:

Multivariate analysis revealed that cancer stage (P=0.005) and Bismuth stage (P=0.003) were independently and significantly associated with early stent occlusion. In the training cohort, BP-ANN had larger AUC than the multivariate logistic regression model (P=0.00049). In the internal testing cohort, the AUC of the BP-ANN had larger AUC than the multivariate logistic regression model (P=0.02142).

Conclusions:

This study showed that the BP-ANN model is a good predictive tool. It performed better than the conventional and commonly used statistical model in predicting early occlusion of bilateral plastic stent placement for inoperable HCA.

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