Predicting Early Allograft Dysfunction after Liver Transplantation from Post-Reperfusion Donor Liver Image

BACKGROUND: To explore the relationship between early allograft dysfunction (EAD) and post-reperfusion liver appearance, and to develop image-based models which predict EAD and short-term mortality. METHODS: A total of 351 recipients of liver transplant were enrolled and divided into training set and testing set. Liver images of post-reperfusion donors and clinical information were collected. All the images were preprocessed. Support vector machines (SVM) and convolution neural network (CNN) models based on the texture analysis of post-reperfusion liver RGB images were constructed to predict EAD. Then, the model with a better performance was selected to construct further predictive models with additional inputs of clinical information. In addition, a score, namely image score, was assigned to each liver image based on the prediction probability from the CNN model. Further, the comparisons of outcomes among different image scores were performed. RESULTS: Out of the 351 enrolled recipients, 229 were in the training set while 122 in the testing set. CNN model achieved an AUC of 0.709 in testing set, outperforming the SVM model which has an AUC of 0.661. Further predictive model was based on the framework of the CNN model, where an AUC of 0.727 was obtained. Moreover, the lager image score was found to be relative to more postoperative infusion, more postoperative complication, the longer length of ICU and hospital stay. CONCLUSION: The post-reperfusion appearance of donor liver was associated with the occurrence of EAD. Moreover, it was feasible to predict EAD and patient outcomes through the texture analysis of post-reperfusion liver RGB images.