Abstract：
BACKGROUND AND OBJECTIVE:Liver quality evaluation is one of the vital steps for predicting the success of liver transplantation. Current evaluation methods, such as biopsy and visual inspection, which are either invasive or lack of consistent standards, provide limited predictive value of long-term transplant viability. Objective analytical models, based on the real-time infrared images of livers during perfusion and preservation, are proposed as novel methods to precisely evaluate donated liver quality. METHODS:In this study, by using principal component analysis to extract infrared image features as predictors, we construct a multivariate logistic regression model for single liver quality evaluation, and a multi-task learning logistic regression model for cross-liver quality evaluation. RESULTS:The single liver quality predictions show testing errors of 0%. The leave-one-liver-out predictions show testing errors ranging from 9% to 36%. CONCLUSIONS:It is found that there is a strong correlation between the viability of livers and the infrared image features in both single liver and cross-liver quality evaluations. These analytical methods also determine that the selected significant infrared image features indicate regional difference in viability and show that more stringent pre-implantation evaluation may be needed to predict transplant outcomes.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Lan Q,Sun H,Robertson J,Deng X,Jin Rdoi
10.1016/j.cmpb.2018.06.003subject
Has Abstractpub_date
2018-10-01 00:00:00pages
31-47eissn
0169-2607issn
1872-7565pii
S0169-2607(17)30209-2journal_volume
164pub_type
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