Non-invasive assessment of liver quality in transplantation based on thermal imaging analysis. (October 2018)

• Record Type: Journal Article
• Authors: Lan, Qing; Sun, Hongyue; Robertson, John; Deng, Xinwei; Jin, Ran
• Is Part Of: Computer methods and programs in biomedicine. Volume 164(2018)
• Page Start: 31
• Page End: 47
• Publication Date: 2018-10
• Subjects: Liver quality evaluation -- Liver transplantation -- Infrared image -- Principal component analysis -- Logistic regression -- Multi-task learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.06.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Principal component analysis (PCA) features were extracted from real-time infrared images to precisely evaluate liver quality in order to conduct transplantation. A multivariate logistic regression model was applied for single liver quality evaluation. A multi-task learning logistic regression model was constructed for cross-liver quality evaluation. There is a strong correlation between the viability of livers and the infrared image features. These analytical methods determine that the selected significant infrared image features indicate difference in liver viability. 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. Graphical abstract: