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Published online before print January 21, 2005, 10.1148/radiol.2343040142
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(Radiology 2005;234:793-803.)
© RSNA, 2005

Gastrointestinal Imaging

Appropriateness of a Donor Liver with Respect to Macrosteatosis: Application of Artificial Neural Networks to US Images—Initial Experience1

Se Hyung Kim, MD, Jeong Min Lee, MD, Jong Hyo Kim, PhD, Kwang Gi Kim, PhD, Joon Koo Han, MD, Kyoung Ho Lee, MD, Seong Ho Park, MD, Nam-Joon Yi, MD, Kyung-Suk Suh, MD, Su Kyung An, MD, Young Jun Kim, MD, Kyu Ri Son, MD, Hye Seung Lee, MD and Byung Ihn Choi, MD

1 From the Department of Radiology (S.H.K., J.M.L., J.H.K., J.K.H., K.H.L., S.H.P., S.K.A., Y.J.K., K.R.S., B.I.C.), Institute of Radiation Medicine (J.M.L., J.H.K., J.K.H., B.I.C.), Department of Medical Engineering (J.H.K., K.G.K.), Department of Surgery (N.J.Y., K.S.S.), and Department of Pathology (H.S.L.), Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744, Korea; and Department of Radiology, Seoul National University Bundang Hospital, Seoul, Korea (K.H.L.). Received January 25, 2004; revision requested April 1; revision received May 12; accepted June 15. Address correspondence to J.M.L. (e-mail: leejm@radcom.snu.ac.kr).

PURPOSE: To retrospectively compare performance of artificial neural networks (ANNs) applied to ultrasonographic (US) images with that of radiologists for prediction of appropriateness of a donor liver with respect to macrosteatosis before liver transplantation.

MATERIALS AND METHODS: Institutional ethics committee approved study; written informed consent was obtained. ANNs, constructed with three-layered 15-neuron back-propagation algorithm, were trained to predict appropriateness of a donor liver with respect to macrosteatosis by using statistically significant laboratory and US parameters derived from univariate analyses, together with correct diagnosis. Input variables for ANNs were alkaline phosphatase, glutamic oxaloacetic transaminase, glutamic pyruvate transaminase, {gamma}-glutamyltransferase, hepatorenal ratio of echogenicity, and tail area ratio and tail length of portal vein wall echogenicity. Three radiologists graded US images in 94 potential donors (71 men and 23 women) on the basis of four degrees of hepatic steatosis. After training and testing of ANNs, performance of ANNs and radiologists in predicting appropriateness of potential donors was evaluated with receiver operating characteristic (ROC) analysis and compared by means of univariate z score test.

RESULTS: Among 94 potential donor livers, 76 were normal or had mild steatosis, and 18 had moderate or severe macrosteatosis at histopathologic examination. Area under ROC curve (Az) of ANNs (Az = 0.9673) was significantly greater than that of radiologists (faculty, Az = 0.9106, P = .048; fellow, Az = 0.9038, P = .044; resident, Az = 0.8931, P = .038). No statistically significant difference in sensitivity for predicting appropriateness as a liver donor with respect to macrosteatosis was found between ANNs (88.9%) and radiologists (P > .05). However, specificity of ANNs (96.1%) was significantly better than that of radiologists (P < .003).

CONCLUSION: ANNs might be a useful tool to categorize whether a donor liver is appropriate for transplantation with respect to macrosteatosis on the basis of multiple variables related to laboratory and US features. Further study is needed.

© RSNA, 2005




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