S-Space College of Medicine/School of Medicine (의과대학/대학원) Radiology (영상의학전공) Journal Papers (저널논문_영상의학전공)
Appropriateness of a donor liver with respect to macrosteatosis: application of artificial neural networks to US images--initial experience
- Kim, Se Hyung; Lee, Jeong Min; Kim, Jong Hyo; Kim, Kwang Gi; Han, Joon Koo; Lee, Kyoung Ho; Park, Seong Ho; Yi, Nam-Joon; Suh, Kyung-Suk; An, Su Kyung; Kim, Young Jun; Son, Kyu Ri; Lee, Hye Seung; Choi, Byung Ihn
- Issue Date
- Radiological Society of North America
- Radiology 2005; 234:793–803.
- 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.
- 0033-8419 (Print)
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