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- Title
Prediction of liver remnant regeneration after living donor liver transplantation using preoperative CT texture analysis.
- Authors
Kim, Ji-Eun; Park, Sang Joon; Kim, Jung Hoon; Han, Joon Koo; Choi, Seo-Youn; Yi, Nam-Joon
- Abstract
Purpose: To predict the rate of liver regeneration after living donor liver transplantation (LDLT) using pre-operative computed tomography (CT) texture analysis. Materials and methods: 112 living donors who performed right hepatectomy for LDLT were included retrospectively. We measured the volume of future remnant liver (FLR) on pre-operative CT and the volume of remnant liver (LR) on follow-up CT, taken at a median of 123 days after transplantation. The regeneration index (RI) was calculated using the following equation: [ (V LR - V FLR) / V FLR ] × 100 . Computerized texture analysis of the semi-automatically segmented FLR was performed. We used a stepwise, multivariable linear regression to assess associations of clinical features and texture parameters in relation to RI and to make the best-fit predictive model. Results: The mean RI was 110.7 ± 37.8%, highly variable ranging from 22.4% to 247.0%. Among texture parameters, volume of FLR, standard deviation, variance, and gray level co-occurrence matrices (GLCM) contrast were found to have significant correlations between RI. In multivariable analysis, smaller volume of FLR (ß − 0.17, 95% CI − 0.22 to − 0.13) and lower GLCM contrast (ß − 1.87, 95% CI − 3.64 to − 0.10) were associated with higher RI. The regression equation predicting RI was following: RI = 203.82 + 10.42 × pre-operative serum total bilirubin (mg/dL) − 0.17 × VFLR (cm3) − 1.87 × GLCM contrast (× 100). Conclusion: Volume of FLR and GLCM contrast were independent predictors of RI, showing significant negative correlations. Pre-operative CT with texture analysis can be useful for predicting the rate of liver regeneration in living donor of liver transplantation.
- Subjects
LIVER regeneration; PREDICTION models; LIVER transplantation; COMPUTED tomography; TEXTURE analysis (Image processing); ORGAN donors
- Publication
Abdominal Radiology, 2019, Vol 44, Issue 5, p1785
- ISSN
2366-004X
- Publication type
Article
- DOI
10.1007/s00261-018-01892-2