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Prediction of liver remnant regeneration after living donor liver transplantation using preoperative CT texture analysis

  • Ji-Eun Kim
  • Jung Hoon KimEmail author
  • Sang Joon Park
  • Seo-Youn Choi
  • Nam-Joon Yi
  • Joon Koo Han
Hepatobiliary
  • 105 Downloads

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: \( [(V_{\text{LR}} - V_{\text{FLR}} )/V_{\text{FLR}} ]\, \times \,100 \). 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.

Keywords

Liver Liver transplantation Tissue Donor Regeneration Tomography 

Abbreviations

LDLT

Living donor liver transplantation

CT

Computed tomography

ROIs

Regions of interest

FLR

Future liver remnant

SD

Standard deviation

GLCM

Gray-Level Co-occurrence Matrix

ASM

Angular second moment

IDM

GLCM inverse difference moment

LR

Liver remnant

AIC

Akaike information criteria

VIF

Variance inflation factor

CIs

Confidence intervals

Notes

Acknowledgements

We would like to thank Bonnie Hami, MA (USA) and Seunghyun Kim for her editorial assistance in the preparation of this manuscript.

Compliance with ethical standards

Conflict of interest

All authors confirm that no disclosure of potential conflicts of interest.

Ethical approval

This retrospective study was approved by our institutional review board, and the requirement to obtain written, informed consent was waived.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Ji-Eun Kim
    • 1
  • Jung Hoon Kim
    • 1
    • 2
    • 3
    Email author
  • Sang Joon Park
    • 1
  • Seo-Youn Choi
    • 4
  • Nam-Joon Yi
    • 5
  • Joon Koo Han
    • 1
    • 2
    • 3
  1. 1.Department of RadiologySeoul National University HospitalSeoulRepublic of Korea
  2. 2.Department of RadiologySeoul National University College of MedicineSeoulRepublic of Korea
  3. 3.Institute of Radiation MedicineSeoul National University Medical Research CenterSeoulRepublic of Korea
  4. 4.Department of RadiologySoonchunhyang University Bucheon HospitalBucheon-Si, Gyeonggi-DoSouth Korea
  5. 5.Department of SurgerySeoul National University HospitalSeoulRepublic of Korea

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