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

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

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.

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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

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Ji-Eun Kim, Jung Hoon Kim, Sang Joon Park & Joon Koo Han

  2. Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea

    Jung Hoon Kim & Joon Koo Han

  3. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea

    Jung Hoon Kim & Joon Koo Han

  4. Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon-Si, Gyeonggi-Do, South Korea

    Seo-Youn Choi

  5. Department of Surgery, Seoul National University Hospital, Seoul, Republic of Korea

    Nam-Joon Yi

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  1. Ji-Eun Kim
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Correspondence to Jung Hoon Kim.

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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.

Appendix

Appendix

See Table 5.

Table 5 Models for predicting regeneration index
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Kim, JE., Kim, J.H., Park, S.J. et al. Prediction of liver remnant regeneration after living donor liver transplantation using preoperative CT texture analysis. Abdom Radiol 44, 1785–1794 (2019). https://doi.org/10.1007/s00261-018-01892-2

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