Ann Surg Treat Res.  2024 Aug;107(2):91-99. 10.4174/astr.2024.107.2.91.

Multivariable linear model for predicting graft weight based on 3-dimensional volumetry in regards to body weight change of living liver donor: an observational cohort study

• Han S
• Rhu J
• Lim S
• Choi Gs
• Kim JM
• Joh JW

• KMID: 2558385
• DOI: http://doi.org/10.4174/astr.2024.107.2.91

Abstract

Purpose
The purpose of this study is to build a prediction model for estimating graft weight about different graft volumetry methods combined with other variables.
Methods
Donors who underwent living-donor right hepatectomy from March 2021 to March 2023 were included. Estimated graft volume measured by conventional method and 3-dimensional (3D) software were collected as well as the actual graft weight. Linear regression was used to build a prediction model. Donor groups were divided according to the 3D volumetry of <700 cm³ , 700–899 cm³ , and ≥900 cm³ to compare the performance of different models.
Results
A total of 119 donors were included. Conventional volumetry showed R² of 0.656 (P < 0.001) while 3D software showed R² of 0.776 (P < 0.001). The R² of the multivariable model was 0.842 (P < 0.001) including for 3D volume (β = 0.623, P < 0.001), body mass index (β = 7.648, P < 0.001), and amount of weight loss (β = –7.252, P < 0.001). The median errors between different models and actual graft weight did not differ in donor groups (<700 and 700–899 cm³ ), while the median error of univariable linear model using 3D software (122.5; interquartile range [IQR], 61.5–179.8) was significantly higher than multivariable-adjusted linear model (41.5; IQR, 24.8–69.8; P = 0.003) in donors with estimated graft weight ≥900 cm³ .
Conclusion
The univariable 3D volumetry model showed an acceptable outcome for donors with an estimated graft volume <900 cm³ . For donors with an estimated graft volume ≥900 cm³ , the multivariable-adjusted linear model showed higher accuracy.

Keyword

Liver transplantation; Liver graft; Three-dimensional volumetry; Volumetry

Figure

• 

  Fig. 1 Different methods for measuring the estimated graft volume. (A) a: The conventional method involved the transplant surgeon measuring the liver’s cross-sectional area using picture archiving and communication system (PACS) and Microsoft Excel. b: A biomedical artist created a 3-dimensional (3D) model using 3D software and calculated the volume through the software. c: The actual graft was weighed intraoperatively after preservation solution perfusion. (B) While the 3D view obtained from the PACS image shows a hazy and indistinguishable view, additional edits using (C) Mimics Medical (Materialise), (D) 3D Slicer (https://www.slicer.org), and (E) beta version of AcroXeR LiverAIz viewer (SurgicalMind, Inc. and LiverAIz, Inc.) show a well-distinguishable 3D view with volumetric information. GRWR, graft-to-recipient weight ratio; PACS, picture archiving and communication system.

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  Fig. 2 Scatter plot between different graft weight estimation models and actual graft post-perfusion weight. (A) Estimated graft weight by univariable linear regression model using conventional method. (B) Estimated graft weight by univariable linear regression model using 3-dimensional (3D) software. (C) Estimated graft weight by multivariable linear regression model including 3D software, body mass index and amount of weight loss.

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  Fig. 3 The mean ± standard deviations (SD) for error of 3-dimensional (3D) software subtracted by error of multivariable-adjusted model for 3 different donor groups divided by 3D volumetry. As the box and whisker plot approaches the positive value, it suggests that the univariable linear model using 3D software exhibits more error. Conversely, when the plot approaches the negative value, it suggests that the multivariable-adjusted linear regression model shows more error.

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