Clinical implication of hepatic volumetry for living donor liver transplantation

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

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.

Accuracy and Efficiency of Right-Lobe Graft Weight Estimation Using Deep-Learning-Assisted CT Volumetry for Living-Donor Liver Transplantation

CT volumetry (CTV) has been widely used for pre-operative graft weight (GW) estimation in living-donor liver transplantation (LDLT), and the use of a deep-learning algorithm (DLA) may further improve its efficiency. However, its accuracy has not been well determined. To evaluate the efficiency and accuracy of DLA-assisted CTV in GW estimation, we performed a retrospective study including 581 consecutive LDLT donors who donated a right-lobe graft. Right-lobe graft volume (GV) was measured on CT using the software implemented with the DLA for automated liver segmentation. In the development group (n = 207), a volume-to-weight conversion formula was constructed by linear regression analysis between the CTV-measured GV and the intraoperative GW. In the validation group (n = 374), the agreement between the estimated and measured GWs was assessed using the Bland–Altman 95% limit-of-agreement (LOA). The mean process time for GV measurement was 1.8 ± 0.6 min (range, 1.3–8.0 min). In the validation group, the GW was estimated using the volume-to-weight conversion formula (estimated GW [g] = 206.3 + 0.653 × CTV-measured GV [mL]), and the Bland–Altman 95% LOA between the estimated and measured GWs was −1.7% ± 17.1%. The DLA-assisted CT volumetry allows for time-efficient and accurate estimation of GW in LDLT.