Reproducibility and reliability of computed tomography volumetry in estimation of the right-lobe graft weight in adult-to-adult living donor liver transplantation: Cantlie's line vs portal vein territorialization

Reproducibility and efficiency of liver volumetry using manual method and liver analysis software

BACKGROUND

For liver volumetry, manual tracing on computed tomography (CT) images is time-consuming and operator dependent. To overcome these disadvantages, several three-dimensional simulation software programs have been developed; however, their efficacy has not fully been evaluated.

METHODS

Three physicians performed liver volumetry on preoperative CT images on 30 patients who underwent formal right hepatectomy, using manual tracing volumetry and two simulation software programs, SYNAPSE and syngo.via. The future liver remnant (FLR) was calculated using each method of volumetry. The primary endpoint was reproducibility and secondary outcomes were calculation time and learning curve.

RESULTS

The mean FLR was significantly lower for manual volumetry than for SYNAPSE or syngo.via; there was no significant difference in mean FLR between the two software-based methods. Reproducibility was lower for the manual method than for the software-based methods. Mean calculation time was shortest for SYNAPSE. For the two physicians unfamiliar with the software, no obvious learning curve was observed for using SYNAPSE, whereas learning curves were observed for using syngo.via.

CONCLUSIONS

Liver volumetry was more reproducible and faster with three-dimensional simulation software, especially SYNAPSE software, than with the conventional manual tracing method. Software can help even inexperienced physicians learn quickly how to perform liver volumetry.

Estimation of right lobe graft weight for living donor liver transplantation using deep learning-based fully automatic computed tomographic volumetry

This study aimed at developing a fully automatic technique for right lobe graft weight estimation using deep learning algorithms. The proposed method consists of segmentation of the full liver region from computed tomography (CT) images, classification of the entire liver region into the right and left lobes, and estimation of the right lobe graft weight from the CT-measured right lobe graft volume using a volume-to-weight conversion formula. The first two steps were performed with a transformer-based deep learning model. To train and evaluate the model, a total of 248 CT datasets (188 for training, 40 for validation, and 20 for testing and clinical evaluation) were used. The Dice similarity coefficient (DSC), mean surface distance (MSD), and the 95th percentile Hausdorff distance (HD95) were used for evaluating the segmentation accuracy of the full liver region and the right liver lobe. The correlation coefficient (CC), percentage error (PE), and percentage absolute error (PAE) were used for the clinical evaluation of the estimated right lobe graft weight. The proposed method achieved high accuracy in segmentation for DSC, MSD, and HD95 (95.9% ± 1.0%, 1.2 ± 0.4 mm, and 5.2 ± 1.9 mm for the entire liver region; 92.4% ± 2.7%, 2.0 ± 0.7 mm, and 8.8 ± 2.9 mm for the right lobe) and in clinical evaluation for CC, PE, and PAE (0.859, - 1.8% ± 9.6%, and 8.6% ± 4.7%). For the right lobe graft weight estimation, the present study underestimated the graft weight by - 1.8% on average. A mean difference of - 21.3 g (95% confidence interval: - 55.7 to 13.1, p = 0.211) between the estimated graft weight and the actual graft weight was achieved in this study. The proposed method is effective for clinical application.

Preoperative estimation of hemi-liver volume using standard liver volume and portal vein diameter ratio in living donor liver transplantation

Backgrounds/Aims

Although body surface area (BSA)-based standard liver volume (SLV) formulae have been used for living donor liver transplantation and hepatic resection, hemi-liver volume (HLV) is needed more frequently. HLV can be assessed using right or left portal vein diameter (RPVD or LPVD). The aim of this study was to validate the reliability of using portal vein diameter ratio (PVDR) for assessing HLV in living liver donors.

Methods

This study included 92 living liver donors (59 males and 33 females) who underwent surgery between January 2020 and December 2020. Computed tomography (CT) images were used for measurements.

Results

Mean age of donors was 35.5 ± 7.2 years. CT volumetry-measured total liver volume (TLV), right HLV, left HLV, and percentage of right HLV in TLV were 1,442.9 ± 314.2 mL, 931.5 ± 206.4 mL, 551.4 ± 126.5 mL, and 64.6% ± 3.6%, respectively. RPVD, LPVD, and main portal vein diameter were 12.2 ± 1.5 mm, 10.0 ± 1.3 mm, and 15.3 ± 1.7 mm, respectively (corresponding square values: 149.9 ± 36.9 mm², 101.5 ± 25.2 mm², and 237.2 ± 52.2 mm², respectively). The sum of RPVD² and LPVD² was 251.1 ± 56.9 mm². BSA-based SLV was 1,279.5 ± 188.7 mL (error rate: 9.1% ± 14.4%). SLV formula- and PVDR-based right HLV was 760.0 ± 130.7 mL (error rate: 16.2% ± 13.3%).

Conclusions

Combining BSA-based SLV and PVDR appears to be a simple method to predict right or left HLV in living donors or split liver transplantation.

Relationship of body mass index and abdominal fat with radiation dose received during preoperative liver CT in potential living liver donors: a cross-sectional study

BACKGROUND

Although contrast-enhanced computed tomography (CT) is currently the most widely-used imaging modality for the preoperative evaluation of potential living liver donors, radiation exposure remains a major concern. The present study aimed to determine the relationship of body mass index (BMI) and abdominal fat with the effective radiation dose received during liver CT scans as part of a pre-donation work-up in potential living donors.

METHODS

This retrospective cross-sectional study included 695 potential living donors (mean age, 30.5±9.7 years; 445 men and 250 women) who had undergone preoperative liver CT scans between 2017 and 2018. The following measures were evaluated: BMI, abdominal fat as measured at the level of the third lumbar vertebra, and effective dose based on the dose length product (DLP). Correlations between the effective dose and other variables were evaluated using Pearson's correlation coefficient.

RESULTS

The mean BMI, total fat area (TFA), and effective dose were 23.6±3.3 kg/m2, 218.7±110.0 cm2, and 9.4±3.3 mSv, respectively. The effective dose during liver CT scans had a strong positive correlation with both BMI (r=0.715; P<0.001) and TFA (r=0.792; P<0.001). As BMI and TFA increased, so did the effective dose.

CONCLUSIONS

Higher BMI and TFA significantly increased the radiation dose received during liver CT scans in potential living donors.

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.