Dr. Liver: A preoperative planning system of liver graft volumetry for living donor liver transplantation

Liver volumetry and liver-regenerative interventions: history, rationale, and emerging tools

BACKGROUND

Postoperative hepatic insufficiency (PHI) is the most feared complication after hepatectomy. Volume of the future liver remnant (FLR) is one objectively measurable indicator to identify patients at risk of PHI. In this review, we summarized the development and rationale for the use of liver volumetry and liver-regenerative interventions and highlighted emerging tools that could yield new advancements in liver volumetry.

METHODS

A review of MEDLINE/PubMed, Embase, and Cochrane Library databases was conducted to identify literature related to liver volumetry. The references of relevant articles were reviewed to identify additional publications.

RESULTS

Liver volumetry based on radiologic imaging was developed in the 1980s to identify patients at risk of PHI and later used in the 1990s to evaluate grafts for living donor living transplantation. The field evolved in the 2000s by the introduction of standardized FLR based on the hepatic metabolic demands and in the 2010s by the introduction of the degree of hypertrophy and kinetic growth rate as measures of the FLR regenerative and functional capacity. Several liver-regenerative interventions, most notably portal vein embolization, are used to increase resectability and reduce the risk of PHI. In parallel with the increase in automation and machine assistance to physicians, many semi- and fully automated tools are being developed to facilitate liver volumetry.

CONCLUSION

Liver volumetry is the most reliable tool to detect patients at risk of PHI. Advances in imaging analysis technologies, newly developed functional measures, and liver-regenerative interventions have been improving our ability to perform safe hepatectomy.

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.

CT volume analysis in living donor liver transplantation: accuracy of three different approaches

OBJECTIVES

The aim of this retrospective study is to compare and evaluate accuracy of three different approaches of liver volume quantification in living donor transplantations.

METHODS

This is a single-center, retrospective study of 60 donors. The total and right lobe liver volumes were analyzed in the portal-venous phase by two independent radiologists who estimated the volumes using manual, semi-automated and automated segmentation methods. The measured right lobe liver volume was compared to the real weight of the graft after back-table examinations.

RESULTS

The mean estimated overall liver volume was 1164.4 ± 137.0 mL for manual, 1277.4 ± 190.4 mL for semi-automated and 1240.1 ± 108.5 mL for automated segmentation. The mean estimated right lobe volume was 762.0 ± 122.4 mL for manual, 792.9 ± 139.9 mL for semi-automated and 765.4 ± 132.7 mL for automated segmentation. The mean graft weight was 711.2 ± 142.9 g. The manual method better correlated with the graft weight (r = 0.730) in comparison with the semi-automated (r = 0.685) and the automated (r = 0.699) methods (p < 0.001). The mean error ratio in volume estimation by each application was 12.7 ± 16.6% for manual, 17.1 ± 17.3% for semi-automated, 14.7 ± 16.8% for automated methods. There was a statistically significant difference between the mean error ratio of the manual and the semi-automated segmentations (p = 0.017), and no statistically significant difference between the manual and the automated applications (p = 0.199).

CONCLUSION

Volume analysis application better correlates with graft weight, but there is no obvious difference between correlation coefficients of all three methods. All three modalities had an error ratio, of which the semi-automated method showed the highest value.

CRITICAL RELEVANCE STATEMENT

Volume analysis application was more accurate, but there is no drastic difference between correlation coefficients of all three methods.

Incidence of superficial left hepatic vein and its usability for graft hepatic vein venoplasty in pediatric liver transplantation

BACKGROUNDS

The anatomy of the left hepatic vein (LHV) is variable; thus, it should be considered for graft hepatic vein (GHV) venoplasty for left lateral section (LLS) and left liver grafts. This study assessed the incidence of superficial LHV (sLHV) branches according to LHV anatomy and its usability for GHV venoplasty in pediatric liver transplantation (LT).

METHODS

This study consisted of three parts: (1) anatomical classification of LHV variations and the incidence of sLHV branches; (2) morphometric simulative analysis of GHV reconstruction and (3) clinical application based on LHV anatomy.

RESULTS

The LHV anatomy of 248 potential LLS graft donors was classified into four types according to the number and location of GHV openings: one single opening (type 1; n = 186 [75.0%]), two large openings (type 2; n = 35 [14.1%]), one large and one small adjacent opening (type 3; n = 14 [5.6%]), and two large widely-separated openings (type 4; n = 13 [5.2%]). An sLHV branch was identified in 87 of 248 (35.1%) donor livers. Morphometric analysis of simulative GHV venoplasty with an sLHV branch increased GHV diameters by 30% in type 1 LLS grafts and 20% in type 2/3 LLS grafts. An analysis of 50 consecutive patients who underwent pediatric LT showed that the 2-year rates of GHV obstruction were 2.0% with LLS grafts and 0% with left liver grafts.

CONCLUSIONS

The GHV orifice can be enlarged through LHV anatomy-based unification venoplasty. Unification venoplasty with an sLHV branch provided sufficient enlargement of the GHV orifice.

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.

Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

CT Volumetry of Convoluted Objects-A Simple Method Using Volume Averaging

Accurate measurement of object volumes using computed tomography is often important but can be challenging, especially for finely convoluted objects with severe marginal blurring from volume averaging. We aimed to test the accuracy of a simple method for volumetry by constructing, scanning and analyzing a phantom object with these characteristics which consisted of a cluster of small lucite beads embedded in petroleum jelly. Our method involves drawing simple regions of interest containing the entirety of the object and a portion of the surrounding material and using its density, along with the densities of pure lucite and petroleum jelly and the slice thickness to calculate the volume of the object in each slice. Comparison of our results with the object’s true volume showed the technique to be highly accurate, irrespective of slice thickness, image noise, reconstruction planes, spatial resolution and variations in regions of interest. We conclude that the method can be easily used for accurate volumetry in clinical and research scans without the need for specialized volumetry computer programs.