Accuracy of preoperative CT liver volumetry in living donor hepatectomy and its clinical implications

Automated liver volumetry and hepatic steatosis quantification with magnetic resonance imaging proton density fat fraction

BACKGROUND

Preoperative imaging evaluation of liver volume and hepatic steatosis for the donor affects transplantation outcomes. However, computed tomography (CT) for liver volumetry and magnetic resonance spectroscopy (MRS) for hepatic steatosis are time consuming. Therefore, we investigated the correlation of automated 3D-multi-echo-Dixon sequence magnetic resonance imaging (ME-Dixon MRI) and its derived proton density fat fraction (MRI-PDFF) with CT liver volumetry and MRS hepatic steatosis measurements in living liver donors.

METHODS

This retrospective cross-sectional study was conducted from December 2017 to November 2022. We enrolled donors who received a dynamic CT scan and an MRI exam within 2 days. First, the CT volumetry was processed semiautomatically using commercial software, and ME-Dixon MRI volumetry was automatically measured using an embedded sequence. Next, the signal intensity of MRI-PDFF volumetric data was correlated with MRS as the gold standard.

RESULTS

We included the 165 living donors. The total liver volume of ME-Dixon MRI was significantly correlated with CT (r = 0.913, p < 0.001). The fat percentage measured using MRI-PDFF revealed a strong correlation between automatic segmental volume and MRS (r = 0.705, p < 0.001). Furthermore, the hepatic steatosis group (MRS ≥5%) had a strong correlation than the non-hepatic steatosis group (MRS <5%) in both volumetric (r = 0.906 vs. r = 0.887) and fat fraction analysis (r = 0.779 vs. r = 0.338).

CONCLUSION

Automated ME-Dixon MRI liver volumetry and MRI-PDFF were strongly correlated with CT liver volumetry and MRS hepatic steatosis measurements, especially in donors with hepatic steatosis.

Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence

The shortage of deceased donor organs has prompted the development of alternative liver grafts for transplantation. Living-donor liver transplantation (LDLT) has emerged as a viable option, expanding the donor pool and enabling timely transplantation with favorable graft function and improved long-term outcomes. An accurate evaluation of the donor liver's volumetry (LV) and anatomical study is crucial to ensure adequate future liver remnant, graft volume and precise liver resection. Thus, ensuring donor safety and an appropriate graft-to-recipient weight ratio. Manual LV (MLV) using computed tomography has traditionally been considered the gold standard for assessing liver volume. However, the method has been limited by cost, subjectivity, and variability. Automated LV techniques employing advanced segmentation algorithms offer improved reproducibility, reduced variability, and enhanced efficiency compared to manual measurements. However, the accuracy of automated LV requires further investigation. The study provides a comprehensive review of traditional and emerging LV methods, including semi-automated image processing, automated LV techniques, and machine learning-based approaches. Additionally, the study discusses the respective strengths and weaknesses of each of the aforementioned techniques. The use of artificial intelligence (AI) technologies, including machine learning and deep learning, is expected to become a routine part of surgical planning in the near future. The implementation of AI is expected to enable faster and more accurate image study interpretations, improve workflow efficiency, and enhance the safety, speed, and cost-effectiveness of the procedures. Accurate preoperative assessment of the liver plays a crucial role in ensuring safe donor selection and improved outcomes in LDLT. MLV has inherent limitations that have led to the adoption of semi-automated and automated software solutions. Moreover, AI has tremendous potential for LV and segmentation; however, its widespread use is hindered by cost and availability. Therefore, the integration of multiple specialties is necessary to embrace technology and explore its possibilities, ranging from patient counseling to intraoperative decision-making through automation and AI.

Preoperative Computed Tomography Volumetry and Graft Weight Estimation of Left Lateral Segment in Pediatric Living Donor Liver Transplant

OBJECTIVES

Liver volumetry based on a computed tomography scan is widely used to estimate liver volume before any liver resection, especially before living donorliver donation. The 1-to-1 conversion rule for liver volume to liver weight has been widely adopted; however, debate continues regarding this approach. Therefore, we analyzed the relationship between the left-lateral lobe liver graft volume and actual graft weight.

MATERIALS AND METHODS

This study retrospectively included consecutive donors who underwent left lateral hepatectomy for pediatric living donor liver transplant from December 2008 to September 2020. All donors were healthy adults who met the evaluation criteria for pediatric living donor liver transplant and underwent a preoperative contrast-enhanced computed tomography scan. Manual segmentation of the leftlateral liverlobe for graft volume estimation and intraoperative measurement of an actual graft weight were performed. The relationship between estimated graft volume and actual graft weight was analyzed.

RESULTS

Ninety-four living liver donors were included in the study. The mean actual graft weight was ~283.4 ± 68.5 g, and the mean graft volume was 244.9 ± 63.86 mL. A strong correlation was shown between graft volume and actual graft weight (r = 0.804; P < .001). Bland-Altman analysis revealed an interobserver agreement of 38.0 ± 97.25, and intraclass correlation coefficient showed almost perfect agreement(r = 0.840; P < .001). The conversion formula for calculating graft weight based on computed tomography volumetry was determined based on regression analysis: 0.88 × graft volume + 41.63.

CONCLUSIONS

The estimation of left liver graft weight using only the 1-to-1 rule is subject to measurable variability in calculated graft weights and tends to underestimate the true graft weight. Instead, a different, improved conversion formula should be used to calculate graft weight to more accurately determine donor graft weight-to-recipient body weightratio and reduce the risk of underestimation of liver graft weightin the donor selection process before pediatric living donor liver transplant.

Pure Laparoscopic Versus Open Right Hepatectomy in Living Liver Donors: Graft Weight Discrepancy

BACKGROUND Accurate volumetric evaluation of donors' livers before surgery is crucial for successful living-donor liver transplantation. However, there are few studies on the volumetric evaluation in the recently popularized pure laparoscopic donor hepatectomy method, in contrast to the number of studies for conventional donor hepatectomy. We aimed to analyze the difference between estimated graft weight and actual graft weight in pure laparoscopic donor right hepatectomy (PLDRH) and conventional donor right hepatectomy (CDRH) procedures. MATERIAL AND METHODS The medical records of 612 donors who underwent right hepatectomy in living-donor liver transplantation between January 2014 and December 2020 were retrospectively reviewed. The CDRH group targeted patients from January 2014 to October 2015, and the PLDRH group targeted patients from March 2016 to December 2020. RESULTS There were 119 and 376 donors who underwent CDRH and PLDRH, respectively. Although there was no significant difference in the estimated graft weights (P=0.994) and actual graft weights (P=0.489) between the groups, the estimated graft weights were significantly higher than the actual graft weights in both groups. However, the estimated graft weight and actual graft weight showed linear correlations in both the CDRH (r=0.81, P<0.001) and PLDRH (r=0.76, P<0.001) groups, with the CDRH group having greater linearity. CONCLUSIONS The estimates of graft weight were similar between the 2 groups. However, since the actual graft weight tended to be smaller in the PLDRH group, this should be considered before surgery.

Fully automated preoperative liver volumetry incorporating the anatomical location of the central hepatic vein

The precise preoperative calculation of functional liver volumes is essential prior major liver resections, as well as for the evaluation of a suitable donor for living donor liver transplantation. The aim of this study was to develop a fully automated, reproducible, and quantitative 3D volumetry of the liver from standard CT examinations of the abdomen as part of routine clinical imaging. Therefore, an in-house dataset of 100 venous phase CT examinations for training and 30 venous phase ex-house CT examinations with a slice thickness of 5 mm for testing and validating were fully annotated with right and left liver lobe. Multi-Resolution U-Net 3D neural networks were employed for segmenting these liver regions. The Sørensen-Dice coefficient was greater than 0.9726 ± 0.0058, 0.9639 ± 0.0088, and 0.9223 ± 0.0187 and a mean volume difference of 32.12 ± 19.40 ml, 22.68 ± 21.67 ml, and 9.44 ± 27.08 ml compared to the standard of reference (SoR) liver, right lobe, and left lobe annotation was achieved. Our results show that fully automated 3D volumetry of the liver on routine CT imaging can provide reproducible, quantitative, fast and accurate results without needing any examiner in the preoperative work-up for hepatobiliary surgery and especially for living donor liver transplantation.

Correlation between liver volume and liver weight in a cohort with chronic liver disease: a semiautomated CT-volumetry study

BACKGROUND

To estimate the optimal density coefficient for conversion of liver volume into liver weight in patients with chronic liver disease based on semiautomated CT-liver volumetry data and the histologic Ishak score of explanted liver.

METHODS

A total of 114 patients (39 female; age, 46±20 years) with chronic liver diseases who underwent liver transplantation between January 2010 and September 2020 were identified over a patient chart search at our institution and subsequently analyzed in retrospect. All patients had contrast-enhanced CT-examinations (mean, 24 days) to liver transplantation. Liver volume was calculated by a semiautomated software and results compared with the liver weight registered by the pathologist. Each explanted liver was histologically scored into six classes according to the Ishak classification where the categories were subgrouped based on recommendation of the pathologists into the following categories 0-3, 4-5 and 6.

RESULTS

Mean liver volume was 1,870±1,195, 1,162±679 and 1,278±510 mL for the categories 0-3, 4-5 and 6, respectively. Mean liver weight was 1,624±999, 1,082±669 and 1,346±559 g for the categories 0-3, 4-5 and 6, respectively. A coefficient of 0.92±0.22, 0.98±0.28 and 1.06±0.20 g/mL was found at best for conversion of liver volume into liver weight in these subgroups. Differences between Ishak-subgroups proved significant (0.002). In 4 patients with cystic liver disease, density coefficients varied significantly and were found generally lower compared to the other liver disorders.

CONCLUSIONS

Our results yielded significant differences between the density coefficients calculated along with the Ishak score and also for the subgroup with cystic liver disease.

Left lateral segment liver volume is not correlated with anthropometric measures

BACKGROUND

Liver transplantation is definitive therapy for end stage liver disease in pediatric patients. Living donor liver transplantation (LDLT) with the left lateral segment (LLS) is often a feasible option. However, the size of LLS is an important factor in donor suitability - particularly when the recipient weighs less than 10 kg. In the present study, we sought to define a formula for estimating left lateral segment volume (LLSV) in potential LLS donors.

METHODS

We obtained demographic and anthropometric measurements on 50 patients with Computed Tomography (CT) scans to determine whole liver volume (WLV), right liver volume (RLV), and LLSV. We performed univariable and multivariable linear regression with backwards stepwise variable selection (p < 0.10) to determine final models.

RESULTS

Our study found that previously reported anthropometric and demographics variables correlated with volume were significantly associated with WLV and RLV. On univariable analysis, no demographic or anthropometric measures were correlated with LLSV. On multivariable analysis, LLSV was poorly predicted by the final model (R2 = 0.10, Coefficient of Variation [CV] = 42.2) relative to WLV (R2 = 0.33, CV = 18.8) and RLV (R2 = 0.41, CV = 15.8).

CONCLUSION

Potential LLS living donors should not be excluded based on anthropometric data: all potential donors should be evaluated regardless of their size.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

PURPOSE

To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging.

METHODS

A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results.

RESULTS

The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE).

CONCLUSION

Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.

Is Portal Inflow Modulation Always Necessary for Successful Utilization of Small Volume Living Donor Liver Grafts?

Although the well-accepted lower limit of the graft-to-recipient weight ratio (GRWR) for successful living donor liver transplantation (LDLT) remains 0.80%, many believe grafts with lower GRWR may suffice with portal inflow modulation (PIM), resulting in equally good recipient outcomes. This study was done to evaluate the outcomes of LDLT with small-for-size grafts (GRWR <0.80%). Of 1321 consecutive adult LDLTs from January 2012 to December 2017, 287 (21.7%) had GRWR <0.80%. PIM was performed (hemiportocaval shunt [HPCS], n = 109; splenic artery ligation [SAL], n = 14) in 42.9% patients. No PIM was done if portal pressure (PP) in the dissection phase was <16 mm Hg. Mean age of the cohort was 49.3 ± 9.1 years. Median Model for End-Stage Liver Disease score was 14, and the lowest GRWR was 0.54%. A total of 72 recipients had a GRWR <0.70%, of whom 58 underwent HPCS (1 of whom underwent HPCS + SAL) and 14 underwent no PIM, whereas 215 had GRWR between 0.70% and 0.79%, of whom 51 and 14 underwent HPCS and SAL, respectively. During the same period, 1034 had GRWR ≥0.80% and did not undergo PIM. Small-for-size syndrome developed in 2.8% patients. Three patients needed shunt closure at 1 and 4 weeks and 60 months. The 1-year patient survival rates were comparable. In conclusion, with PIM protocol that optimizes postperfusion PP, low-GRWR grafts can be used for appropriately selected LDLT recipients with acceptable outcomes.

Dual graft living donor liver transplantation - a case report

Background

Living donor liver transplantation (LDLT) has emerged as an equally viable option to deceased donor liver transplant for treating end stage liver disease patients. Optimising the recipient outcome without compromising donor safety is the primary goal of LDLT. Achieving the adequate graft to recipient weight ratio (GRWR) is important to prevent small for size syndrome which is an uncommon but potentially lethal complication of LDLT.

Case presentation

Here we describe a case of successful dual lobe liver transplant for a 32 years old patient with ethanol related end stage liver disease. A right lobe graft without middle hepatic vein and another left lateral sector graft were transplanted successfully. Recipient and both donors recovered uneventfully.

Conclusion

Dual lobe liver transplant is a feasible strategy to achieve adequate GRWR without compromising donor safety.