Liver Volumetry Research Articles

Transileocolic Portal Vein Embolization Increases Remnant Liver Volume After Major Hepatectomy.

Transileocolic portal vein embolization (TIPE) may reduce the risk of liver failure after massive hepatectomy. However, convincing evidence of its usefulness in this regard is yet to be reported. Therefore, this study aimed to investigate the use of TIPE after massive hepatectomy. Twelve patients who underwent TIPE were included. Pre- and postoperative liver volumetry was determined using a 3D simulator with computed tomography. After TIPE, the percent change in total liver volume was 104.0%±13.1% (p=0.08). Conversely, the percent increase in remnant liver volume (ml)/total liver volume (ml) and remnant liver volume (ml)/standard liver volume (ml) was 122.9%±18.6% (p<0.001) and 132.2%±19.6% (p<0.001), respectively. TIPE operation time was 125±84.6 min, with minimal blood loss in all cases. Postoperative hospitalization duration during TIPE was 4.5±2.6 days. No TIPE-related complications occurred in any patient. TIPE is beneficial and safe when preoperative volumetry indicates that the remaining liver volume is inadequate and inoperable.

An anatomical analysis of liver volume and quality by ethnicity in a New Zealand population.

Post-hepatectomy liver failure is a major cause of mortality, where future liver remnant (FLR) is the key controllable factor. Recommended minimum FLR is influenced by quality of liver parenchyma. Historical research has often failed to include Māori and Pacific Island (PI) populations despite worse health outcomes. Liver analysis by ethnicity is one such example of this. The aims were to determine digital FLR for various anatomical hepatectomies, investigate any correlations between computed tomography (CT) hepatic textural analysis and body mass index (BMI); and assess the variance of these relationships for different ethnicities. One hundred and fifty-one patients who underwent abdominal CT scans at Burwood Hospital, Christchurch were retrospectively analysed. Māori and PI patients were selectively recruited to represent New Zealand's diversity. Liver volumetry, segmental ratio, and intra-hepatic fat deposits (IHFD) per ethnicity were examined. Median age of the cohort was 66 (19-95) and 75 (50%) were males. 68%, 23% and 9% patients identified as being European, Māori/PI and Asian, respectively. No statistically significant difference in volume or segment/total volume ratio were noted across different ethnicities. Obese patients had higher IHFD compared with overweight and normal BMI groups. When stratified across ethnic groups, higher IHFD were observed in Asian compared with Māori/PI populations, despite lower BMI. No significant variances in liver volumetry were found across different ethnic groups in New Zealand. However association between BMI and IHFD varied across different ethnic cohorts. Consequently, knowledge of liver volumetry is not enough; patient liver quality and ethnicity should considered for hepatic-surgery planning.

Optimization of surgical evaluation algorithms for living donor liver transplantation

BackgroundLiving donor liver transplantation (LDLT) is an established and endorsed alternative for deceased donor liver transplantation with better recipient outcomes. Nevertheless, while extensive evaluation of potential donors is crucial, evaluation algorithms differ between transplant centres and guidelines. MethodsWe included 317 individuals evaluated for LDLT between 07/2007–07/2022 in a retrospective analysis. The evaluation process was analysed to identify the key reasons for declining 77 potential donors. Additionally, 146 donors that underwent LDLT were analysed regarding risk factors for complications. ResultsThe main reasons for donor refusal were liver volumetry (40.3 %) and metabolic factors including obesity or steatotic liver disease (20.8 %). Contrast-enhanced computed tomography (CECT) identified 63.6 % of all declined donors; CECT combined with assessment of medical history, physical examination, blood testing and ultrasonography, identified 87.0 % of declined potential donors. Associated with this selection, complication rates in donors were low (≥II in 17.1 %; none with ≥IVb). Notably, higher age was a risk factor for developing a complication ≥II after hemi-hepatectomy (p = 0.0373). ConclusionsWe propose a progressive 4-step evaluation algorithm that begins with a very basic assessment combined with up-front CECT. This early phase of testing is expected to identify nearly 90 % of ineligible donors, thereby conserving critical resources, time and money, as well as minimising burden for potential donors. FundingJ.M.W. received funding by grant We-4675/6–1 from the Deutsche Forschungsgemeinschaft (DFG) in Bonn, Germany.

Comprehensive Review of Future Liver Remnant (FLR) Assessment and Hypertrophy Techniques Before Major Hepatectomy: How to Assess and Manage the FLR.

The regenerative capacities of the liver and improvements in surgical techniques have expanded the possibilities of resectability. Liver resection is often the only curative treatment for primary and secondary malignancies, despite the risk of post-hepatectomy liver failure (PHLF). This serious complication (with a 50% mortality rate) can be avoided by better assessment of liver volume and function of the future liver remnant (FLR). The aim of this review was to understand and assess clinical, biological, and imaging predictors of PHLF risk, as well as the various hypertrophy techniques, to achieve an adequate FLR before hepatectomy. We reviewed the state of the art in liver regeneration and FLR hypertrophy techniques. The use of new biological scores (such as the aspartate aminotransferase/platelet ratio index + albumin-bilirubin [APRI+ALBI] score), concurrent utilization of 99mTc-mebrofenin scintigraphy (HBS), or dynamic hepatocyte contrast-enhanced MRI (DHCE-MRI) for liver volumetry helps predict the risk of PHLF. Besides portal vein embolization, there are other FLR optimization techniques that have their indications in case of risk of failure (e.g., associating liver partition and portal vein ligation for staged hepatectomy, liver venous deprivation) or in specific situations (transarterial radioembolization). There is a need to standardize volumetry and function measurement techniques, as well as FLR hypertrophy techniques, to limit the risk of PHLF.

Role of Multi Detector CT Liver Volumetry in Surgical Assessment of Hepatic Lesions.

Role of Multi Detector CT Liver Volumetry in Surgical Assessment of Hepatic Lesions.

Automated Liver Volumetry and Hepatic Steatosis Quantification with Magnetic Resonance Imaging Proton Density Fat Fraction

BackgroundPreoperative 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. MethodsThis 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. ResultsWe 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). ConclusionAutomated 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.

Clinician overconfidence in visual estimation of the posthepatectomy liver remnant volume: A proximal source of liver failure after major hepatic resection?

BackgroundPost-hepatectomy liver failure is a source of morbidity and mortality after major hepatectomy and is related to the volume of the future liver remnant. The accuracy of a clinician’s ability to visually estimate the future liver remnant without formal computed tomography liver volumetry is unknown. MethodsTwenty physicians in diagnostic radiology, interventional radiology, and hepatopancreatobiliary surgery reviewed 20 computed tomography scans of patients without underlying liver pathology who were not scheduled for liver resection. We evaluated clinician accuracy to estimate the future liver remnant for 3 hypothetical major hepatic resections: left hepatectomy, right hepatectomy, and right trisectionectomy. The percent-difference between the mean and actual computed tomography liver volumetry (mean percent difference) was tested along with specialty differences using mixed-effects regression analysis. ResultsThe actual future liver remnant (computed tomography liver volumetry) remaining after a hypothetical left hepatectomy ranged from 59% to 75% (physician estimated range: 50%–85%), 23% to 40% right hepatectomy (15%–50%), and 13% to 29% right trisectionectomy (8%–39%). For right hepatectomy, the mean future liver remnant was overestimated by 95% of clinicians with a mean percent difference of 22% (6%–45%; P < .001). For right trisectionectomy, 90% overestimated the future liver remnant by a mean percent difference of 25% (6%–50%; P < .001). Hepatopancreatobiliary surgeons overestimated the future liver remnant for proposed right hepatectomy and right trisectionectomy by a mean percent difference of 25% and 34%, respectively. Based on years of experience, providers with <10 years of experience had a greater mean percent difference than providers with 10+ years of experience for hypothetical major hepatic resections, but was only significantly higher for left hepatectomy (9% vs 6%, P = .002). ConclusionA clinician’s ability to visually estimate the future liver remnant volume is inaccurate when compared to computed tomography liver volumetry. Clinicians tend to overestimate the future liver remnant volume, especially in patients with a small future liver remnant where the risk of posthepatectomy liver failure is greatest.

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

BackgroundFor 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. MethodsThree 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. ResultsThe 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. ConclusionsLiver 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.

Effect of Sarcopenia on the Increase in Liver Volume and Function After Portal Vein Embolization

PurposeSarcopenia is associated with a decreased kinetic growth rate (KGR) of the future liver remnant (FLR) after portal vein embolization (PVE). However, little is known on the increase in FLR function (FLRF) after PVE. This study evaluated the effect of sarcopenia on the functional growth rate (FGR) after PVE measured with hepatobiliary scintigraphy (HBS).MethodsAll patients who underwent PVE at the Amsterdam UMC between January 2005 and August 2017 were analyzed. Functional imaging by HBS was used to determine FGR. Liver volumetry was performed using multiphase contrast computed tomography (CT). Muscle area measurement to determine sarcopenia was taken at the third lumbar level (L3).ResultsOut of the 95 included patients, 9 were excluded due to unavailable data. 70/86 (81%) patients were sarcopenic. In the multivariate logistic regression analysis, sarcopenia (p = 0.009) and FLR volume (FRLV) before PVE (p = 0.021) were the only factors correlated with KGR, while no correlation was found with FGR. 90-day mortality was similar across the sarcopenic and non-sarcopenic group (4/53 [8%] versus 1/11 [9%]; p = 1.000). The resection rates were also comparable (53/70 [75%] versus 11/16 [69%]; p = 0.542).ConclusionFGR after PVE as measured by HBS appears to be preserved in sarcopenic patients. This is in contrast to KGR after PVE as measured by liver volumetry which is decreased in sarcopenic patients.Level of EvidenceLevel 3b, cohort and case control studies.

Liver volumetry in cirrhotic patients with or without hepatocellular carcinoma: Its correlation with Child-Pugh, model for end-stage liver diseases and indocyanine green dye test.

To evaluate the correlation between non-tumoral liver volume (NTLV) by computed tomography (CT) volumetry and indocyanine green retention at 15minutes (ICG-r15%), Child-Pugh score (CTP) and model for end-stage liver diseases (MELD) score in cirrhotic patients having hepatocellular carcinoma (HCC) (group A) and in cirrhotics without HCC (group B). As many as 111 consecutive patients with liver cirrhosis, who underwent triple-phase CT abdomen, were retrospectively included in our study. They were classified into group A (cirrhosis with HCC, n = 69) and group B (cirrhosis only, n = 42). Segmental liver volume, tumor and NTLV were calculated using Myrian XP-Liver segmentation software. In group B, NTLV was the same as the total liver volume (TLV). The correlation of NTLV with ICG-r15%, CTP and MELD scores was analyzed using appropriate correlation tests for each group. NTLV had a good and significant negative correlation with ICG-r15% (ρ = - 512; p < 0.001) in group A, but not in group B. It also had a significant negative correlation with CTP (ρ = - 251; p = 0.038) and MELD (ρ = - 323; p = 0.007) scores only in group A. Furthermore, ICG-r15% had a good and significant positive correlation with CTP and MELD scores in both groups (p < 0.05). NTLV showed a significant negative correlation with ICG-r15% in cirrhotic patients with HCC, but not in cirrhotic patients without HCC. Therefore, CT volumetry can be a valuable tool to predict the functional hepatic volume in patients of cirrhosis with HCC subjected for hepatectomy, where a facility of ICG-r15% is not available. However, further studies are needed to validate our findings in cirrhotic only patients.

Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.

To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI). This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by theDice similarity coefficient (DSC) with respect to manual segmentation. A Mann-Whitney U test was used to compare the internal and external test sets. Agreement of volumetry and radiomic features was assessed using the intraclass correlation coefficient (ICC). In total, 470 patients met the inclusion criteria (63.9±8.2 years; 376 males) and 20 patients were used for external validation (41±12 years; 13 males). DSC segmentation accuracy of the DCNN was similarly high between the internal (0.97±0.01) and external (0.96±0.03) test sets (p=0.28) and demonstrated robust segmentation performance on public testing (0.93±0.03). Agreement of liver volumetry was satisfactory in the internal (ICC, 0.99), external (ICC, 0.97), and public (ICC, 0.85) test sets. Radiomic features demonstrated excellent agreement in the internal (mean ICC, 0.98±0.04), external (mean ICC, 0.94±0.10), and public (mean ICC, 0.91±0.09) datasets. Automated liver segmentation yields robust and generalizable segmentation performance on MRI data and can be used for volumetry and radiomic feature extraction. Liver volumetry, anatomic localization, and extraction of quantitative imaging biomarkers require accurate segmentation, but manual segmentation is time-consuming. A deep convolutional neural network demonstrates fast and accurate segmentation performance on T1-weighted portal venous MRI. • This deep convolutional neural network yields robust and generalizable liver segmentation performance on internal, external, and public testing data. • Automated liver volumetry demonstrated excellent agreement with manual volumetry. • Automated liver segmentations can be used for robust and reproducible radiomic feature extraction.

Global practice patterns of preoperative image reconstruction for liver surgery

BackgroundThree-dimensional (3-D) liver modeling is used globally; however, its actual practice is limited to a few centers. This study aimed to assess practice patterns and barriers to the use of 3-D modeling among liver surgeons worldwide. MethodsA survey approved by the International Hepato-Pancreato-Biliary Association research council consisting of 27 questions was conducted using an online questionnaire. Incomplete responses were excluded. ResultsOf 235 respondents from 46 countries, 81.3% reported experience with 3-D modeling; however, only 21% used it in > 75% of cases. Surgeons using 3-D reconstruction were older (P = .025), worked more frequently at academic facilities (P = .007), and had more years of experience (P = .001), especially in minimally invasive liver surgery (MILS) (P = .038). In addition, 3-D rendering was performed by surgeons in 50.8% of cases. Liver volumetry was the most frequent indication (80.1%), and decreased postoperative complications were the main perceived benefit (53.6%). ConclusionsMore experience in liver surgery because of seniority, case volume, and openness to novel technology (MILS) is associated with a greater appreciation for the value of 3-D modeling. Our results suggest the need for senior surgeons to help early-career surgeons consider 3-D modeling for the reported benefit of reduced intra- and postoperative complications.

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.

Extracellular volume by dual-energy CT, hepatic reserve capacity scoring, CT volumetry, and transient elastography for estimating liver fibrosis

Our purpose was to compare the efficacy of liver and splenic volumetry (LV and SV), extracellular volume (ECV) on dual-layer spectral-detector CT scoring systems for estimating liver fibrosis (LF) in 45 patients with pathologically staged LF. ECV measured on CT value (HU-ECV), iodine density (ID-ECV), atomic number (Zeff-ECV), and electron density (ED-ECV), LV or SV/body surface area (BSA), albumin bilirubin grade (ALBI), model for end-stage liver disease (MELD) score, aspartate aminotransferase platelet ratio index (APRI), and fibrosis index based on the four factors (FIB-4) were recorded. Transient elastography was measured in 22 patients, and compared to ECV. No correlation was found between transient elastography and all ECVs. Area under the curve (AUC) for estimating F4 on transient elastography was 0.885 (95% CI 0.745–1.000). ALBI was weakly associated with LF (p = 0.451), while MELD (p < 0.001), APRI (p = 0.010), and FIB-4 (p = 0.010) were significantly associated with LF. SV/BSA had a higher AUC than MELD, APRI, and FIB-4 for estimating F4 (AUC = 0.815, 95% CI 0.63–0.999), but MELD (AUC = 0.799, 95% CI 0.634–0.965), APRI (AUC = 0.722, 95% CI 0.561–0.883), and FIB-4 (AUC = 0.741, 95% CI 0.582–0.899) had higher AUCs than ALBI. SV/BSA significantly contributed to differentiation for estimating F4; odds ratio (OR) was 1.304–1.353 (Reader 1–2; R1–R2), whereas MELD significantly contributed to the differentiation between F0–2 and F3–4; OR was 1.528–1.509 (R1–R2). AUC for SV/BSA and MELD combined was 0.877 (95% CI 0.748–1.000). In conclusion, SV/BSA allows for a higher estimation of liver cirrhosis (F4). MELD is more suitable for assessing severe LF (≥ F3–4). The combination of SV/BSA and MELD had a higher AUC than SV/BSA alone for liver cirrhosis (F4).

Role of intelligent/interactive qualitative and quantitative analysis-three-dimensional estimated model in donor-recipient size mismatch following deceased donor liver transplantation.

Donor-recipient size mismatch (DRSM) is considered a crucial factor for poor outcomes in liver transplantation (LT) because of complications, such as massive intraoperative blood loss (IBL) and early allograft dysfunction (EAD). Liver volumetry is performed routinely in living donor LT, but rarely in deceased donor LT (DDLT), which amplifies the adverse effects of DRSM in DDLT. Due to the various shortcomings of traditional manual liver volumetry and formula methods, a feasible model based on intelligent/interactive qualitative and quantitative analysis-three-dimensional (IQQA-3D) for estimating the degree of DRSM is needed. To identify benefits of IQQA-3D liver volumetry in DDLT and establish an estimation model to guide perioperative management. We retrospectively determined the accuracy of IQQA-3D liver volumetry for standard total liver volume (TLV) (sTLV) and established an estimation TLV (eTLV) index (eTLVi) model. Receiver operating characteristic (ROC) curves were drawn to detect the optimal cut-off values for predicting massive IBL and EAD in DDLT using donor sTLV to recipient sTLV (called sTLVi). The factors influencing the occurrence of massive IBL and EAD were explored through logistic regression analysis. Finally, the eTLVi model was compared with the sTLVi model through the ROC curve for verification. A total of 133 patients were included in the analysis. The Changzheng formula was accurate for calculating donor sTLV (P = 0.083) but not for recipient sTLV (P = 0.036). Recipient eTLV calculated using IQQA-3D highly matched with recipient sTLV (P = 0.221). Alcoholic liver disease, gastrointestinal bleeding, and sTLVi > 1.24 were independent risk factors for massive IBL, and drug-induced liver failure was an independent protective factor for massive IBL. Male donor-female recipient combination, model for end-stage liver disease score, sTLVi ≤ 0.85, and sTLVi ≥ 1.32 were independent risk factors for EAD, and viral hepatitis was an independent protective factor for EAD. The overall survival of patients in the 0.85 < sTLVi < 1.32 group was better compared to the sTLVi ≤ 0.85 group and sTLVi ≥ 1.32 group (P < 0.001). There was no statistically significant difference in the area under the curve of the sTLVi model and IQQA-3D eTLVi model in the detection of massive IBL and EAD (all P > 0.05). IQQA-3D eTLVi model has high accuracy in predicting massive IBL and EAD in DDLT. We should follow the guidance of the IQQA-3D eTLVi model in perioperative management.

CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma

ObjectivesPosthepatectomy liver failure (PHLF) is a severe complication of liver resection. We aimed to develop and validate a model based on extracellular volume (ECV) and liver volumetry derived from computed tomography (CT) for preoperative predicting PHLF in resectable hepatocellular carcinoma (HCC) patients.MethodsA total of 393 resectable HCC patients from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. A total of 281 patients from our hospital were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 100), and 112 patients from another hospital formed the external validation cohort. CT-derived ECV was measured on nonenhanced and equilibrium phase images, and liver volumetry was measured on portal phase images. The model is composed of independent predictors of PHLF. The under the receiver operator characteristic curve (AUC) and calibration curve were used to reflect the predictive performance and calibration of the model. Comparison of AUCs used the DeLong test.ResultsCT-derived ECV, measured future liver remnant (mFLR) ratio, and serum albumin were independent predictors for PHLF in resectable HCC patients. The AUC of the model was significantly higher than that of the ALBI score in the training cohort, internal validation cohort, and external validation cohort (all p < 0.001). The calibration curve of the model showed good consistency in the training cohort and the internal and external validation cohorts.ConclusionsThe novel model contributes to the preoperative prediction of PHLF in resectable HCC patients.Critical relevance statementThe novel model combined CT–derived extracellular volume, measured future liver remnant ratio, and serum albumin outperforms the albumin–bilirubin score for predicting posthepatectomy liver failure in patients with resectable hepatocellular carcinoma.Key points• CT-derived ECV correlated well with the fibrosis stage of the background liver.• CT-derived ECV and mFLR ratio were independent predictors for PHLF in HCC.• The AUC of the model was higher than the CT-derived ECV and mFLR ratio.• The model showed a superior predictive performance than that of the ALBI score.Graphical

Sarcopenic obesity may predict worse liver regeneration after right graft living donor liver transplantation.

Sarcopenic obesity impairs the outcome after liver transplantation. The effect of this on liver regeneration has not yet been studied. The aim of this study was to evaluate the potential effect of body composition changes on liver volume gain after living donor liver transplantation. We observed liver regeneration in 100 patients who underwent living donor liver transplantation using right lobe grafts (Segments V-VIII). Liver volumetry and body composition analysis were performed based on CT images with special software. The gain of liver volume was calculated between 2 points in time considering the absolute and percentage values: before surgery and early after surgery, with a median time of 10 days. Pearson's correlation and multivariate analysis using stepwise multiple regression were used to examine the potential correlation between body composition and liver volume gain. The liver volume increase was significantly negatively correlated with adipose tissue in the body stem ( r = -0.4, p < 0.001) and positively correlated with psoas mass ( r = 0.24, p = 0.02). These results correspond with those of the multiple regression analysis, which indicated adipose tissue (ß = -1.0, p < 0.001) and psoas mass (ß = 0.12, p < 0.001). The presence of malignancy as an indication for liver transplantation was another significant independent factor negatively affecting liver growth (ß = -13.1, p = 0.046). Sarcopenic obesity predicts an impaired liver volume increase after living donation. This could worsen the postoperative outcome. The role of alimentary interventions and exercises in improving body composition and thus postoperative outcome should be evaluated through prospective interventional studies.

The value of intraoperative dynamic liver function test ICG in predicting postoperative complications in patients undergoing staged hepatectomy: a pilot study

PurposeTo assess the predictive value of intraoperative indocyanine green (ICG) test in patients undergoing staged hepatectomy.MethodsWe analyzed intraoperative ICG measurements of future liver remnant (FLR), preoperative ICG, volumetry, and hepatobiliary scintigraphy in 15 patients undergoing associated liver partition and portal vein ligation for staged hepatectomy (ALPPS). Main endpoints were the correlation of intraoperative ICG values to postoperative complications (Comprehensive Complication Index (CCI®)) at discharge and 90 days after surgery, and to postoperative liver function.ResultsMedian intraoperative R15 (ICG retention rate at 15 min) correlated significantly with CCI® at discharge (p = 0.05) and with CCI® at 90 days (p = 0.0036). Preoperative ICG, volumetry, and scintigraphy did not correlate to postoperative outcome. ROC curve analysis revealed a cutoff value of 11.4 for the intraoperative R15 to predict major complications (Clavien-Dindo ≥ III) with 100% sensitivity and 63% specificity. No patient with R15 ≤ 11 developed major complications.ConclusionThis pilot study suggests that intraoperative ICG clearance determines the functional capacity of the future liver remnant more accurately than preoperative tests. This may further reduce the number of postoperative liver failures, even if it means intraoperative abortion of hepatectomy in individual cases.

Liver remnant (FLR) assessment after major hepatectomies with liver volumetry using CT and functionality evaluation with single photon emission (SPECT) computed tomography-hepatobiliary scintigraphy (HBS)

Liver remnant (FLR) assessment after major hepatectomies with liver volumetry using CT and functionality evaluation with single photon emission (SPECT) computed tomography-hepatobiliary scintigraphy (HBS)

Predicting Post-Hepatectomy Liver Failure in HCC Patients: A Review of Liver Function Assessment Based on Laboratory Tests Scores.

The assessment of liver function is crucial in predicting the risk of post-hepatectomy liver failure (PHLF) in patients undergoing liver resection, especially in cases of hepatocellular carcinoma (HCC) which is often associated with cirrhosis. There are currently no standardized criteria for predicting the risk of PHLF. Blood tests are often the first- and least invasive expensive method for assessing hepatic function. The Child-Pugh score (CP score) and the Model for End Stage Liver Disease (MELD) score are widely used tools for predicting PHLF, but they have some limitations. The CP score does not consider renal function, and the evaluation of ascites and encephalopathy is subjective. The MELD score can accurately predict outcomes in cirrhotic patients, but its predictive capabilities diminish in non-cirrhotic patients. The albumin-bilirubin score (ALBI) is based on serum bilirubin and albumin levels and allows the most accurate prediction of PHLF for HCC patients. However, this score does not consider liver cirrhosis or portal hypertension. To overcome this limitation, researchers suggest combining the ALBI score with platelet count, a surrogate marker of portal hypertension, into the platelet-albumin-bilirubin (PALBI) grade. Non-invasive markers of fibrosis, such as FIB-4 and APRI, are also available for predicting PHLF but they focus only on cirrhosis related aspects and are potentially incomplete in assessing the global liver function. To improve the predictive power of the PHLF of these models, it has been proposed to combine them into a new score, such as the ALBI-APRI score. In conclusion, blood test scores may be combined to achieve a better predictive value of PHLF. However, even if combined, they may not be sufficient to evaluate liver function and to predict PHLF; thus, the inclusion of dynamic and imaging tests such as liver volumetry and ICG r15 may be helpful to potentially improve the predictive capacity of these models.