Standard formula for liver volume in Middle Eastern Arabic adults

Evaluation of artificial-intelligence-based liver segmentation and its application for longitudinal liver volume measurement

BACKGROUND

Accurate liver-volume measurements from CT scans are essential for treatment planning, particularly in liver resection cases, to avoid postoperative liver failure. However, manual segmentation is time-consuming and prone to variability. Advancements in artificial intelligence (AI), specifically convolutional neural networks, have enhanced liver segmentation accuracy. We aimed to identify optimal CT phases for AI-based liver volume estimation and apply the model to track liver volume changes over time. We also evaluated temporal changes in liver volume in participants without liver disease.

METHODS

In this retrospective, single-center study, we assessed the performance of an open-source AI-based liver segmentation model previously reported, using non-contrast and dynamic CT phases. The accuracy of the model was compared with that of expert radiologists. The Dice similarity coefficient (DSC) was calculated across various CT phases, including arterial, portal venous, and non-contrast, to validate the model. The model was then applied to a longitudinal study involving 39 patients without liver disease (527 CT scans) to examine age-related liver volume changes over 5 to 20 years.

RESULTS

The model demonstrated high accuracy across all phases compared to manual segmentation. Among the CT phases, the highest DSC of 0.988 ± 0.010 was in the arterial phase. The intraclass correlation coefficients for liver volume were also high, exceeding 0.9 for contrast-enhanced phases and 0.8 for non-contrast CT. In the longitudinal study, the model indicated an annual decrease of 0.95%.

CONCLUSION

This model provides high accuracy in liver segmentation across various CT phases and offers insights into age-related liver volume reduction. Measuring changes in liver volume may help with the early detection of diseases and the understanding of pathophysiology.

Evaluation of Liver Volume Estimation Methods in Living Donor Liver Transplant: CT Volumetry vs MeVis, With Comparison of Open and Laparoscopic Surgery

BACKGROUND

Accurately assessing graft volume is crucial for donor and recipient safety in living donor liver transplantation. This can be performed using manual computed tomography volumetry (CTvol) or semiautomated methods (MeVis). We aimed to compare CTvol and MeVis in estimating the actual graft weight during LDLT, and analyse any differences in weight between laparoscopic and open donor hepatectomy.

METHODS

A retrospective study of living donors between 2015 and 2022 with complete imaging data was performed. Graft weights were estimated using (1) CT volumetry and (2) semiautomated MeVis software. The primary outcome was graft weight variance ([Predicted weight-Actual weight]/Predicted weight) × 100. The secondary outcome of interest was whether open or laparoscopic surgery affected graft weight variance.

RESULTS

Of the 33 donors, 52.6% were right liver without middle hepatic vein grafts. Nineteen donors (57.6%) underwent open hepatectomy. Both CTvol (r = 0.70; P < .001) and MeVis (r = 0.85; P < .001) showed strong correlation with actual graft weight. Weight variance using CTvol was -2.9% vs -15.3% (P = .04) for open vs laparoscopic, while the corresponding using MeVis was -0.9% vs -8.5% (P = .11). Actual graft-to-recipient weight ratio predicted by MeVis was similar between open and laparoscopic approaches (-0.01 vs 0.07; P = .12).

CONCLUSIONS

Both CT volumetry and MeVis showed strong correlation between predicted and actual graft weights. Laparoscopic hepatectomy showed greater variability in graft weight estimation using CT volumetry, but MeVis was similar across both open and laparoscopic surgery.

Prediction and management of small-for-size syndrome in living donor liver transplantation

Small-for-size syndrome (SFSS) remains a critical challenge in living donor liver transplantation (LDLT), characterized by graft insufficiency due to inadequate liver volume, leading to significant postoperative morbidity and mortality. As the global adoption of LDLT increases, the ability to predict and manage SFSS has become paramount in optimizing recipient outcomes. This review provides a comprehensive examination of the pathophysiology, risk factors, and strategies for managing SFSS across the pre-, intra-, and postoperative phases. The pathophysiology of SFSS has evolved from being solely volume-based to incorporating portal hemodynamics, now recognized as small-for-flow syndrome. Key risk factors include donor-related parameters like age and graft volume, recipient-related factors such as MELD score and portal hypertension, and intraoperative factors related to venous outflow and portal inflow modulation. Current strategies to mitigate SFSS include careful graft selection based on graft-to-recipient weight ratio and liver volumetry, surgical techniques to optimize portal hemodynamics, and novel interventions such as splenic artery ligation and hemiportocaval shunts. Pharmacological agents like somatostatin and terlipressin have also shown promise in modulating portal pressure. Advances in 3D imaging and artificial intelligence-based volumetry further aid in preoperative planning. This review emphasizes the importance of a multifaceted approach to prevent and manage SFSS, advocating for standardized definitions and grading systems. Through an integrated approach to surgical techniques, hemodynamic monitoring, and perioperative management, significant strides can be made in improving the outcomes of LDLT recipients. Further research is necessary to refine these strategies and expand the application of LDLT, especially in challenging cases involving small-for-size grafts.

Evaluation of Various Methods of Liver Measurement in Comparison to Volumetric Segmentation Based on Computed Tomography

Background: A reliable assessment of liver volume, necessary before transplantation, remains a challenge. Our work aimed to assess the differences in the evaluation and measurements of the liver between independent observers and compare different formulas calculating its volume in relation to volumetric segmentation. Methods: Eight researchers measured standard liver dimensions based on 105 abdominal computed tomography (CT) scans. Based on the results obtained, the volume of the liver was calculated using twelve different methods. An independent observer performed a volumetric segmentation of the livers based on the same CT examinations. Results: Significant differences were found between the formulas and in relation to volumetric segmentation, with the closest results obtained for the Heinemann et al. method. The measurements of individual observers differed significantly from one another. The observers also rated different numbers of livers as enlarged. Conclusions: Due to significant differences, despite its time-consuming nature, the use of volumetric liver segmentation in the daily assessment of liver volume seems to be the most accurate method.

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.

Artificial Intelligence for Improved Hepatosplenomegaly Diagnosis

Hepatosplenomegaly is commonly diagnosed by radiologists based on single dimension measurements and heuristic cut-offs. Volumetric measurements may be more accurate for diagnosing organ enlargement. Artificial intelligence techniques may be able to automatically calculate liver and spleen volume and facilitate more accurate diagnosis. After IRB approval, 2 convolutional neural networks (CNN) were developed to automatically segment the liver and spleen on a training dataset comprised of 500 single-phase, contrast-enhanced CT abdomen and pelvis examinations. A separate dataset of ten thousand sequential examinations at a single institution was segmented with these CNNs. Performance was evaluated on a 1% subset and compared with manual segmentations using Sorensen-Dice coefficients and Pearson correlation coefficients. Radiologist reports were reviewed for diagnosis of hepatomegaly and splenomegaly and compared with calculated volumes. Abnormal enlargement was defined as greater than 2 standard deviations above the mean. Median Dice coefficients for liver and spleen segmentation were 0.988 and 0.981, respectively. Pearson correlation coefficients of CNN-derived estimates of organ volume against the gold-standard manual annotation were 0.999 for the liver and spleen (P < 0.001). Average liver volume was 1556.8 ± 498.7 cc and average spleen volume was 194.6 ± 123.0 cc. There were significant differences in average liver and spleen volumes between male and female patients. Thus, the volume thresholds for ground-truth determination of hepatomegaly and splenomegaly were determined separately for each sex. Radiologist classification of hepatomegaly was 65% sensitive, 91% specific, with a positive predictive value (PPV) of 23% and an negative predictive value (NPV) of 98%. Radiologist classification of splenomegaly was 68% sensitive, 97% specific, with a positive predictive value (PPV) of 50% and a negative predictive value (NPV) of 99%. Convolutional neural networks can accurately segment the liver and spleen and may be helpful to improve radiologist accuracy in the diagnosis of hepatomegaly and splenomegaly.

Post Living Donor Liver Transplantation Small-for-size Syndrome: Definitions, Timelines, Biochemical, and Clinical Factors for Diagnosis: Guidelines From the ILTS-iLDLT-LTSI Consensus Conference

BACKGROUND

When a partial liver graft is unable to meet the demands of the recipient, a clinical phenomenon, small-for-size syndrome (SFSS), may ensue. Clear definition, diagnosis, and management are needed to optimize transplant outcomes.

METHODS

A Consensus Scientific committee (106 members from 21 countries) performed an extensive literature review on specific aspects of SFSS, recommendations underwent blinded review by an independent panel, and discussion/voting on the recommendations occurred at the Consensus Conference.

RESULTS

The ideal graft-to-recipient weight ratio of ≥0.8% (or graft volume standard liver volume ratio of ≥40%) is recommended. It is also recommended to measure portal pressure or portal blood flow during living donor liver transplantation and maintain a postreperfusion portal pressure of <15 mm Hg and/or portal blood flow of <250 mL/min/100 g graft weight to optimize outcomes. The typical time point to diagnose SFSS is the postoperative day 7 to facilitate treatment and intervention. An objective 3-grade stratification of severity for protocolized management of SFSS is proposed.

CONCLUSIONS

The proposed grading system based on clinical and biochemical factors will help clinicians in the early identification of patients at risk of developing SFSS and institute timely therapeutic measures. The validity of this newly created grading system should be evaluated in future prospective studies.

The effect of hepatic steatosis on liver volume determined by proton density fat fraction and deep learning-measured liver volume

OBJECTIVES

We aimed to evaluate the effect of hepatic steatosis (HS) on liver volume and to develop a formula to estimate lean liver volume correcting the HS effect.

METHODS

This retrospective study included healthy adult liver donors who underwent gadoxetic acid-enhanced MRI and proton density fat fraction (PDFF) measurement from 2015 to 2019. The degree of HS was graded at 5% PDFF intervals from grade 0 (no HS; PDFF < 5.5%). Liver volume was measured with hepatobiliary phase MRI using deep learning algorithm, and standard liver volume (SLV) was calculated as the reference lean liver volume. The association between liver volume and SLV ratio with PDFF grades was evaluated using Spearman's correlation (ρ). The effect of PDFF grades on liver volume was evaluated using the multivariable linear regression model.

RESULTS

The study population included 1038 donors (mean age, 31 ± 9 years; 689 men). Mean liver volume to SLV ratio increased according to PDFF grades (ρ = 0.234, p < 0.001). The multivariable analysis indicated that SLV (β = 1.004, p < 0.001) and PDFF grade*SLV (β = 0.044, p < 0.001) independently affected liver volume, suggesting a 4.4% increase in liver volume per one-point increment in the PDFF grade. PDFF-adjusted lean liver volume was estimated using the formula, liver volume/[1.004 + 0.044 × PDFF grade]. The mean estimated lean liver volume to SLV ratio approximated to one for all PDFF grades, with no significant association with PDFF grades (p = 0.851).

CONCLUSION

HS increases liver volume. The formula to estimate lean liver volume may be useful to adjust for the effect of HS on liver volume.

KEY POINTS

• Hepatic steatosis increases liver volume. • The presented formula to estimate lean liver volume using MRI-measured proton density fat fraction and liver volume may be useful to adjust for the effect of hepatic steatosis on measured liver volume.

Achieving clinically optimal balance between accuracy and simplicity of a formula for manual use: Development of a simple formula for estimating liver graft weight with donor anthropometrics

In developing a formula for manual use in clinical settings, simplicity is as important as accuracy. Whole-liver (WL) mass is often estimated using demographic and anthropometric information to calculate the standard liver volume or recommended graft volume in liver transplantation. Multiple formulas for estimating WL mass have been reported, including those with multiple independent variables. However, it is unknown whether multivariable models lead to clinically meaningful improvements in accuracy over univariable models. Our goal was to quantitatively define clinically meaningful improvements in accuracy, which justifies an additional independent variable, and to identify an estimation formula for WL graft weight that best balances accuracy and simplicity given the criterion. From the Japanese Liver Transplantation Society registry, which contains data on all liver transplant cases in Japan, 129 WL donor-graft pairs were extracted. Among the candidate models, those with the smallest cross-validation (CV) root-mean-square error (RMSE) were selected, penalizing model complexity by requiring more complex models to yield a ≥5% decrease in CV RMSE. The winning model by voting with random subsets was fitted to the entire dataset to obtain the final formula. External validity was assessed using CV. A simple univariable linear regression formula using body weight (BW) was obtained as follows: WL graft weight [g] = 14.8 × BW [kg] + 439.2. The CV RMSE (g) and coefficient of determination (R2) were 195.2 and 0.548, respectively. In summary, in the development of a simple formula for manually estimating WL weight using demographic and anthropometric variables, a clinically acceptable trade-off between accuracy and simplicity was quantitatively defined, and the best model was selected using this criterion. A univariable linear model using BW achieved a clinically optimal balance between simplicity and accuracy, while one using body surface area performed similarly.

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.

Shulan Estimation Model: A New Formula for Estimation of Standard Liver Volume In Chinese Adults

BACKGROUND

To establish a new and accurate model for standard liver volume (SLV) estimation and graft size prediction in liver transplantation for Chinese adults.

METHODS

In this study, the data of morphologic indices and liver volume (LV) were retrospectively obtained on 507 cadaveric liver transplantation donors between June 2017 and September 2020 in Shulan (Hangzhou) Hospital. Linear regression analysis was performed to evaluate the impact of each parameter and develop a new SLV formula. The new formula was then validated prospectively on 97 donors between October 2020 and June 2021, and the prediction accuracy was compared with previous formulas.

RESULTS

The average LV in all subjects was 1445.68 ± 309.94 mL. Body weight (BW) showing the strongest correlation (r = 0.453, P < .001). By stepwise multiple linear regression analysis, BW and age were the only 2 independent correlation factors for LV. Shulan estimation model derived: SLV (mL) = 13.266 × BW (kg) - 4.693 × age + 797.16 (R2 = 0.236, P < .001). In the validation cohort, our new model achieved no significant differences between the estimated SLV and the actual LV (P > .05), and showed the lowest mean percentage error of 0.33%. The proportions of estimated SLV within the actual LV ± 20%, ± 15%, and ± 10% percentage errors were 69.1%, 55.7%, and 40.2%, respectively.

DISCUSSION

The Shulan SLV estimation model predicted LV more accurately than previous formulas on Chinese adults, which could serve as a simple screening tool during the initial assessment of graft volume for potential donors.

A new formula for estimation of standard liver volume using liver height and thoracic width

Purpose

Precise estimation of the standard liver volume (SLV) is crucial in decision making regarding major hepatectomy and living donor liver transplantation. This study aimed to propose an accurate and efficient formula for estimating the SLV in the Korean population.

Methods

We created a regression model for SLV estimation using a data set of 230 Korean patients with healthy livers. The proposed model was cross validated using a different data set of 37 patients with healthy livers. The total liver volume (TLV), except for the volume of liver blood vessels, was measured through computed tomography volumetry as the dependent variable. Various anthropometric variables, liver height (LH), thoracic width (TW), age, and sex (0, female and 1, male) were considered as candidates for independent variables. We conducted stepwise regression analysis to identify variables to be included in the proposed model.

Results

A new formula was established; SLV = −1,275 + 9.85 × body weight (BW, kg) + 19.95 × TW (cm) + 7.401 × LH (mm). The proposed formula showed the best performance among existing formulas over the cross-validation data set.

Conclusion

The proposed formula derived using BW, TW, and LH estimated the TLV in the cross-validation data set more accurately than existing formulas.

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.

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (n = 1960) or renal donor evaluation (n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Standard liver weight model in adult deceased donors with fatty liver: A prospective cohort study

BACKGROUND

Standard liver weight (SLW) is frequently used in deceased donor liver transplantation to avoid size mismatches with the recipient. However, some deceased donors (DDs) have fatty liver (FL). A few studies have reported that FL could impact liver size. To the best of our knowledge, there are no relevant SLW models for predicting liver size.

AIM

To demonstrate the relationship between FL and total liver weight (TLW) in detail and present a related SLW formula.

METHODS

We prospectively enrolled 212 adult DDs from West China Hospital of Sichuan University from June 2019 to February 2021, recorded their basic information, such as sex, age, body height (BH) and body weight (BW), and performed abdominal ultrasound (US) and pathological biopsy (PB). The chi-square test and kappa consistency score were used to assess the consistency in terms of FL diagnosed by US relative to PB. Simple linear regression analysis was used to explore the variables related to TLW. Multiple linear regression analysis was used to formulate SLW models, and the root mean standard error and interclass correlation coefficient were used to test the fitting efficiency and accuracy of the model, respectively. Furthermore, the optimal formula was compared with previous formulas.

RESULTS

Approximately 28.8% of DDs had FL. US had a high diagnostic ability (sensitivity and specificity were 86.2% and 92.9%, respectively; kappa value was 0.70, P < 0.001) for livers with more than a 5% fatty change. Simple linear regression analysis showed that sex (R2, 0.226; P < 0.001), BH (R2, 0.241; P < 0.001), BW (R2, 0.441; P < 0.001), BMI (R2, 0.224; P < 0.001), BSA (R2, 0.454; P < 0.001) and FL (R2, 0.130; P < 0.001) significantly impacted TLW. In addition, multiple linear regression analysis showed that there was no significant difference in liver weight between the DDs with no steatosis and those with steatosis within 5%. Furthermore, in the context of hepatic steatosis, TLW increased positively (non-linear); compared with the TLW of the non-FL group, the TLW of the groups with hepatic steatosis within 5%, between 5% and 20% and more than 20% increased by 0 g, 90 g, and 340 g, respectively. A novel formula, namely, -348.6 + (110.7 x Sex [0 = Female, 1 = Male]) + 958.0 x BSA + (179.8 x FLUS [0 = No, 1 = Yes]), where FL was diagnosed by US, was more convenient and accurate than any other formula for predicting SLW.

CONCLUSION

FL is positively correlated with TLW. The novel formula deduced using sex, BSA and FLUS is the optimal formula for predicting SLW in adult DDs.

Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis

Background Reference intervals guiding volumetric assessment of the liver and spleen have yet to be established. Purpose To establish population-based and personalized reference intervals for liver volume, spleen volume, and liver-to-spleen volume ratio (LSVR). Materials and Methods This retrospective study consecutively included healthy adult liver donors from 2001 to 2013 (reference group) and from 2014 to 2016 (healthy validation group) and patients with viral hepatitis from 2007 to 2017. Liver volume, spleen volume, and LSVR were measured with CT by using a deep learning algorithm. In the reference group, the reference intervals for the volume indexes were determined by using the population-based (ranges encompassing the central 95% of donors) and personalized (quantile regression modeling of the 2.5th and 97.5th percentiles as a function of age, sex, height, and weight) approaches. The validity of the reference intervals was evaluated in the healthy validation group and the viral hepatitis group. Results The reference and healthy validation groups had 2989 donors (mean age ± standard deviation, 30 years ± 9; 1828 men) and 472 donors (mean age, 30 years ± 9; 334 men), respectively. The viral hepatitis group had 158 patients (mean age, 48 years ± 12; 95 men). The population-based reference intervals were 824.5-1700.0 cm³ for liver volume, 81.1-322.0 cm³ for spleen volume, and 3.96-13.78 for LSVR. Formulae and a web calculator (https://i-pacs.com/calculators) were presented to calculate the personalized reference intervals. In the healthy validation group, both the population-based and personalized reference intervals were used to classify the volume indexes of 94%-96% of the donors as falling within the reference interval. In the viral hepatitis group, when compared with the population-based reference intervals, the personalized reference intervals helped identify more patients with volume indexes outside the reference interval (liver volume, 21.5% [34 of 158] vs 13.3% [21 of 158], P = .01; spleen volume, 29.1% [46 of 158] vs 22.2% [35 of 158], P = .01; LSVR, 35.4% [56 of 158] vs 26.6% [42 of 158], P < .001). Conclusion Reference intervals derived from a deep learning approach in healthy adults may enable evidence-based assessments of liver and spleen volume in clinical practice. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Ringl in this issue.

Analysis of factors associated with discrepancies between predicted and observed liver weight in liver transplantation

BACKGROUND

Even using predictive formulas based on anthropometrics in about 30% of subjects, liver weight (LW) cannot be predicted with a ≤20% margin of error. We aimed to identify factors associated with discrepancies between predicted and observed LW.

METHODS

In 500 consecutive liver grafts, we tested LW predictive performance using 17 formulas based on anthropometric characteristics. Hashimoto's formula (961.3 × BSA_D-404.8) was associated with the lowest mean absolute error and used to predict LW for the entire cohort. Clinical factors associated with a ≥20% margin of error were identified in a multivariable analysis after propensity score matching (PSM) of donors with similar anthropometric characteristics.

RESULTS

The total LW was underestimated with a ≥20% margin of error in 53/500 (10.6%) donors and overestimated in 62/500 (12%) donors. After PSM analysis, ages ≥ 65, (OR = 3.21; CI95% = 1.63-6.31; P = .0007), age ≤ 30 years, (OR = 2.92; CI95% = 1.15-7.40; P = .02), and elevated gamma-glutamyltransferase (GGT) levels (OR = 0.98; CI95% = 0.97-0.99; P = .006), influenced the risk of LW overestimation. Age ≥ 65 years, (OR = 5.98; CI95% = 2.28-15.6; P = .0002), intensive care unit (ICU) stay with ventilation > 7 days, (OR = 0.32; CI95% = 0.12-0.85; P = .02) and waist circumference increase (OR = 1.02; CI95% = 1.00-1.04; P = .04) were factors associated with LW underestimation.

CONCLUSIONS

Increased waist circumference, age, prolonged ICU stay with ventilation, elevated GGT were associated with an increase in the margin of error in LW prediction. These factors and anthropometric characteristics could help transplant surgeons during the donor-recipient matching process.

Comparison of skeletal muscle index-based formula and body surface area-based formula for calculating standard liver volume

Backgrounds/Aims

Formula-derived standard liver volume (SLV) has been clinically used for living donor liver transplantation and hepatic resection. The majority of currently available SLV formulae are based on body surface are (BSA). However, they often show a wide range of error. Skeletal muscle index measured at the third lumbar vertebra level (L3SMI) appears to reflect lean body mass. The objective of this study was to compare the accuracy of L3SMI-based formula and BSA-based formula for calculating SLV.

Methods

The study cohort was 500 hundred living liver donors who underwent surgery between January 2010 and December 2013. Computed tomography images were used for liver volumetry and skeletal muscle area measurement.

Results

The study cohort included 250 male and 250 female donors. Their age, BSA, L3SMI, and body mass index were 26.8±8.7 years, 1.68±0.16 m², 45.6±9.0 cm²/m², and 21.7±2.5 kg/m², respectively. The BSA-based SLV formula was “SLV (ml)=−362.3+901.5×BSA (m²) (r=0.71, r²=0.50, p<0.001)”. The L3SMI-based SLV formula was “SLV (ml)=471.9+14.9×L3SMI (cm²/m²) (r=0.65, r²=0.42, p<0.001)”. Correlation coefficients were similar in subgroup analyses with 250 male donors and 250 female donors. There was a crude correlation between L3SMI and body mass index (r=0.51, r²=0.27, p<0.001).

Conclusions

The results of this study suggest that SLV calculation with L3SMI-based formula does not appear to be superior to the currently available BSA-based formulae.

Predicting the available space for liver transplantation in cirrhotic patients: a computed tomography-based volumetric study

BACKGROUND

Anthropometric parameters (weight, height) are usually used for quick matching between two individuals (donor and recipient) in liver transplantation (LT). This study aimed to evaluate clinical factors influencing the overall available space for implanting a liver graft in cirrhotic patients.

METHODS

In a cohort of 275 cirrhotic patients undergoing LT, we calculated the liver volume (LV), cavity volume (CV), which is considered the additional space between the liver and the right hypocondrium, and the overall volume (OV = LV + CV) using a computed tomography (CT)-based volumetric system. We then chose the formula based on anthropometric parameters that showed the best predictive value for LV. This formula was used to predict the OV in the same population. Factors influencing OV variations were identified by multivariable logistic analysis.

RESULTS

The Hashimoto formula (961.3 × BSA_D-404.8) yielded the lowest median absolute percentage error (21.7%) in predicting the LV. The median LV was 1531 ml. One-hundred eighty-five patients (67.2%) had a median CV of 1156 ml (range: 70-7006), and the median OV was 2240 ml (range: 592-8537). Forty-nine patients (17%) had an OV lower than that predicted by the Hashimoto formula. Independent factors influencing the OV included the number of portosystemic shunts, right anteroposterior abdominal diameter, model for end-stage liver disease (MELD) score > 25, high albumin value, and BMI > 30.

CONCLUSIONS

Additional anthropometric characteristics (right anteroposterior diameter, body mass index) clinical (number of portosystemic shunts), and biological (MELD, albumin) factors might influence the overall volume available for liver graft implantation. Knowledge of these factors might be helpful during the donor-recipient matching.

A Single-Center Experience in Portal Flow Augmentation in Liver Transplantation With Prior Large Spontaneous Splenorenal Shunt

Large portosystemic shunts may cause portal steal syndrome in liver transplantation (LT). Because of the possible devastating consequences of the syndrome, the authors recommend perioperative management of these large shunts. Fourteen adult recipients who underwent portal flow augmentation, including left renal vein ligation (LRVL), renoportal anastomosis (RPA), shunt ligation (SL), and splenic vein ligation (SVL) for large spontaneous splenorenal shunt (SSRS), are included in this study, and the results were analyzed. A total of 13 patients had a large SSRS, and in 1 patient, the large shunt was placed between the superior mesenteric vein and the right renal vein. LDLT was performed in 13 patients. LRVL (n = 5), SVL (n = 6), RPA (n = 2), SL (n = 1) were performed to the patients as graft inflow augmentation. The graft-recipient weight ratios (GRWR) were less than 0.8% in 5 patients (35.7%): 2 had LRVL, and 3 had SVL. Small-for-size syndrome (SFSS) occurred only in these 2 patients with LRVL (GRWR ≤0.8%) and, splenic artery ligation was performed for graft inflow modulation. No mortality or serious complications were reported during follow-up. We consider that in patients with large SSRS and small-for-size grafts, SVL can be performed safely and with satisfactory outcomes.

Non-invasive prediction model for high-risk esophageal varices in the Chinese population

BACKGROUND

There are two types of esophageal varices (EVs): high-risk EVs (HEVs) and low-risk EVs, and HEVs pose a greater threat to patient life than low-risk EVs. The diagnosis of EVs is mainly conducted by gastroscopy, which can cause discomfort to patients, or by non-invasive prediction models. A number of non-invasive models for predicting EVs have been reported; however, those that are based on the formula for calculation of liver and spleen volume in HEVs have not been reported.

AIM

To establish a non-invasive prediction model based on the formula for liver and spleen volume for predicting HEVs in patients with viral cirrhosis.

METHODS

Data from 86 EV patients with viral cirrhosis were collected. Actual liver and spleen volumes of the patients were determined by computed tomography, and their calculated liver and spleen volumes were calculated by standard formulas. Other imaging and biochemical data were determined. The impact of each parameter on HEVs was analyzed by univariate and multivariate analyses, the data from which were employed to establish a non-invasive prediction model. Then the established prediction model was compared with other previous prediction models. Finally, the discriminating ability, calibration ability, and clinical efficacy of the new model was verified in both the modeling group and the external validation group.

RESULTS

Data from univariate and multivariate analyses indicated that the liver-spleen volume ratio, spleen volume change rate, and aspartate aminotransferase were correlated with HEVs. These indexes were successfully used to establish the non-invasive prediction model. The comparison of the models showed that the established model could better predict HEVs compared with previous models. The discriminating ability, calibration ability, and clinical efficacy of the new model were affirmed.

CONCLUSION

The non-invasive prediction model for predicting HEVs in patients with viral cirrhosis was successfully established. The new model is reliable for predicting HEVs and has clinical applicability.

Risk factors of posthepatectomy liver failure for perihilar cholangiocarcinoma: Risk score and significance of future liver remnant volume-to-body weight ratio

BACKGROUND

Surgery for perihilar cholangiocarcinoma (PHCC) is associated with high morbidity. This study aimed to investigate the clinical value of the future liver remnant volume-to-body weight (FLRV/BW) and propose a risk score for predicting the risk of patients with PHCC developing posthepatectomy liver failure (PHLF).

METHODS

This study included 348 patients who underwent major hepatectomy with bile duct resection for PHCC during 2008-2015 at a single center in Korea and they were retrospectively analyzed.

RESULTS

Clinically relevant PHLF was noted in 40 patients (11.4%). The area under the curve (AUC) for FLRV/BW was not significantly different from that for FLRV/total liver volume (P = .803) or indocyanine green clearance of the future liver remnant (P = .629) in terms of predicting PHLF. On multivariate analysis, predictors of PHLF (P < .05) were male sex, albumin less than 3.5 g/dL, preoperative cholangitis, portal vein resection, FLRV/BW less than 0.5%, and FLRV/BW 0.5% to 0.75%. These variables were included in the risk score that showed good discrimination (AUC, 0.853; 95% CI, 0.802-0.904). It will help rank patients into three risk subgroups with a predicted liver failure incidence of 4.75%, 18.73%, and 51.58%, respectively.

CONCLUSIONS

FLRV/BW is a comparable risk prediction factor of PHLF and the proposed risk score can help to predict the risk of planned surgery in PHCC.

Accuracy of estimated total liver volume formulas before liver resection

BACKGROUND

Future remnant liver volume is used to predict the risk for liver failure in patients who will undergo major liver resection. Formulas to estimate total liver volume based on biometric data are widely used to calculate future remnant liver volume; however, it remains unclear which formula is most accurate. This study evaluated published estimate total liver volume formulas to determine which formula best predicts the actual future remnant liver volume based on measurements in a large number of patients who underwent associating liver partition and portal vein ligation for staged hepatectomy surgery.

METHODS

All patients with complete liver volume data in the associating liver partition and portal vein ligation for staged hepatectomy registry were included in this study. Estimate total liver volume and estimated future remnant liver volume were calculated for 16 published formulas. The median over- or underestimation compared with actual measured volumes were determined for estimate total liver volume and future remnant liver volume. The proportion of patients with an under- or overestimated future remnant liver volume for each formula were compared with each other using a 25% cut-off for each formula.

RESULTS

Among 529 studied patients, the formulas ranged from a 19% underestimation to a 63% overestimation of estimate total liver volume. Estimation of future remnant liver volume lead to a 10% underestimation to a 5% overestimation among the formulas. Of all studied formulas, the Vauthey1 formula was the most accurate, generating underestimation of future remnant liver volume in 20% and overestimation of future remnant liver volume in 6% of patients.

CONCLUSION

Validation of 16 published total liver volume formulas in a multicenter international cohort of 529 patients that underwent staged hepatectomy revealed that the Vauthey formula (estimate total liver volume = 18.51 × body weight + 191.8) provides the most accurate prediction of the actual future remnant liver volume.

Estimation of Standard Liver Volume Using CT Volume, Body Composition, and Abdominal Geometry Measurements

PURPOSE

The present study developed formulas for estimation of standard liver volume (SLV) with high accuracy for the Korean population.

MATERIALS AND METHODS

SLV estimation formulas were established using gender-balanced and gender-unbalanced measurements of anthropometric variables, body composition variables, and abdominal geometry of healthy Koreans (n=790). Total liver volume excluding blood volume, was measured based on CT volumetry.

RESULTS

SLV estimation formulas as preferred in various conditions of data availability were suggested in the present study. The suggested SLV estimation formulas in the present study were found superior to existing formulas, with an increased accuracy of 4.0-217.5 mL for absolute error and 0.2-18.7% for percentage of absolute error.

CONCLUSION

SLV estimation formulas using gender-balanced measurements showed better performance than those using gender-unbalanced measurements. Inclusion of body composition and abdominal geometry variables contributed to improved performance of SLV estimation.

Assessing the Non-tumorous Liver: Implications for Patient Management and Surgical Therapy

INTRODUCTION

Hepatic resection is performed for various benign and malignant liver tumors. Over the last several decades, there have been improvements in the surgical technique and postoperative care of patients undergoing liver surgery. Despite this, liver failure following an extended hepatic resection remains a critical potential postoperative complication. Patients with underlying parenchymal liver diseases are at particular risk of liver failure due to impaired liver regeneration with an associated mortality risk as high as 60 to 90%. In addition, live donor liver transplantation requires a thorough presurgical assessment of the donor liver to minimize the risk of postoperative complications.

RESULTS AND CONCLUSION

Recently, cross-sectional imaging assessment of diffuse liver diseases has gained momentum due to its ability to provide both anatomical and functional assessments of normal and abnormal tissues. Various imaging techniques are being employed to assess diffuse liver diseases including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US). MRI has the ability to detect abnormal intracellular and molecular processes and tissue architecture. CT has a high spatial resolution, while US provides real-time imaging, is inexpensive, and readily available. We herein review current state-of-the-art techniques to assess the underlying non-tumorous liver. Specifically, we summarize current approaches to evaluating diffuse liver diseases including fatty liver alcoholic or non-alcoholic (NAFLD, AFLD), hepatic fibrosis (HF), and iron deposition (ID) with a focus on advanced imaging techniques for non-invasive assessment along with their implications for patient management. In addition, the role of and techniques to assess hepatic volume in hepatic surgery are discussed.

A Formula to Calculate Standard Liver Volume Using Thoracoabdominal Circumference

Background

With the use of split liver grafts as well as living donor liver transplantation (LDLT) it is imperative to know the minimum graft volume to avoid complications. Most current formulas to predict standard liver volume (SLV) rely on weight-based measures that are likely inaccurate in the setting of cirrhosis. Therefore, we sought to create a formula for estimating SLV without weight-based covariates.

Methods

LDLT donors underwent computed tomography scan volumetric evaluation of their livers. An optimal formula for calculating SLV using the anthropomorphic measure thoracoabdominal circumference (TAC) was determined using leave-one-out cross-validation. The ability of this formula to correctly predict liver volume was checked against other existing formulas by analysis of variance. The ability of the formula to predict small grafts in LDLT was evaluated by exact logistic regression.

Results

The optimal formula using TAC was determined to be SLV = (TAC × 3.5816) − (Age × 3.9844) − (Sex × 109.7386) − 934.5949. When compared to historic formulas, the current formula was the only one which was not significantly different than computed tomography determined liver volumes when compared by analysis of variance with Dunnett posttest. When evaluating the ability of the formula to predict small for size syndrome, many (10/16) of the formulas tested had significant results by exact logistic regression, with our formula predicting small for size syndrome with an odds ratio of 7.94 (95% confidence interval, 1.23-91.36; P = 0.025).

Conclusion

We report a formula for calculating SLV that does not rely on weight-based variables that has good ability to predict SLV and identify patients with potentially small grafts.

A new formula for estimation of standard liver volume using computed tomography-measured body thickness

The objective of this article is to derive a more accurate and easy-to-use formula for finding estimated standard liver volume (ESLV) using novel computed tomography (CT) measurement parameters. New formulas for ESLV have been emerging that aim to improve the accuracy of estimation. However, many of these formulas contain body surface area measurements and logarithms in the equations that lead to a more complicated calculation. In addition, substantial errors in ESLV using these old formulas have been shown. An improved version of the formula for ESLV is needed. This is a retrospective cohort of consecutive living donor liver transplantations from 2005 to 2016. Donors were randomly assigned to either the formula derivation or validation groups. Total liver volume (TLV) measured by CT was used as the reference for a linear regression analysis against various patient factors. The derived formula was compared with the existing formulas. There were 722 patients (197 from the derivation group, 164 from the validation group, and 361 from the recipient group) involved in the study. The donor's body weight (odds ratio [OR], 10.42; 95% confidence interval [CI], 7.25-13.60; P < 0.01) and body thickness (OR, 2.00; 95% CI, 0.36-3.65; P = 0.02) were found to be independent factors for the TLV calculation. A formula for TLV (cm3 ) was derived: 2 × thickness (mm) + 10 × weight (kg) + 190 with R2 0.48, which was the highest when compared with the 4 other most often cited formulas. This formula remained superior to other published formulas in the validation set analysis (R2 , 5.37; interclass correlation coefficient, 0.74). Graft weight/ESLV values calculated by the new formula were shown to have the highest correlation with delayed graft function (C-statistic, 0.79; 95% CI, 0.69-0.90; P < 0.01). The new formula (2 × thickness + 10 × weight + 190) represents the first study proposing the use of CT-measured body thickness which is novel, easy to use, and the most accurate for ESLV. Liver Transplantation 23 1113-1122 2017 AASLD.

New formula for predicting standard liver volume in Chinese adults

AIM

To obtain a reference range of morphological indices and establish a formula to accurately predict standard liver volume (SLV) in Chinese adults.

METHODS

Computed tomography (CT)-estimated total liver volume (CTLV) was determined in 369 Chinese adults. Age, sex, body weight, body height, body mass index, and body surface area (BSA) were recorded using CT. Total splenic volume, portal venous diameter (PVD), splenic venous diameter (SVD), and portal venous cross-sectional area (PVCSA) were also measured by CT. Stepwise multiple linear regression analysis was performed to evaluate the impact of each parameter on CTLV and to develop a new SLV formula. The accuracy of the new formula was compared with the existing formulas in a validation group.

RESULTS

The average CTLV was 1205.41 ± 257.53 cm³ (range, 593.80-2250.10 cm³). The average of PVD, SVD and PVCSA was 9.34 ± 1.51 mm, 7.40 ± 1.31 mm and 173.22 ± 48.11 mm², respectively. The CT-estimated splenic volume of healthy adults varied markedly (range, 46.60-2892.30 cm³). Sex, age, body height, body weight, body mass index, and BSA were significantly correlated with CTLV. BSA showed the strongest correlation (r = 0.546, P < 0.001), and was used to establish a new model for calculating SLV: SLV (cm³) = 758.259 × BSA (m²)-124.272 (R² = 0.299, P < 0.001). This formula also predicted CTLV more accurately than the existing formulas, but overestimated CTLV in elderly subjects > 70 years of age, and underestimated liver volume when CTLV was > 1800 cm³.

CONCLUSION

Our new BSA-based formula is more accurate than other formulas in estimating SLV in Chinese adults.

Estimation of liver volume in the western Indian population

BACKGROUND

A number of formulae to estimate standard liver volume (SLV) exist. However, studies have shown that only certain formulae are applicable to a particular patient population, whereas the other formulae have not been accurate in estimating the SLV. Aim of this study was to assess which formula is most accurate in estimating SLV in the western Indian population.

METHODS

Data for donors of living donor liver transplantation from September 2014 to July 2015 was analyzed. Liver volumes were measured using computed tomography volumetry (CTV). SLV was calculated using formulae by the currently existing formulae. The mean SLV and CTV, percentage error in the SLV, and the correlation between SLV and CTV were calculated.

RESULTS

Fifty-nine healthy subjects underwent donor hepatectomy [28 (47.5 %) males]. The mean age, mean body mass index (BMI), and mean body surface area (BSA) were 31.8 ± 8.8 years, 23.8 ± 3.7 kg/m(2), and 1.6 ± 0.4, respectively. Mean CTV was 1178 ± 246.8 mL. Difference between mean SLV and mean CTV ranged from -133.5 (±189) mL to 632.2 (±190.2) mL. Mean SLV was significantly different from CTV by all the formulae except Urata. Percentage of population whose SLV was within 15 % of the mean CTV ranged from 1.7 % to 67.8 %, with the highest percentage obtained by using Fu-Gui's formula. However, there was wide inter-individual variation on scatter plots between SLV and CTV by both these formulae.

CONCLUSION

Currently existing formulae were not accurate in estimating SLV in our population.

Calculation of standard liver volume in Korean adults with analysis of confounding variables

Backgrounds/Aims

Standard liver volume (SLV) is an important parameter that has been used as a reference value to estimate the graft matching in living donor liver transplantation (LDLT). This study aimed to determine a reliable SLV formula for Korean adult patients as compared with the 15 SLV formulae from other studies and further estimate SLV formula by gender and body mass index (BMI).

Methods

Computed tomography liver volumetry was performed in 1,000 living donors for LDLT and regression formulae for SLV was calculated. Individual donor data were applied to the 15 previously published SLV formulae, as compared with the SLV formula derived in this study. Analysis for confounding variables of BMI and gender was also performed.

Results

Two formulae, "SLV (ml)=908.204×BSA-464.728" with DuBois body surface area (BSA) formula and "SLV (ml)=893.485×BSA-439.169" with Monsteller BSA formula, were derived by using the profiles of the 1,000 living donors included in the study. Comparison with other 15 other formulae, all except for Chouker formula showed the mean volume percentage errors of 4.8-5.4%. The gender showed no significant effect on total liver volume (TLV), but there was a significant increase in TLV as BMI increased.

Conclusions

Our study suggested that most SLV formulae showed a crudely applicable range of SLV estimation for Korean adults. Considering the volume error in estimating SLV, further SLV studies with larger population from multiple centers should be performed to enhance its predictability. Our results suggested that classifying SLV formulae by BMI and gender is unnecessary.

A new formula for calculating standard liver volume for living donor liver transplantation without using body weight

BACKGROUND & AIMS

The standard liver volume (SLV) is widely used in liver surgery, especially for living donor liver transplantation (LDLT). All the reported formulas for SLV use body surface area or body weight, which can be influenced strongly by the general condition of the patient.

METHODS

We analyzed the liver volumes of 180 Japanese donor candidates and 160 Swiss patients with normal livers to develop a new formula. The dataset was randomly divided into two subsets, the test and validation sample, stratified by race. The new formula was validated using 50 LDLT recipients.

RESULTS

Without using body weight-related variables, age, thoracic width measured using computed tomography, and race independently predicted the total liver volume (TLV). A new formula: 203.3-(3.61×age)+(58.7×thoracic width)-(463.7×race [1=Asian, 0=Caucasian]), most accurately predicted the TLV in the validation dataset as compared with any other formulas. The graft volume for LDLT was correlated with the postoperative prothrombin time, and the graft volume/SLV ratio calculated using the new formula was significantly better correlated with the postoperative prothrombin time than the graft volume/SLV ratio calculated using the other formulas or the graft volume/body weight ratio.

CONCLUSIONS

The new formula derived using the age, thoracic width and race predicted both the TLV in the healthy patient group and the SLV in LDLT recipients more accurately than any other previously reported formulas.

Liver function following extended hepatectomy can be accurately predicted using remnant liver volume to body weight ratio

BACKGROUND

Standardised measurement of remnant liver volume (RLV), where total liver volume (TLV) is calculated from patients' body surface area (RLV-sTLV), has been advocated. Extrapolating the model of living donor liver transplantation, we showed in a pilot study that the simplified RLV/body weight ratio (RLVBWR) was accurate in assessing the functional limit of hepatectomy. The aim of the study was to compare in a prospective series of extended right hepatectomy the predictive value of the RLVBWR and the RLV-sTLV at a cut-off of 0.5% (RLVBWR0.5%) and 20% (RLV-sTLV20%), respectively.

METHODS

We studied the impact of RLVBWR0.5% and of RLV-sTLV20% on three months morbidity and mortality in 74 non-cirrhotic patients operated on for malignant tumours. Of these, 47 patients who were not included in the initial pilot study were enrolled in a prospective validation cohort to reappraise the predictive value of each method.

RESULTS

RLVBWR and RLV-sTLV were highly correlated (Pearson correlation coefficient, 0.966). Three months overall and severe morbidity (grade 3b-5) and mortality were significantly increased in groups RLVBWR ≤ 0.5% and RLV-sTLVs ≤ 20% compared to groups >0.5% and >20%, respectively. The sensitivity and specificity in predicting death from liver failure were 100 and 84.1% for RLVBWR0.5% and 60 and 94.2% for RLV-sTLV20%, respectively. Similar results were observed in the validation cohort for the RLVBWR0.5% (lack of statistical power for RLV-sTLV as only 2 patients showed a RLV-sTLV ≤ 20%).

CONCLUSIONS

The RLVBWR0.5% is a method of assessing the remnant liver that is simple and as reliable as the standardised RLV-sTLV20%.

Quantified Risk Assessment for Major Hepatectomy via the Indocyanine Green Clearance Rate and Liver Volumetry Combined with Standard Liver Volume

BACKGROUND

Preoperative risk assessment for post-hepatectomy liver failure (PHLF) is essential for major hepatectomy. We intended to establish a standard liver volume (SLV) formula for Korean patients and validate the predictive power of the indocyanine green clearance rate constant (ICG-K) fraction of future remnant liver (FRL) (FRL-kICG) to total liver volume (TLV).

METHODS

This study comprised 2 retrospective studies. Part I established SLV formula and acquired ICG pharmacokinetic data from 2155 living donors. In part II, FRL-kICG cutoff was determined using 723 patients who underwent right liver resection for hepatocellular carcinoma.

RESULTS

In part I, the formula SLV (mL) = -456.3 + 969.8 × BSA (m(2)) (r = 0.707, r (2) = 0.500, p = 0.000) was derived with mean volume error of 10.5%. There was no correlation between TLV and ICG retention rate at 15 min. With a cutoff of 0.04 with hepatic parenchymal resection rate (PHRR) limit of 70%, 99.0% of our living donors were permissible for left or right hepatectomy. In part II, 25 hepatocellular carcinoma patients (3.5%) showed an FRL-kICG or SLV-corrected FRL-kICG <0.05. Of these, 4 (16 %) died of PHLF, whereas only 2 (0.3%) died in the other patient group with both an FRL-kICG and SLV-corrected FRL-kICG ≥ 0.05 (P = 0.000).

CONCLUSIONS

The FRL-kICG appears to reliably predict PHLF risk quantitatively. We suggest FRL-kICG cutoffs of 0.04 and 0.05 with PHRR limits of 70% and 65% for normal and diseased livers, respectively. Further validation with large patient population in multicenter studies is necessary to improve FRL-kICG predictability.

Variability of standard liver volume estimation versus software-assisted total liver volume measurement

The estimation of the standard liver volume (SLV) is an important component of the evaluation of potential living liver donors and the surgical planning for resection for tumors. At least 16 different formulas for estimating SLV have been published in the worldwide literature. More recently, several proprietary software-assisted image postprocessing (SAIP) programs have been developed to provide accurate volume measurements based on the actual anatomy of a specific patient. Using SAIP, we measured SLV in 375 healthy potential liver donors and compared the results to SLV values that were estimated with the previously published formulas and each donor's demographic and anthropomorphic data. The percentage errors of the 16 SLV formulas versus SAIP varied by more than 59% (from -21.6% to +37.7%). One formula was not statistically different from SAIP with respect to the percentage error (-1.2%), and another formula was not statistically different with respect to the absolute liver volume (18 mL). More than 75% of the estimated SLV values produced by these 2 formulas had percentage errors within ±15%, and the formulas provided good predictions within acceptable agreement (±15%) on scatter plots. Because of the wide variability, care must be taken when a formula is being chosen for estimating SLV, but the 2 aforementioned formulas provided the most accurate results with our patient demographics.