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

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.

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.

Computed Tomography-Measured Liver Volume Predicts the Risk of Hepatocellular Carcinoma Development in Chronic Hepatitis C Patients

AIM

In this retrospective cohort study, we evaluated the significance of liver volume in the prediction of hepatocellular carcinoma (HCC) in 277 chronic hepatitis C (CHC) patients who received dynamic computed tomography (CT) during surveillance.

METHODS

Liver volumes were measured on portal venous phase of CT images by using ImageJ software. Liver volume index, a ratio of the standard liver volume expected by weight and height to the measured liver volume, was calculated to adjust for normal variations. The cohort was randomly divided to derivation (n = 100) and validation sets (n = 177) for the generation of a liver volume-based Cox prediction model and validation of a liver volume-based nomogram, respectively.

RESULTS

The liver volume index was independent of weight or height, and it predicted further development of HCC (hazard ratio [HR] 16.30, 95% CI 6.70-39.62; p < 0.001). Liver cirrhosis, gamma-glutamyl transferase, and liver volume index were independent predictors of HCC, and nomogram-based prediction score from these three parameters identified high-risk patients at the cutoff of 110 in both derivation (p < 0.001) and validation cohort (p < 0.001).

CONCLUSION

Liver volume-based prediction model stratifies the risk of developing HCC in CHC patients whose initial dynamic CT study gave negative results.

Physiologically based pharmacokinetic (PBPK) modeling of meloxicam in different CYP2C9 genotypes

Meloxicam, a non-steroidal anti-inflammatory drug, is used for the treatment of rheumatoid arthritis and osteoarthritis. Cytochrome P450 (CYP) 2C9 and CYP3A4 are major and minor enzymes involved in the metabolism of meloxicam. Impaired enzyme activity of CYP2C9 variants increases the plasma exposures of meloxicam and the risk of adverse events. The objective of our study is to develop and validate the physiologically based pharmacokinetic (PBPK) model of meloxicam related to CYP2C9 genetic polymorphism using the PK-Sim^® software. In vitro k_cat of CYP2C9 was optimized in different CYP2C9 genotypes. The demographic and pharmacokinetic dataset for the development of the PBPK model was extracted from two previous clinical pharmacokinetic studies. Thirty-one clinical datasets, representing different dose regimens and demographic characteristics, were utilized to validate the PBPK model. The shapes of simulated plasma concentration-time profiles in each CYP2C9 genotype were visually similar to observed profiles. The predicted exposures (AUC_inf) of meloxicam in CYP2C9*1/*3, CYP2C9*1/*13, and CYP2C9*3/*3 genotypes were increased by 1.77-, 2.91-, and 8.35-fold compared to CYP2C9*1/*1 genotype, respectively. In all datasets for the development and validations, fold errors between predicted and observed pharmacokinetic parameters were within the two-fold error criteria. As a result, the PBPK model was appropriately established and properly described the pharmacokinetics of meloxicam in different CYP2C9 genotypes. This study is expected to contribute to reducing the risk of adverse events of meloxicam through optimization of meloxicam dosing in different CYP2C9 genotypes.

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 FL_US [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 FL_US is the optimal formula for predicting SLW in adult DDs.

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.

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.

Efficacy and Safety of Weight Reduction of the Donor in Hepatic Steatosis for Living Donor Liver Transplantation

Background

Use of steatotic livers is a known risk factor for increased primary nonfunction after liver transplantation. This study investigated the efficacy and clinical outcome of simple weight reduction of steatosis for donors undergoing living-donor liver transplantation (LDLT).

Material/Methods

We defined two groups: the reduction group, which included donors with >30% macrovesicular steatosis and body mass index (BMI) >25 kg/m², and the conventional group, which included donors with <30% macrovesicular steatosis. Donors in the reduction group were educated about the goal of voluntary weight reduction to lose 5% of body weight, not exceeding 1.6 kg/week, and attempted to maintain weight reduction for at least 8 weeks.

Results

Weight reduction significantly improved steatosis (40.71±14.56 vs. 7.867±2.67, p=0.000). Body weight and BMI were reduced in the weight reduction group (85.40±8.254 kg vs. 76.27±7.556 kg, p=0.052; and 28.89±2.303 kg/m²vs. 26.16±1.629 kg/m², p=0.025, respectively). The transplanted grafts of recipients and remnant livers of donors showed intact liver function, and there was no difference in liver function tests between the conventional and reduction groups. No significant difference in graft survival was observed.

Conclusions

Simple weight reduction improves steatosis and contributes to safer LDLT for both recipient and donor. Importantly, according to our results, even steatotic livers can be used for LDLT after patients follow a simple weight reduction protocol.

[Clinical practice guidelines for precision diagnosis and treatment of complex liver tumor guided by three-dimensional visualization technology (version 2019)]

Virtual resection of liver structures guided by three-dimensional visualization technology (3DVT) has been extensively used in China. This technique provides a safe and effective method for accurate diagnosis of liver tumors and has important applications in preoperative evaluation, surgical planning and intraoperative guidance of liver cancer surgeries. The technical advantages and clinical significance of 3DVT in the diagnosis and treatment of complex liver tumors have been recognized. In order to standardize the application of 3DVT in the precision diagnosis and treatment of complex liver tumors, this guideline provides explanations and recommendations in the following aspects: (1) the establishment of homogenization processing and quality control system of 3D reconstruction; (2) the establishment of 3D reconstructed models of abdominal organs and lesions; (3) the individualized classification and quantitative analysis of blood vessels based on 3DVT; (4) 3DVT-based classification and grading of hepatic vessels in complex hepatic tumors; (5) evaluation system for surgery evaluation after reconstruction of the 3D visualization model; (6) application of 3D printing in complex hepatectomy; (7) virtual reality technology; (8) ICG fluorescence imaging; (9) multi-modal images for real-time navigation; (10) three-dimensional visualization to guide the preoperative surgical planning of precision hepatectomy; (11) application of 3DVT guidance in other therapeutic methods of hepatocellular carcinoma; (12) application of 3DVT in follow-up evaluation of the patients after liver cancer surgeries. 3DVT for visualization of the liver structures has important clinical values for accurate preoperative evaluation, preoperative planning and surgical navigation of complex liver tumors, and it facilitates precision surgeries to improve the outcomes and promote postoperative recovery of the patients.

Liver volume index predicts the risk of esophageal variceal hemorrhage in cirrhotic patients on propranolol prophylaxis

BACKGROUND/AIMS

Non-selective β-blockers (NSBBs) are used for primary prevention of esophageal variceal hemorrhage (VH) in patients with portal hypertension, but a significant number of patients develop VH while on NSBB therapy. In this study, we sought to determine whether liver volume can predict the risk of primary prophylaxis failure in cirrhotic patients on NSBB therapy.

METHODS

A retrospective cohort of 309 patients on prophylactic propranolol was analyzed. Liver volume was measured in portal venous phase images of multidetector computed tomography. Predictors of VH were assessed using a Cox proportional hazards model with competing-risks analysis. A nomogram was developed for estimation of the risk of primary prophylaxis failure.

RESULTS

During a median follow-up of 36 months, 37 patients on propranolol developed VH. Liver volume index, the ratio of measured-to-expected liver volume, was an independent predictor of VH (adjusted hazard ratio [HR], 2.70; 95% confidence interval [CI], 1.37 to 5.33; p = 0.004) as were the presence of large varices and the absence of ascites. A nomogram-based volume score of > 0.6 was predictive of prophylaxis failure (HR, 7.54; 95% CI, 2.88 to 19.73; p < 0.001). Time-dependent receiver operating characteristic curve analysis revealed that a nomogram-based risk score had significantly better discriminatory power than the North Italian Endoscopy Club index in predicting prophylaxis failure at 6 and 8 years.

CONCLUSION

Liver volume index is an independent predictor of first VH and a nomogram-based volume score stratifies the VH risk in cirrhotic patients on propranolol prophylaxis.

Survival Outcomes in Split Compared With Whole Liver Transplantation

Split-liver transplantation (SLT) should be cautiously considered because the right trisection (RTS) graft can be a marginal graft in adult recipients. Herein, we analyzed the outcomes of RTS-SLT in Korea, where >75% of adult liver transplantations are performed with living donor liver transplantation. Among 2462 patients who underwent deceased donor liver transplantations (DDLTs) from 2005 to 2014, we retrospectively reviewed 86 (3.5%) adult patients who received a RTS graft (RTS-SLT group). The outcomes of the RTS-SLT group were compared with those of 303 recipients of whole liver (WL; WL-DDLT group). Recipient age, laboratory Model for End-Stage-Liver Disease (L-MELD) score, ischemia time, and donor-to-recipient weight ratio (DRWR) were not different between the 2 groups (P > 0.05). However, malignancy was uncommon (4.7% versus 36.3%), and the donor was younger (25.2 versus 42.7 years) in the RST-SLT group than in the WL-DDLT group (P < 0.05). The technical complication rates and the 5-year graft survival rates (89.0% versus 92.8%) were not different between the 2 groups (P > 0.05). The 5-year overall survival (OS) rate (63.1%) and graft-failure-free survival rate (63.1%) of the RTS-SLT group were worse than that of the WL-DDLT group (79.3% and 79.3%; P < 0.05). The factors affecting graft survival rates were not definite. However, the factors affecting OS in the RTS-SLT group were L-MELD score >30 and DRWR ≤1.0. In the subgroup analysis, OS was not different between the 2 groups if the DRWR was >1.0, regardless of the L-MELD score (P > 0.05). In conclusion, a sufficient volume of the graft estimated from DRWR-matching could lead to better outcomes of adult SLTs with a RTS graft, even in patients with high L-MELD scores.

How small is TOO small? New liver constraint is needed- Proton therapy of hepatocellular carcinoma patients with small normal liver

PURPOSE

This study evaluated the outcomes of hepatocellular carcinoma (HCC) patients with small normal liver volume (NLV) treated with proton beam therapy (PBT) and introduced estimated standard liver volume (eSLV) as a new constraint.

MATERIALS AND METHODS

HCC patients with NLV < 800 cm3 and no distant metastasis who received treatment in our proton center were included. The doses of PBT were mainly 72.6 Gray equivalents (GyE) in 22 fractions and 66 GyE in 10 fractions according to tumor locations. The Urata equation was used to calculate eSLV.

RESULTS

Twenty-two patients were treated between November 2015 and December 2016. The 1-year progression-free and overall survival rates were 40.4% and 81.8%, respectively. The 1-year in-field failure-free rate was 95.5%. NLV ranged from 483.9 to 795.8 cm3 (median = 673.8 cm3), eSLV ranged from 889.3 to 1290.0 cm3 (median = 1104.5 cm3), and the resulting NLV/eSLV ratio ranged from 44.3 to 81.2% (median = 57.7%). Non-irradiated liver volume (NILV) ranged from 232.9 to 531.6 cm3 (median = 391.2 cm3). The NILV/eSLV ratio ranged from 21.2 to 48.0% (median = 33.3%). NLV in the patients who received <30 GyE (rV30) ranged from 319.1 to 633.3 cm3 (median = 488.2 cm3), and their rV30/eSLV ratio ranged from 30.7 to 58.0%. None of our patients developed liver failure. One patient with initial abnormal liver enzyme levels developed non-classic radiation-induced liver disease (RILD).

CONCLUSION

From the viewpoint of minimal liver toxicity occurring in our patients with NLV < 800 cm3, conventional liver constraints involving the use of absolute volume could not accurately predict the risk of RILD. It is reasonable to start using individualized constraints with eSLV for HCC patients undergoing PBT. According to the study results, an NILV/eSLV ratio of >20% and an rV30/eSLV ratio of >30% are acceptable.

Reference values of liver volume in Caucasian population and factors influencing liver size

PURPOSE

To investigate factors influencing liver size and to determine reference values of liver volume (LV) for healthy subjects.

METHODS

2773 volunteers underwent magnetic resonance imaging (MRI) of the liver in the setting of the population based Study of Health in Pomerania. Based on measurement of maximum diameters in three orientations, LVs were calculated and correlated with demographic factors such as age, gender, and body mass index. In addition, LVs of healthy volunteers and participants with parenchymal liver diseases such as fatty liver disease, iron overload, fibrosis/cirrhosis were compared. Adjusted reference values of liver volumes were defined for the group of healthy participants.

RESULTS

In general, mean LV (mean ± standard deviation) was 1505 ± 385 cm³. Age, gender and body mass index correlated significantly with the liver volume (p ≤ 0.001). Parenchymal liver diseases significantly influence LV (with: 1624 ± 420 cm³, n = 1525 and without parenchymal liver diseases: 1360 ± 273 cm³; n = 1.248, p ≤ 0.001). Compared to LV of participants without liver diseases, LV was increased in volunteers with hepatic steatosis (1717 ± 419 cm³; n = 1111), liver iron overload (1558 ± 367 cm³; n = 553; p ≤ 0.001) as well as in participants with fibrosis/cirrhosis (1494 ± 459 cm³; n = 383).

CONCLUSIONS

LV is influenced by age, body mass index and parenchymal liver diseases. Reference values were established to aid in the diagnosis of parenchymal liver diseases.

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.

Liver volume-based prediction model stratifies risks for hepatocellular carcinoma in chronic hepatitis B patients on surveillance

BACKGROUND AND AIM

The aim of this study was to determine whether dynamic computed tomography (CT)-measured liver volume predicts the risk of hepatocellular carcinoma (HCC) when the CT scans do not reveal evidence of HCC in chronic hepatitis B (CHB) patients on surveillance.

METHODS

This retrospective multicentre cohort study included 1,246 patients who received entecavir and regular HCC surveillance in three tertiary referral centres in South Korea. Liver volumes were measured on portal venous phase CT images. A nomogram was developed based on Cox independent predictors and externally validated. Time-dependent receiver operating characteristic (ROC) analysis was performed for comparison with previous prediction models.

RESULTS

Patients who received dynamic CT studies during surveillance had significantly higher risk for HCC compared to patients without CT studies (hazard ratio [HR] = 3.1; p < 0.001). Expected/measured liver volume ratio was an independent predictor of HCC (HR = 4.2; p = 0.002) in addition to age, sex and cirrhosis. The nomogram based on the four predictors discriminated risks for HCC (HR = 4.1 and 6.0 in derivation and validation cohort, respectively, for volume score > 150; p < 0.001). Time-dependent ROC analysis confirmed better performance of the volume score compared to HCC prediction models with conventional predictors (integrated area under curve = 0.758 vs. 0.661-0.712; p < 0.05).

CONCLUSIONS

CT-measured liver volume is an independent predictor of future HCC, and nomogram-based liver volume score may stratify the risks of HCC in CHB patients who showed negative CT findings for HCC during surveillance.

Factors Affecting Liver Regeneration in Living Donors After Hepatectomy

Background

The safety of living liver donors is the paramount priority of liver transplantation surgeons. The liver has an effective regeneration capacity. The regeneration rate of the liver remnant in living liver donors provides much information useful in liver surgery. The outcome of the remnant liver after hepatectomy can be affected by many different perioperative factors.

Material/Methods

A total of 46 patients were enrolled in the study. Retrospective clinical data, including preoperative and postoperative early and late computed tomography liver volumetry measurements, estimated resection volumes, resected liver weights, and postoperative laboratory values, were statistically evaluated according to the liver resection type.

Results

No significant difference was detected in age, sex, calculated and computed tomography estimated total liver volume, intraoperative Hb decrease, postoperative complications, or postoperative portal vein flow rate. Postoperative liver enlargement rates were significant higher in the right hemihepatectomy (RHH) group than in the left lateral sectionectomy (LLS) group. The size of the liver remnant or graft has a major effect on regeneration rate. Postoperative biliary leakage did not have any significant effect on liver regeneration. No post-hepatectomy liver failure was detected among the liver donors.

Conclusions

Liver hypertrophy depends on the extent of liver resection. The cause of volume decrease in the LLS group after hepatectomy in our series appears to be the gradual atrophy of liver segment 4. RHH and LLS surgeries differ from each other in terms of resected liver volume, as well as inflammatory activity, and the latter appears to affect liver regeneration.

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.

Value of Preoperative Indocyanine Green Clearance Test for Predicting Post-Hepatectomy Liver Failure in Noncirrhotic Patients

BACKGROUND Liver failure is the most feared complication following hepatectomy. Post-hepatectomy liver failure (PHLF) is closely related to the remnant liver volume, and functional reserve. There are several methods for predicting PHLF prior to liver resection. The indocyanine green (ICG) clearance test was popularized in patients with hepatocellular cancer (HCC). We aim to demonstrate the value of preoperative ICG clearance measurement via pulse spectrophotometer (LIMON®) in prediction of PHLF in noncirrhotic patients prior to liver resection. MATERIAL AND METHODS Fifty-three noncirrhotic patients who underwent liver resection due to different pathologies were included. Retrospectively collected clinical data, including the preoperative ICG clearance measurements and remnant liver volumes of the patients, were statistically evaluated according to the PHLF criteria of the International Study Group of Liver Surgery. RESULTS Four (7.5%) patients with PHLF were observed. There was no significant difference between PHLF and non-PHLF groups regarding ICG clearance measurements with cut-off values of 5% and 9.5%. CONCLUSIONS The ICG clearance test does not satisfy our expectations in noncirrhotic patients in predicting PHLF. We believe that the ICG clearance test should be reserved for patients with cirrhosis and/or HCC. This test could be an option for noncirrhotic patients with chronic active hepatitis, advanced-grade fatty livers, or for patients who received long-term preoperative chemotherapy, and also for patients who underwent single or multiple sessions of TACE or TARE prior to liver resection. If the routine selection criteria have been fulfilled, there is no further need to perform the ICG clearance test for living liver donors.

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.

Streamline flow of the portal vein affects the lobar distribution of colorectal liver metastases and has a clinical impact on survival

Purpose

It is believed that blood from the superior mesenteric vein and splenic vein mixes incompletely in the portal vein and maintains a streamline flow influencing its anatomic distribution. Although several experimental studies have demonstrated the existence of streamlining, clinical studies have shown conflicting results. We investigated whether streamlining of portal vein affects the lobar distribution of colorectal liver metastases and estimated its impact on survival.

Methods

Data of patients who underwent hepatectomy for colorectal liver metastases were retrospectively collected. The chi-square test was used for analyzing the distribution of metastasis. Cox analysis was used to identify risk factors of survival. Fisher exact test was used for subgroup analysis comparing hepatic recurrence.

Results

A total of 410 patients were included. The right-to-left ratio of liver metastases were 2.20:1 in right-sided colon cancer and 1.39:1 in left-sided cancer (P = 0.017). Cox analyses showed that margin < 5 mm (P < 0.001; 95% confidence interval [CI], 1.648–4.884; hazard ratio [HR], 2.837), age ≥ 60 years (P = 0.004; 95% CI, 1.269–3.641; HR, 2.149), N2 status (P < 0.001, 95% CI, 1.598–4.215; HR, 2.595), tumor size ≥ 45 mm (P = 0.014; 95% CI, 1.159–3.758; HR, 2.087) and other metastasis (P = 0.012; 95% CI, 1.250–5.927; HR, 2.722) were risk factors of survival. However, in 70 patients who underwent right hemihepatectomy for solitary metastasis, left-sided colorectal cancer was a risk factor (P = 0.019; 95% CI, 1.293–17.956; HR, 4.818), and was associated with higher recurrence than right-sided cancer (43.1% and 15.8%, respectively, P = 0.049).

Conclusion

This study showed significant difference in lobar distribution of liver metastases between right colon cancer and left colorecral cancer. Furthermore, survival of left-sided colorectal cancer was poorer than that of right-sided cancer in patients who underwent right hemihepatectomy for solitary metastasis. These findings can be helpful for clinicians planning treatment strategy.