Prediction of liver remnant regeneration after living donor liver transplantation using preoperative CT texture analysis

Preoperative assessment of liver regeneration using T1 mapping and the functional liver imaging score derived from Gd-EOB-DTPA-enhanced magnetic resonance for patient with hepatocellular carcinoma after hepatectomy

Objectives

To explore whether T1 mapping parameters and the functional liver imaging score (FLIS) based on Gd-EOB-DTPA MRI could evaluate liver regeneration after hepatectomy for HCC patient.

Methods

This retrospective study finally included 60 HCC patients (48 men and 12 women, with a median age of 53 years). T1 relaxation time of liver before gadoxetic acid injection (T1pre) and during the hepatobiliary phase (T1HBP), reduction rate (Δ%) and FLIS were calculated, their correlations with liver fibrosis stage, hepatic steatosis, and liver regeneration, quantified as regeneration index (RI), were assessed by Kendall's tau-b correlation test or Spearman's correlation test. Multivariate linear regression analyses were used to explore the indicator of RI.

Results

T1pre, T1HBP, Δ%, and FLIS manifested significant correlation with fibrosis stage (r = 0.434, P =0.001; r = 0.546, P < 0.001; r = -0.356, P =0.005; r = -0.653, P <0.001, respectively). T1pre showed significant correction with steatosis grade (r = 0.415, P =0.001). Fibrosis stage and steatosis grade were associated with RI (r = -0.436, P<0.001; r = -0.338, P =0.008). Accordingly, T1pre, T1HBP and FLIS were the significant predictors (P<0.05) of RI in multivariate analysis. Similarly, in the patients undergoing minor hepatectomy (n=35), T1HBP, Δ% and FLIS were related to RI (P<0.05) in multivariate analysis. Nevertheless, in the patients undergoing major hepatectomy (n=25), no T1 mapping parameter and FLIS was the independent predictor of RI.

Conclusions

T1 mapping parameters and FLIS were the potential noninvasive indicators of liver regeneration, except for HCC patients undergoing major hepatectomy.

Clinical relevance statement

The value of T1 mapping and FLIS with Gd-EOB-DTPA MRI for accurate preoperative evaluation of liver regeneration is critical to prevent liver failure and improve prognosis of HCC patients.

Dynamic three-dimensional liver volume assessment of liver regeneration in hilar cholangiocarcinoma patients undergoing hemi-hepatectomy

Background

For patients with hilar cholangiocarcinoma (HC) undergoing hemi-hepatectomy, there are controversies regarding the requirement of, indications for, and timing of preoperative biliary drainage (PBD). Dynamic three-dimensional volume reconstruction could effectively evaluate the regeneration of liver after surgery, which may provide assistance for exploring indications for PBD and optimal preoperative bilirubin value. The purpose of this study was to explore the indications for PBD and the optimal preoperative bilirubin value to improve prognosis for HC patients undergoing hemi-hepatectomy.

Methods

We retrospectively analyzed the data of HC patients who underwent hemi-hepatectomy in the First Affiliated Hospital of China Medical University from 2012 to 2023. The liver regeneration rate was calculated using three-dimensional volume reconstruction. We analyzed the factors affecting the liver regeneration rate and occurrence of postoperative liver insufficiency.

Results

This study involved 83 patients with HC, which were divided into PBD group (n=36) and non-PBD group (n=47). The preoperative bilirubin level may be an independent risk factor affecting the liver regeneration rate (P=0.014) and postoperative liver insufficiency (P=0.016, odds ratio=1.016, β=0.016, 95% CI=1.003-1.029). For patients whose initial bilirubin level was >200 μmol/L (n=45), PBD resulted in better liver regeneration in the early stage (P=0.006) and reduced the incidence of postoperative liver insufficiency [P=0.012, odds ratio=0.144, 95% confidence interval (CI)=0.031-0.657]. The cut-off value of bilirubin was 103.15 μmol/L based on the liver regeneration rate. Patients with a preoperative bilirubin level of ≤103.15 μmol/L shown a better liver regeneration (P<0.01) and lower incidence of postoperative hepatic insufficiency (P=0.011, odds ratio=0.067, 95% CI=0.008-0.537).

Conclusion

For HC patients undergoing hemi-hepatectomy whose initial bilirubin level is >200 μmol/L, PBD may result in better liver regeneration and reduce the incidence of postoperative liver insufficiency. Preoperative bilirubin levels ≤103.15 μmol/L maybe recommended for leading to a better liver regeneration and lower incidence of postoperative hepatic insufficiency.

Intravoxel incoherent motion diffusion weighted imaging for preoperative evaluation of liver regeneration after hepatectomy in hepatocellular carcinoma

OBJECTIVES

To explore whether intravoxel incoherent motion (IVIM) parameters could evaluate liver regeneration preoperatively.

METHODS

A total of 175 HCC patients were initially recruited. The apparent diffusion coefficient, true diffusion coefficient (D), pseudodiffusion coefficient (D*), pseudodiffusion fraction (f), diffusion distribution coefficient, and diffusion heterogeneity index (Alpha) were measured by two independent radiologists. Spearman's correlation test was used to assess correlations between IVIM parameters and the regeneration index (RI), calculated as 100% × (the volume of the postoperative remnant liver - the volume of the preoperative remnant liver) / the volume of the preoperative remnant liver. Multivariate linear regression analyses were used to identify the factors for RI.

RESULTS

Finally, 54 HCC patients (45 men and 9 women, mean age 51.26 ± 10.41 years) were retrospectively analyzed. The intraclass correlation coefficient ranged from 0.842 to 0.918. In all patients, fibrosis stage was reclassified as F0-1 (n = 10), F2-3 (n = 26), and F4 (n = 18) using the METAVIR system. Spearman correlation test showed D* (r = 0.303, p = 0.026) was associated with RI; however, multivariate analysis showed that only D value was a significant predictor (p < 0.05) of RI. D and D*showed moderate correlations with fibrosis stage (r = -0.361, p = 0.007; r = -0.457, p = 0.001). Fibrosis stage showed a negative correlation with RI (r = -0.263, p = 0.015). In the 29 patients who underwent minor hepatectomy, only the D value showed a positive association (p < 0.05) with RI, and a negative correlation with fibrosis stage (r = -0.360, p = 0.018). However, in the 25 patients who underwent major hepatectomy, no IVIM parameters were associated with RI (p > 0.05).

CONCLUSIONS

The D and D* values, especially the D value, may be reliable preoperative predictors of liver regeneration.

KEY POINTS

• The D and D* values, especially the D value, derived from IVIM diffusion-weighted imaging may be useful markers for the preoperative prediction of liver regeneration in patients with HCC. • The D and D* values derived from IVIM diffusion-weighted imaging show significant negative correlations with fibrosis, an important predictor of liver regeneration. • No IVIM parameters were associated with liver regeneration in patients who underwent major hepatectomy, but the D value was a significant predictor of liver regeneration in patients who underwent minor hepatectomy.

In silico modeling for the hepatic circulation and transport: From the liver organ to lobules

The function of the liver depends critically on its blood supply. Numerous in silico models have been developed to study various aspects of the hepatic circulation, including not only the macro-hemodynamics at the organ level, but also the microcirculation at the lobular level. In addition, computational models of blood flow and bile flow have been used to study the transport, metabolism, and clearance of drugs in pharmacokinetic studies. These in silico models aim to provide insights into the liver organ function under both healthy and diseased states, and to assist quantitative analysis for surgical planning and postsurgery treatment. The purpose of this review is to provide an update on state-of-the-art in silico models of the hepatic circulation and transport processes. We introduce the numerical methods and the physiological background of these models. We also discuss multiscale frameworks that have been proposed for the liver, and their linkage with the large context of systems biology, systems pharmacology, and the Physiome project. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering Cardiovascular Diseases > Computational Models.

Preoperative evaluation of liver regeneration following hepatectomy in hepatocellular carcinoma using magnetic resonance elastography

BACKGROUND

For patients with hepatocellular carcinoma (HCC) undergoing hepatectomy, insufficient remnant liver regenerative capacity can lead to liver failure. The aim of this study was to evaluate the potential role of magnetic resonance elastography (MRE) for the preoperative prediction of liver regeneration in patients with HCC after partial hepatectomy (PH).

METHODS

A total of 54 patients with HCC undergoing MRE prior to PH were retrospectively included. The total functional liver, volume of preoperative future liver remnant (LVpre), and volume of postoperative liver remnant (LVpost), respectively, were measured, and the regeneration index (RI) and parenchymal hepatic resection rate (PHRR) were manually calculated. Univariate and multivariate logistic regression analyses were conducted to identify factors associated with a high RI, and receiver operating characteristic (ROC) curves were employed to evaluate the diagnostic performance of the liver stiffness (LS) values. Patients were classified into three subgroups based on the value of PHRR: low PHRR (<30%), intermediate PHRR (30-50%), and high PHRR (>50%). Subsequently, Spearman correlation analysis was used to investigate the relationship between LS values and RI in the subgroups.

RESULTS

Multivariable analysis revealed a low LS value was associated with greater odds of a high RI [odds ratio (OR), 0.049; 95% confidence interval (CI): 0.002 to 0.980]. An optimal cutoff value of 3.30 kPa was used to divide all patients into a low RI group and a high RI group with an area under the curve (AUC) value of 0.882 (95% CI: 0.767 to 0.996). A significant negative relationship between RI and LS values (r=-0.799; P<0.001) was observed in the intermediate PHRR subgroup.

CONCLUSIONS

The LS values based on MRE may serve as a potential preoperative predictor of liver regeneration for patients with HCC undergoing PH.

Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy

Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R²) and is time-saving. Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.

Predictive Factors for Hypertrophy of the Future Liver Remnant After Portal Vein Embolization: A Systematic Review

This systematic review was conducted to determine factors that are associated with the degree of hypertrophy of the future liver remnant following portal vein embolization. An extensive search on September 15, 2020, and subsequent literature screening resulted in the inclusion of forty-eight articles with 3368 patients in qualitative analysis, of which 18 studies were included in quantitative synthesis. Meta-analyses based on a limited number of studies showed an increase in hypertrophy response when additional embolization of segment 4 was performed (pooled difference of medians = − 3.47, 95% CI − 5.51 to − 1.43) and the use of N-butyl cyanoacrylate for portal vein embolization induced more hypertrophy than polyvinyl alcohol (pooled standardized mean difference (SMD) = 0.60, 95% CI 0.30 to 0.91). There was no indication of a difference in degree of hypertrophy between patients who received neo-adjuvant chemotherapy and those who did not receive pre-procedural systemic therapy

(pooled SMD = − 0.37, 95% CI − 1.35 to 0.61), or between male and female patients (pooled SMD = 0.19, 95% CI − 0.12 to 0.50).

The study was registered in the International Prospective Register of Systematic Reviews on April 28, 2020 (CRD42020175708).

Prediction of Remnant Liver Regeneration after Right Hepatectomy in Patients with Hepatocellular Carcinoma Using Preoperative CT Texture Analysis and Clinical Features

OBJECTIVES

To predict the regenerative rate of liver in patients with HCCs after right hepatectomy using texture analysis on preoperative CT combined with clinical features.

MATERIALS AND METHODS

88 patients with 90 HCCs who underwent right hepatectomy were retrospectively included. The future remnant liver was semiautomatically segmented, and the volume of future remnant liver on preoperative CT (LVpre) and the volume of remnant liver on following-up CT (LVfu) were measured. We calculated the regeneration index (RI) by the following equation: (LVfu - LVpre)/LVpre) × 100 (%). The support vector machine recursive method was used for the feature selection. The Naive Bayes classifier was used to predict liver RI, and 5-fold cross-validation was performed to adjust the parameters. Sensitivity, specificity, and accuracy were calculated to evaluate the diagnostic efficiency of the model.

RESULTS

The mean RI was 142.99 ± 92.17%. Of all clinical parameters and texture features, the AST, ALB, PT-INR, Perc.10%, and S(5, -5)Correlat were found to be statistically significant with RI. The diagnostic sensitivity, specificity, and accuracy of the model in the training group were 0.902, 0.634, and 0.768, and the AUC value of the obtained model was 0.841. In the test group, the sensitivity, specificity, and accuracy of the model were 1.0, 0.429, and 0.778, respectively, and the AUC value was 0.844.

CONCLUSION

The use of texture analysis on preoperative CT combined with clinical features can be helpful in predicting the liver regeneration rate in patients with HCCs after right hepatectomy.

Prediction of liver regeneration in recipients after living-donor liver transplantation in using preoperative CT texture analysis and clinical features

PURPOSE

The aim of the study is to predict the rate of liver regeneration in recipients after living-donor liver transplantation using preoperative CT texture and shape analysis of the future graft.

METHODS

102 donor-recipient pairs who underwent living-donor liver transplantation using right lobe grafts were retrospectively included. We semi-automatically segmented the future graft from preoperative CT. The volume of the future graft (LV_pre) was measured, and texture and shape analyses were performed. The graft liver was segmented from postoperative follow-up CT and the volume of the graft (LV_post) was measured. The regeneration index was defined by the following equation: [(LV_post-LV_pre)/LV_pre] × 100(%). We performed a stepwise, multivariate linear regression analysis to investigate the association between clinical, texture and shape parameters and the RI and to make the best-fit predictive model.

RESULTS

The mean regeneration index was 47.5 ± 38.6%. In univariate analysis, the volume of the future graft, energy, effective diameter, surface area, sphericity, roundness_m, compactness1, and grey-level co-occurrence matrix contrast as well as several clinical parameters were significantly associated with the regeneration index (p < 0.05). The best-fit predictive model for the regeneration index made by multivariate analysis was as follows: Regeneration index (%) = 127.020-0.367 × effective diameter - 1.827 × roundness_m + 47.371 × recipient body surface area (m²) + 12.041 × log(recipient white blood cell count) (× 10³/μL)+ 18.034 (if the donor was female).

CONCLUSION

The effective diameter and roundness_m of the future graft were associated with liver regeneration. Preoperative CT texture analysis of future grafts can be useful for predicting liver regeneration in recipients after living-donor liver transplantation.