Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

Short-Term Monitoring of Graft Regeneration in Partial Liver Transplantation Recipients

BACKGROUND Liver regeneration after partial liver transplantation, including living donor liver transplantation and split liver transplantation, is important for successful transplantation. MATERIAL AND METHODS We retrospectively analyzed 68 patients who underwent partial liver transplantation and calculated their regeneration index (RI)-based difference in postoperative and preoperative liver volume. We collected clinical data of donors and recipients and analyzed the correlation between clinical characteristics and RI. According to the above results, the generalized estimating equation (GEE) model included white blood cell count (WBC), neutrophils, lymphocytes, platelets, prothrombin time (PT), and activated partial thromboplastin time (APTT) on Days 1, 3, and 7 after LT and was used to predict the RI. RESULTS The mean RI was 40%, which was used as the cutoff value to divide all patients to the high-RI group and the low-RI group. The percentage of Child-Pugh C patients was 44% in the high-RI group, which was significantly more than that (21%) in the low-RI group (P=0.038). Among the postoperative monitoring parameters, neutrophil (P=0.044) and platelet (P=0.036) levels declined in the high-RI group on Day 3, while APTT was higher on Day 1 compared to the low-RI group. The predictive model based on GEE analysis achieved a good effect, with the area under the receiver operating characteristic curve on Day 1 (0.681; 95% CI, 0.556-0.807) and Day 3 (0.705; 95% CI, 0.578-0.832) showing significant differences (P=0.010 and 0.004, respectively). CONCLUSIONS The combination of decreased counts of WBC, neutrophils, lymphocytes, and platelets, as well as elevated PT and APTT on Day 3 after LT showed a good capability to predict a higher rate of liver regeneration after partial liver transplantation.

Longitudinal ultrasound imaging and network modeling in rats reveal sex-dependent suppression of liver regeneration after resection in alcoholic liver disease

Liver resection is an important surgical technique in the treatment of cancers and transplantation. We used ultrasound imaging to study the dynamics of liver regeneration following two-thirds partial hepatectomy (PHx) in male and female rats fed via Lieber-deCarli liquid diet protocol of ethanol or isocaloric control or chow for 5–7 weeks. Ethanol-fed male rats did not recover liver volume to the pre-surgery levels over the course of 2 weeks after surgery. By contrast, ethanol-fed female rats as well as controls of both sexes showed normal volume recovery. Contrary to expectations, transient increases in both portal and hepatic artery blood flow rates were seen in most animals, with ethanol-fed males showing higher peak portal flow than any other experimental group. A computational model of liver regeneration was used to evaluate the contribution of physiological stimuli and estimate the animal-specific parameter intervals. The results implicate lower metabolic load, over a wide range of cell death sensitivity, in matching the model simulations to experimental data of ethanol-fed male rats. However, in the ethanol-fed female rats and controls of both sexes, metabolic load was higher and in combination with cell death sensitivity matched the observed volume recovery dynamics. We conclude that adaptation to chronic ethanol intake has a sex-dependent impact on liver volume recovery following liver resection, likely mediated by differences in the physiological stimuli or cell death responses that govern the regeneration process. Immunohistochemical analysis of pre- and post-resection liver tissue validated the results of computational modeling by associating lack of sensitivity to cell death with lower rates of cell death in ethanol-fed male rats. Our results illustrate the potential for non-invasive ultrasound imaging to assess liver volume recovery towards supporting development of clinically relevant computational models of liver regeneration.

The Influence of Interdisciplinary Work towards Advancing Knowledge on Human Liver Physiology

The knowledge accumulated throughout the years about liver regeneration has allowed a better understanding of normal liver physiology, by reconstructing the sequence of steps that this organ follows when it must rebuild itself after being injured. The scientific community has used several interdisciplinary approaches searching to improve liver regeneration and, therefore, human health. Here, we provide a brief history of the milestones that have advanced liver surgery, and review some of the new insights offered by the interdisciplinary work using animals, in vitro models, tissue engineering, or mathematical models to help advance the knowledge on liver regeneration. We also present several of the main approaches currently available aiming at providing liver support and overcoming organ shortage and we conclude with some of the challenges found in clinical practice and the ethical issues that have concomitantly emerged with the use of those approaches.

Predicting liver regeneration following major resection

Breakdown of synthesis, excretion and detoxification defines liver failure. Post-hepatectomy liver failure (PHLF) is specific for liver resection and a rightfully feared complication due to high lethality and limited therapeutic success. Individual cytokine and growth factor profiles may represent potent predictive markers for recovery of liver function. We aimed to investigate these profiles in post-hepatectomy regeneration. This study combined a time-dependent cytokine and growth factor profiling dataset of a training (30 patients) and a validation (14 patients) cohorts undergoing major liver resection with statistical and predictive models identifying individual pathway signatures. 2319 associations were tested. Primary hepatocytes isolated from patient tissue samples were stimulated and their proliferation was analysed through DNA content assay. Common expression trajectories of cytokines and growth factors with strong correlation to PHLF, morbidity and mortality were identified despite highly individual perioperative dynamics. Especially, dynamics of EGF, HGF, and PLGF were associated with mortality. PLGF was additionally associated with PHLF and complications. A global association-network was calculated and validated to investigate interdependence of cytokines and growth factors with clinical attributes. Preoperative cytokine and growth factor signatures were identified allowing prediction of mortality following major liver resection by regression modelling. Proliferation analysis of corresponding primary human hepatocytes showed associations of individual regenerative potential with clinical outcome. Prediction of PHLF was possible on as early as first postoperative day (POD1) with AUC above 0.75. Prediction of PHLF and mortality is possible on POD1 with liquid-biopsy based risk profiling. Further utilization of these models would allow tailoring of interventional strategies according to individual profiles.

From Seeing to Simulating: A Survey of Imaging Techniques and Spatially-Resolved Data for Developing Multiscale Computational Models of Liver Regeneration

Liver regeneration, which leads to the re-establishment of organ mass, follows a specifically organized set of biological processes acting on various time and length scales. Computational models of liver regeneration largely focused on incorporating molecular and signaling detail have been developed by multiple research groups in the recent years. These modeling efforts have supported a synthesis of disparate experimental results at the molecular scale. Incorporation of tissue and organ scale data using noninvasive imaging methods can extend these computational models towards a comprehensive accounting of multiscale dynamics of liver regeneration. For instance, microscopy-based imaging methods provide detailed histological information at the tissue and cellular scales. Noninvasive imaging methods such as ultrasound, computed tomography and magnetic resonance imaging provide morphological and physiological features including volumetric measures over time. In this review, we discuss multiple imaging modalities capable of informing computational models of liver regeneration at the organ-, tissue- and cellular level. Additionally, we discuss available software and algorithms, which aid in the analysis and integration of imaging data into computational models. Such models can be generated or tuned for an individual patient with liver disease. Progress towards integrated multiscale models of liver regeneration can aid in prognostic tool development for treating liver disease.

Liver regeneration and inflammation: from fundamental science to clinical applications

Liver regeneration is a complex process involving the crosstalk of multiple cell types, including hepatocytes, hepatic stellate cells, endothelial cells and inflammatory cells. The healthy liver is mitotically quiescent, but following toxic damage or resection the cells can rapidly enter the cell cycle to restore liver mass and function. During this process of regeneration, epithelial and non-parenchymal cells respond in a tightly coordinated fashion. Recent studies have described the interaction between inflammatory cells and a number of other cell types in the liver. In particular, macrophages can support biliary regeneration, contribute to fibrosis remodelling by repressing hepatic stellate cell activation and improve liver regeneration by scavenging dead or dying cells in situ. In this Review, we describe the mechanisms of tissue repair following damage, highlighting the close relationship between inflammation and liver regeneration, and discuss how recent findings can help design novel therapeutic approaches.

Muscularity Defined by the Combination of Muscle Quantity and Quality is Closely Related to Both Liver Hypertrophy and Postoperative Outcomes Following Portal Vein Embolization in Cancer Patients

BACKGROUND

Portal vein embolization (PVE) is a common procedure for preventing hepatic insufficiency after major hepatectomy. While evaluating the body composition of surgical patients is common, the impact of muscularity defined by both muscle quantity and quality on liver hypertrophy after PVE and associated outcomes after major hepatectomy in patients with hepatobiliary cancer remain unclear.

METHODS

This retrospective review included 126 patients who had undergone hepatobiliary cancer resection after PVE. Muscularity was measured on preoperative computed tomography images by combining the skeletal mass index and intramuscular adipose content. Various factors including the degree of hypertrophy (DH) of the future liver remnant and post-hepatectomy outcomes were compared according to muscularity.

RESULTS

DH did not differ by malignancy type. Patients with high muscularity had better DH after PVE (P = 0.028), and low muscularity was an independent predictor for poor liver hypertrophy after PVE [odds ratio (OR), 3.418; 95% confidence interval (CI), 1.129-10.352; P = 0.030]. In subgroup analyses in which patients were stratified into groups based on primary hepatobiliary tumors and metastases, low muscularity was associated with higher incidence of post-hepatectomy liver failure (PHLF) ≥ grade B (P = 0.018) and was identified as an independent predictor for high-grade PHLF (OR 3.931; 95% CI 1.113-13.885; P = 0.034) among the primary tumor group. In contrast, muscularity did not affect surgical outcomes in patients with metastases.

CONCLUSIONS

Low muscularity leads to poor liver hypertrophy after PVE and is also a predictor of PHLF, particularly in primary hepatobiliary cancer.

The Primary Aldosteronism Surgical Outcome Score for the Prediction of Clinical Outcomes After Adrenalectomy for Unilateral Primary Aldosteronism

OBJECTIVE

To develop a prediction model for clinical outcomes after unilateral adrenalectomy for unilateral primary aldosteronism.

SUMMARY BACKGROUND DATA

Unilateral primary aldosteronism is the most common surgically curable form of endocrine hypertension. Surgical resection of the dominant overactive adrenal in unilateral primary aldosteronism results in complete clinical success with resolution of hypertension without antihypertensive medication in less than half of patients with a wide between-center variability.

METHODS

A linear discriminant analysis model was built using data of 380 patients treated by adrenalectomy for unilateral primary aldosteronism to classify postsurgical clinical outcomes. The total cohort was then randomly divided into training (280 patients) and test (100 patients) datasets to create and validate a score system to predict clinical outcomes. An online tool (Primary Aldosteronism Surgical Outcome predictor) was developed to facilitate the use of the predictive score.

RESULTS

Six presurgical factors associated with complete clinical success (known duration of hypertension, sex, antihypertensive medication dosage, body mass index, target organ damage, and size of largest nodule at imaging) were selected based on classification performance in the linear discriminant analysis model. A 25-point predictive score was built with an optimal cut-off of greater than 16 points (accuracy of prediction = 79.2%; specificity = 84.4%; sensitivity = 71.3%) with an area under the curve of 0.839.

CONCLUSIONS

The predictive score and the primary aldosteronism surgical outcome predictor can be used in a clinical setting to differentiate patients who are likely to be clinically cured after surgery from those who will need continuous surveillance after surgery due to persistent hypertension.

Volumetric and Functional Regeneration of Remnant Liver after Hepatectomy

BACKGROUND

Post-hepatectomy liver regeneration is of great interest to liver surgeons, and understanding the process of regeneration could contribute to increasing the safety of hepatectomies and improving prognoses.

METHODS

Five hundred thirty-eight patients who underwent hepatectomy were retrospectively analyzed. Postoperative outcomes were evaluated, with a focus on the effects of portal vein resection and resected liver volume on remnant liver regeneration in patients with liver tumors. Remnant liver volumes (RLVs) and laboratory data were measured postoperatively using multidetector computed tomography on day 7 and months 1, 2, 5, 12, and 24 after the operation.

RESULTS

Liver regeneration speed peaked at 1 week postoperatively and gradually decreased. Regeneration with large resections was longer than that with small resections, with the remnant liver regeneration rate being significantly lower in the former at all time points. Remnant liver regeneration plateaued around 5 months postoperatively, when regeneration is almost complete. Up to 1 month postoperatively, laboratory data were significantly worse when more portal veins was resected. After 2 months postoperatively, these data recovered to near normal levels.

CONCLUSION

The speed and rate of remnant liver regeneration primarily showed a strong correlation with the number of resected portal veins and the amount of removed liver parenchyma. The larger the resection ratio, the longer it took the liver to regenerate. We confirmed that recovery of the liver's functional aspects accompanies recovery of the RLV.

Model-based virtual patient analysis of human liver regeneration predicts critical perioperative factors controlling the dynamic mode of response to resection

BACKGROUND

Liver has the unique ability to regenerate following injury, with a wide range of variability of the regenerative response across individuals. Existing computational models of the liver regeneration are largely tuned based on rodent data and hence it is not clear how well these models capture the dynamics of human liver regeneration. Recent availability of human liver volumetry time series data has enabled new opportunities to tune the computational models for human-relevant time scales, and to predict factors that can significantly alter the dynamics of liver regeneration following a resection.

METHODS

We utilized a mathematical model that integrates signaling mechanisms and cellular functional state transitions. We tuned the model parameters to match the time scale of human liver regeneration using an elastic net based regularization approach for identifying optimal parameter values. We initially examined the effect of each parameter individually on the response mode (normal, suppressed, failure) and extent of recovery to identify critical parameters. We employed phase plane analysis to compute the threshold of resection. We mapped the distribution of the response modes and threshold of resection in a virtual patient cohort generated in silico via simultaneous variations in two most critical parameters.

RESULTS

Analysis of the responses to resection with individual parameter variations showed that the response mode and extent of recovery following resection were most sensitive to variations in two perioperative factors, metabolic load and cell death post partial hepatectomy. Phase plane analysis identified two steady states corresponding to recovery and failure, with a threshold of resection separating the two basins of attraction. The size of the basin of attraction for the recovery mode varied as a function of metabolic load and cell death sensitivity, leading to a change in the multiplicity of the system in response to changes in these two parameters.

CONCLUSIONS

Our results suggest that the response mode and threshold of failure are critically dependent on the metabolic load and cell death sensitivity parameters that are likely to be patient-specific. Interventions that modulate these critical perioperative factors may be helpful to drive the liver regenerative response process towards a complete recovery mode.

Modeling the Dynamics of Human Liver Failure Post Liver Resection

Liver resection is an important clinical intervention to treat liver disease. Following liver resection, patients exhibit a wide range of outcomes including normal recovery, suppressed recovery, or liver failure, depending on the regenerative capacity of the remnant liver. The objective of this work is to study the distinct patient outcomes post hepatectomy and determine the processes that are accountable for liver failure. Our model based approach shows that cell death is one of the important processes but not the sole controlling process responsible for liver failure. Additionally, our simulations showed wide variation in the timescale of liver failure that is consistent with the clinically observed timescales of post hepatectomy liver failure scenarios. Liver failure can take place either instantaneously or after a certain delay. We analyzed a virtual patient cohort and concluded that remnant liver fraction is a key regulator of the timescale of liver failure, with higher remnant liver fraction leading to longer time delay prior to failure. Our results suggest that, for a given remnant liver fraction, modulating a combination of cell death controlling parameters and metabolic load may help shift the clinical outcome away from post hepatectomy liver failure towards normal recovery.

Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.