Predicting survival after liver transplantation in patients with hepatocellular carcinoma using the LiTES-HCC score

Simulating the impact of survival benefit-based liver transplant organ allocation

BACKGROUND AND AIMS

In the United States and much of the world, prioritization for a deceased donor liver transplant focuses on sickest-first, based on allocating organs using the MELD score. There have been calls to instead allocate organs based on transplant survival benefit, but the impact of such a system on the broader waitlist population is unknown.

APPROACH AND RESULTS

We performed a simulation study using the Liver Simulated Allocation Model to compare the current US system of liver allocation to the one using different time horizons, focused on pretransplant survival only, posttransplant survival only, and survival benefit (difference of posttransplant survival and pretransplant survival). Changing liver allocation to a survival benefit-based system was simulated to lead to a small improvement in average patient-level posttransplant survival (mean survival over a 5-year time horizon of 4.24 y vs. 4.19 y in the current system). However, this small improvement was associated with a simulated decrease in transplants and an increase in waitlist mortality of 400 deaths per year. The resulting net benefit overall (pretransplant deaths and posttransplant survival) was negligible under a survival benefit-based allocation approach.

CONCLUSIONS

Our simulations predicted that survival benefit-based allocation would only increase posttransplant survival by an average of 18 days per recipient at the expense of a simulated increase in waitlist mortality of 400 deaths per year. In the current practice of liver transplantation, with the sickest-first allocation operating in a system where transplant physicians ration organs to maximize outcomes, the overall survival benefit is maintained and not compromised.

Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery

Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.

Predicting long-term survival among patients with HCC

BACKGROUND

Prognosticating survival among patients with HCC and cirrhosis must account for both the tumor burden/stage, as well as the severity of the underlying liver disease. Although there are many staging systems used to guide therapy, they have not been widely adopted to predict patient-level survival after the diagnosis of HCC. We sought to develop a score to predict long-term survival among patients with early- to intermediate-stage HCC using purely objective criteria.

METHODS

Retrospective cohort study among patients with HCC confined to the liver, without major medical comorbidities within the Veterans Health Administration from 2014 to 2023. Tumor data were manually abstracted and combined with clinical and laboratory data to predict 5-year survival from HCC diagnosis using accelerated failure time models. The data were randomly split using a 75:25 ratio for training and validation. Model discrimination and calibration were assessed and compared to other HCC staging systems.

RESULTS

The cohort included 1325 patients with confirmed HCC. A risk score using baseline clinical, laboratory, and HCC-related survival had excellent discrimination (integrated AUC: 0.71 in the validation set) and calibration (based on calibration plots and Brier scores). Models had superior performance to the BCLC and ALBI scores and similar performance to the combined BCLC-ALBI score.

CONCLUSIONS

We developed a risk score using purely objective data to accurately predict long-term survival for patients with HCC. This score, if validated, can be used to prognosticate survival for patients with HCC, and, in the setting of liver transplantation, can be incorporated to consider the net survival benefit of liver transplantation versus other curative options.

Recurrence risk prediction models for hepatocellular carcinoma after liver transplantation

Liver transplantation (LT) is an effective method for curing hepatocellular carcinoma (HCC). However postoperative tumor recurrence can lead to higher mortality rates. To select suitable candidates for LT, the Milan Criteria (MC) were first proposed based on tumor morphological characteristics. For those patients who meet the MC, the MC can effectively reduce the postoperative tumor recurrence rate and improve the prognosis of patients undergoing LT. It has always been internationally recognized as the gold standard for selecting candidates for LT, marking a milestone in the history of LT for HCC. However, its strict conditions exclude some HCC patients who could benefit from LT. Therefore, comprehension consideration criteria, including serum biomarkers, tumor histology, and other factor, have been continuously proposed in addition to tumor morphology. This article summaries the prediction model for HCC recurrence after LT from five aspects: tumor morphology, serum markers, histopathology, cellular inflammatory factors and downstaging treatment before transplantation. The aim is to assist clinicians in accurately assessing HCC status, selecting appropriate liver transplant candidates, maximize graft and patients' survival, and optimizing the utilization of social health resources.

mTOR inhibitor reduces nontumour-related death in liver transplantation for hepatocellular carcinoma

Sirolimus is a regularly applied immunosuppressant for patients undergoing liver transplantation (LT) for hepatocellular carcinoma (HCC). Sirolimus not only significantly inhibits HCC recurrence but also protects renal function. However, the improvement effect of sirolimus on nontumour-related death in patients is still unknown. The aim of our study was to investigate the therapeutic effect of sirolimus on nontumour-related deaths. In this study, we retrospectively enrolled 403 LT patients with HCC from January 1, 2015, to December 31, 2018. The median follow-up time was 47.1 months. The patients were divided into the sirolimus group (N = 184) and the sirolimus-free group (N = 219). There were no significant differences between the sirolimus group and the sirolimus-free group in survival (P = 0.054). In transplant patients who exceeded the Milan or Hangzhou criteria, the sirolimus group achieved higher survival than the sirolimus-free group (P = 0.005; P = 0.02). Moreover, multivariate analysis showed that sirolimus strongly reduced the hazard ratio (HR) for nontumour-related death in LT patients who exceeded the Milan (HR: 0.42; 95% CI: 0.18-1; P = 0.05) or Hangzhou criteria (HR: 0.26; 95% CI: 0.08-0.89; P = 0.032). HCC recurrence increased the risk of nontumour-related death. In conclusion, sirolimus-based immunosuppression can significantly reduce nontumour-related death in LT patients who exceed the criteria for transplantation. In addition, this finding will further promote the application of sirolimus after liver transplantation for hepatocellular carcinoma.

Trends in Survival for Adult Organ Transplantation

Objective

Intent-to-treat analysis follows patients from listing to death, regardless of their transplant status, and aims to provide a more holistic scope of the progress made in adult solid-organ transplantation.

Background

Many studies have shown progress in waitlist and post-transplant survival for adult kidney, liver, heart, and lung transplants, but there is a need to provide a more comprehensive perspective of transplant outcomes for patients and their families.

Methods

Univariable and multivariable Cox regression analyses were used to analyze factors contributing to intent-to-treat survival in 813,862 adults listed for kidney, liver, heart, and lung transplants. The Kaplan-Meier method was used to examine changes in waitlist, post-transplant, and intent-to-treat survival. Transplantation rates were compared using χ2 tests.

Results

Intent-to-treat survival has steadily increased for liver, heart, and lung transplants. The percentage of patients transplanted within 1 year significantly increased for heart (57.4% from 52.9%) and lung (73.5% from 33.2%). However, the percentage of patients transplanted within 1 year significantly decreased from 35.8% to 21.2% for kidney transplant. Notably, intent-to-treat survival has decreased for kidneys despite increases in waitlist and post-transplant survival, likely because of the decreased transplant rate.

Conclusion

Intent-to-treat survival steadily improved for liver, heart, and lung transplant over the 30-year study period. Continued advancements in allocation policy, immunosuppression, and improved care of patients on the waitlist may contribute to further progress in outcomes of all organs, but the increasing discrepancy in supply and demand of donor kidneys is alarming and has impeded the progress of kidney intent-to-treat survival.

Liver transplantation meets gastrointestinal cancer

Liver transplantation (LT) has emerged as a standard of care for patients with end-stage liver disease, providing a life-saving intervention for patients with severely compromised liver function in both the acute and chronic setting. While LT has also become a routine procedure for early-stage hepatocellular carcinoma (HCC), offering a potential cure by treating both the tumor and the underlying liver disease, its relevance in the context of other malignancies such as cholangiocellular carcinoma (CCA), combined hepatocellular-cholangiocarcinoma (cHCC-CCA) or liver metastases is still the subject of intense debate and no definite recommendations have yet been established. This review summarizes the current therapeutic standards in the context of LT for gastrointestinal malignancies and provides a reflection and outlook on current scientific and clinical developments.

Insights into the history and tendency of liver transplantation for liver cancer: a bibliometric-based visual analysis

Research on liver transplantation (LT) for liver cancer has gained increasing attention. This paper has comprehensively described the current status, hotspots and trends in this field. A total of 2991 relevant articles from 1 January 1963 to 28 February 2023 were obtained from the Web of Science Core Collection. VOSviewer and CiteSpace software were utilized as bibliometric tools to analyze and visualize knowledge mapping. Between 1963 and 2023, the number of papers in the area of LT for liver cancer increased continuously. A total of 70 countries/regions, 2303 institutions and 14 840 researchers have published research articles, with the United States and China being the two most productive countries. Our bibliometric-based visual analysis revealed the expansion of LT indications for liver cancer and the prevention/treatment of cancer recurrence as ongoing research hotspots over the past decades. Meanwhile, emerging studies also focus on downstaging/bridging treatments before LT and the long-term survival of LT recipient, in particular the precise application of immunosuppressants.

Optimal Liver Transplantation Criteria for Hepatocellular Carcinoma

Liver transplantation continues to be the optimal treatment for hepatocellular carcinoma (HCC). Given the limited organ supply, patient selection for liver transplant must carefully balance tumor progression with risk of recurrence posttransplant. There are several pretransplant selection criteria that incorporate biomarkers as well as imaging modality to risk-stratify patients as we continue to look for the optimal transplant cutoff for patients with HCC, which should be transplant-center specific, and account for organ availability and dynamic response to locoregional therapy.

Survival benefit from liver transplantation for patients with and without hepatocellular carcinoma

Background & Aims

In the USA, inequal liver transplantation (LT) access exists between patients with and without hepatocellular carcinoma (HCC). Survival benefit considers survival without and with LT and could equalise LT access. We calculated bias-corrected LT survival benefit for patients with(out) HCC who underwent a transplant, based on longitudinal data in a recent United States cohort.

Methods

Adult LT candidates with(out) HCC between 2010 and 2019 were included. Waitlist survival over time was contrasted to post-transplant survival, to estimate 5-year survival benefit from the moment of LT. Waitlist survival was modelled with a bias-corrected Cox regression, and post-transplant survival was estimated through Cox proportional hazards regression.

Results

Mean HCC survival without LT was always lower than non-HCC waitlist survival. Below model for end-stage liver disease (sodium) (MELD(-Na)) 30, patients with HCC gained more life-years from LT than patients without HCC at the same MELD(-Na) score. Only patients without HCC below MELD(-Na) 9 had negative benefit. Most patients with HCC underwent a transplant below MELD(-Na) 14, and most patients without HCC underwent a transplant above MELD(-Na) 26. Liver function [MELD(-Na), albumin] was the main predictor of 5-year benefit. Therefore, during 5 years, most patients with HCC gained 0.12 to 1.96 years from LT, whereas most patients without HCC gained 2.48 to 3.45 years.

Conclusions

On an individual level, performing a transplant in patients with HCC resulted in survival benefit. However, on a population level, benefit was indirectly decreased, as patients without HCC were likely to gain more survival owing to decreased liver function. For patients who underwent a transplant, a constructed online calculator estimates 5-year survival benefit given specific patient characteristics. Survival benefit scores could serve to equalise LT access.

Impact and implications

Benefit is a comparison of the survival with and without liver transplantation, and it is important when deciding who should undergo a transplant. Liver function is most important when predicting possible benefit from transplantation. Patients with liver cancer die sooner on the waiting list than similar patients without liver cancer. However, patients with liver cancer more often have better liver function. Most patients without liver cancer derive more benefit from transplantation than patients with liver cancer.

Cumulative exposure to tacrolimus during early period after liver transplantation does not affect the recurrence of hepatocellular carcinoma

The clinical effects of tacrolimus (TAC) exposure on hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) remain unclear. In this retrospective single centric study, 512 patients who underwent LT for HCC were divided into four groups according to cumulative exposure to tacrolimus (CET) during 3 months after LT: conventional (n = 218), aggressive minimization (n = 32), minimization (n = 161), and high exposure (n = 101). Impact of CET on HCC recurrence and death were analyzed. Compared with the conventional group, the other three CET groups showed a similar risk of HCC recurrence. The aggressive minimization group showed a higher risk [hazard ratio (HR) 5.64, P < 0.001] and the high exposure group showed a marginal risk (HR 1.67, P = 0.081) of overall death compared to the conventional group. CET during 3 months was not associated with HCC recurrence in the matched cohort and various subgroups. TAC minimization is not effective to prevent HCC recurrence but could result in higher mortality in LT recipients.

Estimating Microvascular Invasion in Patients with Resectable Multinodular Hepatocellular Carcinoma by Using Preoperative Contrast-Enhanced MRI: Establishment and Validation of a Risk Score

Objective

To determine the preoperative clinicoradiological factors to predict microvascular invasion (MVI) in patients with resectable multinodular hepatocellular carcinoma (mHCC), and further to establish and validate a stratified risk scoring system.

Methods

Two hundred and seventy-three patients with pathologically confirmed mHCC (≥2 lesions) without major vascular invasion and biliary tract tumor thrombosis, who underwent preoperative contrast-enhanced MRI and hepatectomy, were consecutively enrolled (training/validation cohort=193/80). Preoperative clinicoradiological variables were collected and analyzed. The multivariable logistic regression was performed to determine the independent predictors of MVI and create a risk score system. The C-index, calibration curve and decision curve were used to evaluate the performance of the risk score. A risk score-based prognostic stratification system was performed in mHCC patients. The risk score system was further verified in the validation cohort.

Results

AFP > 400 ng/mL, presence of satellite nodule, mosaic architecture and increased total tumor diameter were independent predictors of MVI while fat in mass was an independent protective factor of MVI. The risk score yielded satisfactory C-index values (training/validation cohort: 0.777/0.758) and fitted well in calibration curves. Decision curve analysis further confirmed its clinical utility. Based on the risk score, mHCC patients were stratified into high-/low-MVI-risk subgroups with significantly different recurrence-free survival (both P < 0.001).

Conclusion

The presented risk score incorporating clinicoradiological parameters could stratify mHCC patients into high-risk and low-risk subgroups and predict prognosis in patients with resectable mHCC.

Machine learning integrations develop an antigen-presenting-cells and T-Cells-Infiltration derived LncRNA signature for improving clinical outcomes in hepatocellular carcinoma

As a highly heterogeneous cancer, the prognostic stratification and personalized management of hepatocellular carcinoma (HCC) are still challenging. Recently, Antigen-presenting-cells (APCs) and T-cells-infiltration (TCI) have been reported to be implicated in modifying immunology in HCC. Nevertheless, the clinical value of APCs and TCI-related long non-coding RNAs (LncRNAs) in the clinical outcomes and precision treatment of HCC is still obscure. In this study, a total of 805 HCC patients were enrolled from three public datasets and an external clinical cohort. 5 machine learning (ML) algorithms were transformed into 15 kinds of ML integrations, which was used to construct the preliminary APC-TCI related LncRNA signature (ATLS). According to the criterion with the largest average C-index in the validation sets, the optimal ML integration was selected to construct the optimal ATLS. By incorporating several vital clinical characteristics and molecular features for comparison, ATLS was demonstrated to have a relatively more significantly superior predictive capacity. Additionally, it was found that the patients with high ATLS score had dismal prognosis, relatively high frequency of tumor mutation, remarkable immune activation, high expression levels of T cell proliferation regulators and anti-PD-L1 response as well as extraordinary sensitivity to Oxaliplatin/Fluorouracil/Lenvatinib. In conclusion, ATLS may serve as a robust and powerful biomarker for improving the clinical outcomes and precision treatment of HCC.

Current status of liver transplantation for non-B non-C liver cirrhosis and hepatocellular carcinoma

Recently, non-B non-C chronic liver diseases, including alcoholic liver disease (ALD) and nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH), have markedly increased worldwide. Liver transplantation (LT) is an effective curative therapy for hepatocellular carcinoma (HCC) as well as decompensated liver cirrhosis. In Japan, where the source of liver grafts is strongly dependent on living donors, efforts have been made to unify the indications for eligibility of HCC patients for LT, leading to the development of 5-5-500 criteria. Along with the expansion of eligibility for LT, the current changing trends in underlying liver diseases of LT recipients, which are related to the rising tide of non-B non-C cirrhosis and HCC, are highlighting the importance of peri-transplant management of patients with various comorbidities. The post-LT prognosis of patients with ALD is significantly affected by de novo malignancies and metabolic syndrome-related complications as well as posttransplant alcohol relapse. NAFLD/NASH patients often suffer from obesity, type 2 diabetes mellitus, and other metabolic syndrome-related disorders, and nonneoplastic factors such as cardiovascular events and recurrence of NAFLD/NASH have a significant impact on post-LT outcomes. Patient management in the peri-transplant period as well as risk assessment for LT are key to improving post-LT outcomes in the era of a growing number of cases of LT for non-B non-C liver diseases.

Time-varying Comparison of All-cause Mortality After Liver Transplantation Between Recipients With and Without Hepatocellular Carcinoma: A Population-based Cohort Study Using the United Kingdom Liver Transplant Registry

BACKGROUND

Accurately identifying time-varying differences in the hazard of all-cause mortality after liver transplantation (LT) between recipients with and without hepatocellular carcinoma (HCC) may inform patient selection and organ allocation policies as well as post-LT surveillance protocols.

METHODS

A UK population-based study was carried out using 9586 LT recipients. The time-varying association between HCC and post-LT all-cause mortality was estimated using an adjusted flexible parametric model (FPM) and expressed as hazard ratios (HRs). Differences in this association by transplant year were then investigated. Non-cancer-specific mortality was compared between HCC and non-HCC recipients using an adjusted subdistribution hazard model.

RESULTS

The HR comparing HCC recipients with non-HCC recipients was below one immediately after LT (1-mo HR = 0.76; 95% confidence interval [CI], 0.59-0.99; P = 0.044). The HR then increased sharply to a maximum at 1.3 y (HR = 2.07; 95% CI, 1.70-2.52; P < 0.001) before decreasing. The hazard of death was significantly higher in HCC recipients than in non-HCC recipients between 4 mo and 7.4 y post-LT. There were no notable differences in the association between HCC and the post-LT hazard of death by transplant year. The estimated non-cancer-specific subdistribution HR for HCC was 0.93 (95% CI, 0.80-1.09; P = 0.390) and not found to vary over time.

CONCLUSIONS

FPMs can provide a more precise comparison of post-LT hazards of mortality between HCC and non-HCC patients. The results provide further evidence that some HCC patients have extra-hepatic spread at the time of LT, which has implications for optimal post-LT surveillance protocols.

Prediction models of hepatocellular carcinoma recurrence after liver transplantation: A comprehensive review

Liver transplantation (LT) is one of the most effective treatments for hepatocellular carcinoma (HCC). Although LT eliminates HCC and greatly reduces recurrence, some patients experience recurrence after LT. Criteria and models for screening patients with a high probability of HCC recurrence after LT, starting with the Milan criteria, have been published. These models have changed over time, but a standard has not been established. We summarized HCC prediction models after LT by focusing on the application of radiologic, serologic, and pathologic factors and recent trends. This review will look at studies that are based on living donor LT and deceased donor LT, as well as studies that downstaging procedures have been performed preoperatively. This ultimately aims to help make decisions for evaluating the HCC state and selecting candidates for LT according to the circumstances of each transplantation center.

Accurate long-term prediction of death for patients with cirrhosis

BACKGROUND AND AIMS

Cirrhosis is a major cause of death and is associated with extensive health care use. Patients with cirrhosis have complex treatment choices due to risks of morbidity and mortality. To optimally counsel and treat patients with cirrhosis requires tools to predict their longer-term liver-related survival. We sought to develop and validate a risk score to predict longer-term survival of patients with cirrhosis.

APPROACH AND RESULTS

We conducted a retrospective cohort study of adults with cirrhosis with no major life-limiting comorbidities. Adults with cirrhosis within the Veterans Health Administration were used for model training and internal validation, and external validation used the OneFlorida Clinical Research Consortium. We used four model-building approaches including variables predictive of cirrhosis-related mortality, focused on discrimination at key time points (1, 3, 5, and 10 years). Among 30,263 patients with cirrhosis ≤75 years old without major life-limiting comorbidities and complete laboratory data during the baseline period, the boosted survival tree models had the highest discrimination, with 1-year, 3-year, 5-year, and 10-year survival rates of 0.77, 0.81, 0.84, and 0.88, respectively. The 1-year, 3-year, and 5-year discrimination was nearly identical in external validation. Secondary analyses with imputation of missing data and subgroups by etiology of liver disease had similar results to the primary model.

CONCLUSIONS

We developed and validated (internally and externally) a risk score to predict longer-term survival of patients with cirrhosis. This score would transform management of patients with cirrhosis in terms of referral to specialty care and treatment decision-making for non-liver-related care.

The impact of biological features for a better prediction of posttransplant hepatocellular cancer recurrence

PURPOSE OF REVIEW

Morphological criteria (i.e., Milan Criteria) have been considered for a long time to be the best tool for selecting patients with hepatocellular cancer (HCC) waiting for liver transplantation (LT). In the last ten years, a refinement of the selection criteria has been observed, with the introduction of biological tumor characteristics enabling to enlarge the number of potential transplant candidates and to select LT candidates with a lower risk of posttransplant recurrence.

RECENT FINDINGS

Several biological tumor aspects have been explored and validated in international cohorts to expand the ability to predict patients at high risk for recurrence. Alpha-fetoprotein, radiological response to locoregional treatments, and other more recently proposed markers have been principally explored. Moreover, more complex statistical approaches (i.e., deep learning) have been advocated to explore the nonlinear intercorrelations between the investigated features.

SUMMARY

The addition of biological aspects to morphology has improved the ability to discriminate among high- and low-risk patients for recurrence. New prognostic algorithms based on the more sophisticated artificial intelligence approach are further improving the capability to select LT candidates with HCC.

NCA-GA-SVM: A new two-level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Hepatocellular carcinoma (HCC) is one of the major challenges facing biomedical research. Despite the high lethality, methods to predict mortality for this type of aggressive malignant tumor are insufficient. Machine learning is recognized by many authors as a valuable, yet poorly studied tool in this field. Undoubtedly, searching for new feature selection methods is significant in building an effective machine-learning model. In this study, we propose the novel hybrid model using neighborhood components analysis, genetic algorithm and support vector machine classifier (NCA-GA-SVM). Because SVM works with default parameters characterized by low classification results, we decided to use GA for the proper optimization and feature selection. As reported in the available literature, NCA and GA obtain high classification results. Here, we decided to combine these approaches, building a two-level algorithm for HCC fatality prognosis. We used a well-known dataset collected from 165 patients at Coimbra's Hospital and University Center, Portugal. Our results revealed 96.36% classification accuracy and 95.52% F1-score. Additionally, we compared all data for these metrics published so far. We demonstrated that our algorithm achieved the highest accuracy and can be successfully applied for the assessment of hepatocellular carcinoma mortality in the future. Our findings bring methodological value for future HCC studies and emphasize the possibility of using machine-learning techniques to improve the quality of medical decisions.

Predicting recurrence of hepatocellular carcinoma after liver transplantation using a novel model that incorporates tumor and donor-related factors

Evidence suggests that liver graft quality impacts on posttransplant recurrence of hepatocellular carcinoma (HCC). As of today, selection criteria only use variables related to tumor characteristics. Within the Scientific Registry of Transplant Recipients, we identified patients with HCC who underwent liver transplantation between 2004 and 2016 (development cohort, n = 10 887). Based on tumor recurrence rates, we fitted a competing-risk regression incorporating tumor- and donor-related factors, and we developed a prognostic score. Results were validated both internally and externally in the Australia and New Zealand Liver Transplant Registry. Total tumor diameter (subhazard ratio [sub-HR] 1.52 [1.28-1.81]), alpha-feto protein (sub-HR 1.27 [1.23-1.32], recipient male gender (sub-HR 1.43 [1.18-1.74]), elevated donor body mass index (sub-HR 1.26 [1.01-1.58]), and shared graft allocation policy (sub-HR 1.20 [1.01-1.43]) were independently associated with tumor recurrence. We next developed the Darlica score (sub-HR 2.72 [2.41-3.08] P < 0.001) that allows identifying risky combinations between a given donor and a given recipient. Results were validated internally (n = 3 629) and externally in the Australia and New Zealand Liver Transplant Registry (n = 370). The current score is based on variables that are readily available at the time of graft offer. It allows identifying hazardous donor-recipient combinations in terms of risk of tumor recurrence and overall survival.

Blockchain and artificial intelligence technology in e-Health

Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.

Liver transplantation in malignant disease

Liver transplantation for malignant disease has gained increasing attention as part of transplant oncology. Following the implementation of the Milan criteria, hepatocellular carcinoma (HCC) was the first generally accepted indication for transplantation in patients with cancer. Subsequently, more liberal criteria for HCC have been developed, and research on this topic is still ongoing. The evident success of liver transplantation for HCC has led to the attempt to extend its indication to other malignancies. Regarding perihilar cholangiocarcinoma, more and more evidence supports the use of liver transplantation, especially after neoadjuvant therapy. In addition, some data also show a benefit for selected patients with very early stage intrahepatic cholangiocarcinoma. Hepatic epithelioid hemangioendothelioma is a very rare but nonetheless established indication for liver transplantation in primary liver cancer. In contrast, patients with hepatic angiosarcoma are currently not considered to be optimal candidates. In secondary liver tumors, neuroendocrine cancer liver metastases are an accepted but comparability rare indication for liver transplantation. Recently, some evidence has been published supporting the use of liver transplantation even for colorectal liver metastases. This review summarizes the current evidence for liver transplantation for primary and secondary liver cancer.

Revisiting Surgical Strategies for Hepatocellular Carcinoma With Microvascular Invasion

Although liver resection (LR) and liver transplantation (LT) are widely considered as potentially curative therapies for selected patients with hepatocellular carcinoma (HCC); however, there is still high risk of tumor recurrence in majority of HCC patients. Previous studies demonstrated that the presence of microvascular invasion (MVI), which was defined as the presence of tumor emboli within the vessels adjacent to HCC, was one of the key factors of early HCC recurrence and poor surgical outcomes after LR or LT. In this review, we evaluated the impact of current MVI status on surgical outcomes after curative therapies and aimed to explore the surgical strategies for HCC based on different MVI status with evidence from pathological examination. Surgical outcomes of HCC patients with MVI have been described as a varied range after curative therapies due to a broad spectrum of current definitions for MVI. Therefore, an international consensus on the validated definition of MVI in HCC is urgently needed to provide a more consistent evaluation and reliable prediction of surgical outcomes for HCC patients after curative treatments. We concluded that MVI should be further sub-classified into MI (microvessel invasion) and MPVI (microscopic portal vein invasion); for HCC patients with MPVI, local R0 resection with a narrow or wide surgical margin will get the same surgical results. However, for HCC patients with MI, local surgical resection with a wide and negative surgical margin will get better surgical outcomes. Nowadays, MVI status can only be reliably confirmed by histopathologic evaluation of surgical specimens, limiting its clinical application. Taken together, preoperative assessment of MVI is of utmost significance for selecting a reasonable surgical modality and greatly improving the surgical outcomes of HCC patients, especially in those with liver cirrhosis.

Liver Transplantation for T2 Hepatocellular Carcinoma during the COVID-19 Pandemic: A Novel Model Balancing Individual Benefit against Healthcare Resources

Simple Summary

Using Italian national data from more than 8000 patients, we proposed a novel model calculating the net benefit of liver transplantation (individual benefit minus harm to others on the waiting list) in T2 hepatocellular carcinoma (HCC) patients in different scenarios of transplant activity reduction. Our results show that the transplant net benefit is closely related to the decrease in the number of organs, but it is also higher in T2 HCC patients than in non-HCC patients with the same model for end-stage liver disease (MELD) scores. Our model supports liver transplantation for T2 HCC with the highest net benefit also during crises such as COVID-19.

Abstract

The COVID-19 pandemic caused temporary drops in the supply of organs for transplantation, leading to renewed debate about whether T2 hepatocellular carcinoma (HCC) patients should receive priority during these times. The aim of this study was to provide a quantitative model to aid decision-making in liver transplantation for T2 HCC. We proposed a novel ethical framework where the individual transplant benefit for a T2 HCC patient should outweigh the harm to others on the waiting list, determining a “net benefit”, to define appropriate organ allocation. This ethical framework was then translated into a quantitative Markov model including Italian averages for waiting list characteristics, donor resources, mortality, and transplant rates obtained from a national prospective database (n = 8567 patients). The net benefit of transplantation in a T2 HCC patient in a usual situation varied from 0 life months with a model for end-stage liver disease (MELD) score of 15, to 34 life months with a MELD score of 40, while it progressively decreased with acute organ shortage during a pandemic (i.e., with a 50% decrease in organs, the net benefit varied from 0 life months with MELD 30, to 12 life months with MELD 40). Our study supports the continuation of transplantation for T2 HCC patients during crises such as COVID-19; however, the focus needs to be on those T2 HCC patients with the highest net survival benefit.