Machine Learning Algorithms in Controlled Donation After Circulatory Death Under Normothermic Regional Perfusion: A Graft Survival Prediction Model.

Background: Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered using normothermic regional perfusion (NRP). Consequently, we explored the role of different machine learning-based classifiers as predictive models for graft survival.

Predicting waitlist dropout in hepatocellular carcinoma: a narrative review.

Background And Objective: Liver transplantation is the gold standard treatment for patients with hepatocellular carcinoma (HCC). Current allocation systems face a complex issue due to the imbalance between available organs and recipients. The prioritization of HCC patients remains controversial, leading to potential disparities in access to transplantation.

Machine learning in liver surgery: Benefits and pitfalls.

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future.

Machine perfusion and the prevention of ischemic type biliary lesions following liver transplant: What is the evidence?

The widespread uptake of different machine perfusion (MP) strategies for liver transplant has been driven by an effort to minimize graft injury. Damage to the cholangiocytes during the liver donation, preservation, or early posttransplant period may result in stricturing of the biliary tree and inadequate biliary drainage. This problem continues to trouble clinicians, and may have catastrophic consequences for the graft and patient.

Enumeration and Characterization of Circulating Tumor Cells in Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization.

Circulating tumor cells (CTCs), and particularly circulating cancer stem cells (cCSC), are prognostic biomarkers for different malignancies and may be detected using liquid biopsies. The ex vivo culture of cCSCs would provide valuable information regarding biological aggressiveness and would allow monitoring the adaptive changes acquired by the tumor in real time. In this prospective pilot study, we analyzed the presence of EpCAM CTCs using the IsoFlux system in the peripheral blood of 37 patients with hepatocellular carcinoma undergoing transarterial chemoembolization (TACE).

Fast-track protocols in laparoscopic liver surgery: Applicability and correlation with difficulty scoring systems.

Background: Few series have reported the utility of fast-track protocols (FTP) in minimally invasive liver surgery.

Aim: To report the applicability of FTP in minimally invasive liver surgery and to correlate with difficulty scores.

Methods: The series of patients undergoing minimally invasive liver surgery from 2014 was analyzed.

Short-term outcomes of robotic liver resection: An initial single-institution experience.

Background: Liver surgery has traditionally been characterized by the complexity of its procedures and potentially high rates of morbidity and mortality in inexperienced hands. The robotic approach has gradually been introduced in liver surgery and has increased notably in recent years. However, few centers currently perform robotic liver surgery and experiences in robot-assisted surgical procedures continue to be limited compared to the laparoscopic approach.

Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation.

Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a fair comparison, the United Network for Organ Sharing database was used with 4 different end-points (3 months, and 1, 2 and 5 years), with a total of 39, 189 D-R pairs and 28 donor and recipient variables.

Expert Consensus Guidelines on Minimally Invasive Donor Hepatectomy for Living Donor Liver Transplantation From Innovation to Implementation: A Joint Initiative From the International Laparoscopic Liver Society (ILLS) and the Asian-Pacific Hepato-Pancreato-Biliary Association (A-PHPBA).

Objective: The Expert Consensus Guidelines initiative on MIDH for LDLT was organized with the goal of safe implementation and development of these complex techniques with donor safety as the main priority.

Background: Following the development of minimally invasive liver surgery, techniques of MIDH were developed with the aim of reducing the short- and long-term consequences of the procedure on liver donors. These techniques, although increasingly performed, lack clinical guidelines.

Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.

Purpose Of Review: Classifiers based on artificial intelligence can be useful to solve decision problems related to the inclusion or removal of possible liver transplant candidates, and assisting in the heterogeneous field of donor-recipient (D-R) matching.

Recent Findings: Artificial intelligence models can show a great advantage by being able to handle a multitude of variables, be objective and help in cases of similar probabilities. In the field of liver transplantation, the most commonly used classifiers have been artificial neural networks (ANNs) and random forest classifiers.

Interaction between tacrolimus, MELD score and acute kidney injury after liver transplantation. Analysis on a large contemporary bicenter meld-era series.

Background: Acute kidney injury (AKI) after liver transplantation (LT) is a common problem with complex management. The aims were to analyze the profile of AKI-RIFLE categories in the post-transplant setting of a wide multicentre cohort of patients in the MELD era and to specifically determine the effect of tacrolimus-based (TACRO) immunosuppressive regimes on the development of AKI.

Methods: A retrospective analysis of 550 (2007-2012) consecutive patients transplanted at Reina Sofia, Cordoba, and King's College Hospital, London, was performed.

mTOR Expression in Liver Transplant Candidates with Hepatocellular Carcinoma: Impact on Histological Features and Tumour Recurrence.

(1) Background: The mammalian target of rapamycin (mTOR) pathway activation is critical for hepatocellular carcinoma (HCC) progression. We aimed to evaluate the mTOR tissue expression in liver transplant (LT) patients and to analyse its influence on post-LT outcomes. (2) Methods: Prospective study including a cohort of HCC patients who underwent LT (2012⁻2015).

Impact of Early Initiated Everolimus on the Recurrence of Hepatocellular Carcinoma After Liver Transplantation.

Background: Many centers implement everolimus-based immunosuppression in liver transplant patients with hepatocellular carcinoma. We aimed to explore the potential impact of early initiated everolimus on tumor recurrence after liver transplantation.

Methods: This study included 192 patients with hepatocellular carcinoma undergoing liver transplantation among who 64 individuals were prospectively enrolled (2012-2015) and received early initiated everolimus (ie, started between postoperative day 15 to 21), whereas the remaining 128 patients acted as historical controls without everolimus.

Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR-E]). The aim is to test the ANN-based methodology in a different European health care system in order to validate it. An ANN model was designed using a cohort of patients from King's College Hospital (KCH; n = 822).