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. Although the future faces significant challenges, it also holds a great potential.

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. Ischemic injury, as a result of compromised hepatic artery flow, is a well-known cause of biliary strictures, sepsis, and graft failure. However, very similar lesions can appear with a patent hepatic artery and these are known as ischemic type biliary lesions (ITBL) that are attributed to microcirculatory dysfunction rather than main hepatic arterial compromise. Both the warm and cold ischemic period duration appear to influence the onset of ITBL. All of the commonly used MP techniques deliver oxygen to the graft cells, and therefore may minimize the cholangiocyte injury and subsequently reduce the incidence of ITBL. As clinical experience and published evidence grows for these modalities, the impact they have on ITBL rates is important to consider. In this review, the evidence for the three commonly used MP strategies (abdominal normothermic regional perfusion [A-NRP], hypothermic oxygenated perfusion [HOPE], and normothermic machine perfusion [NMP] for ITBL prevention has been critically reviewed. Inconsistencies with ITBL definitions used in trials, coupled with variations in techniques of MP, make interpretation challenging. Overall, the evidence suggests that both HOPE and A-NRP prevent ITBL in donated after circulatory death grafts compared to cold storage. The evidence for ITBL prevention in donor after brain death grafts with any MP technique is weak.

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. Iwate, Southampton and Gayet's scores were compared as predictors of FTP adherence. Accomplishment of FTP was considered within 24-h, 48-h and 72-h. Multivariate models were performed to define discharge < 24 h, < 72 h, complications and readmissions. RESULTS: From 160 cases, 78 were candidates for FTP, of which 22 (28.2%), 19 (24.4%) and 14 (17.9%) were discharged in < 24-h, 48-h and 72-h, respectively (total = 71.5%). Iwate, Southampton and Gayet's scores achieved area under the receiver operating characteristic values for < 24-h stay of 0.780, 0.687 and 0.698, respectively. Sensitivity and specificity values for the best score (Iwate) were 87.7% and 66.7%, respectively (cutoff = 5.5). In multivariate models, < 72 h stay and complications revealed body mass index as a risk factor independent from difficulty scores. CONCLUSION: The development of aggressive FTP is feasible and < 24-h stay can be achieved even in moderate and advanced complexity cases. Difficulty scores, including body mass index value, may be useful to predict which cases may adhere to these protocols.

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. AIM: To analyze the outcomes and feasibility of an initial robotic liver program implemented in an experienced laparoscopic hepatobiliary center. METHODS: A total of forty consecutive patients underwent robotic liver resection (da Vinci Xi, intuitive.com, United States) between June 2019 and January 2021. Patients were prospectively followed and retrospectively reviewed. Clinicopathological characteristics and perioperative and short-term outcomes were analyzed. Data are expressed as mean and standard deviation. The study was approved by the Institutional Review Board. RESULTS: The mean age of patients was 59.55 years, of which 18 (45%) were female. The mean body mass index was 29.41 kg/m². Nine patients (22.5%) were cirrhotic. Patients were divided by type of resection as follows: Ten segmentectomies, three wedge resections, ten left lateral sectionectomies, six bisegmentectomies (two V-VI bisegmentectomies and four IVb-V bisegmentectomies), two right anterior sectionectomies, five left hepatectomies and two right hepatectomies. Malignant lesions occurred in twenty-nine (72.5%) of the patients. The mean operative time was 258.11 min and two patients were transfused intraoperatively (5%). Inflow occlusion was used in thirty cases (75%) and the mean total clamping time was 32.62 min. There was a single conversion due to uncontrollable hemorrhage. Major postoperative complications (Clavien-Dindo > IIIb) occurred in three patients (7.5%) and mortality in one (2.5%). No patient required readmission to the hospital. The mean hospital stay was 5.6 d. CONCLUSION: Although robotic hepatectomy is a safe and feasible procedure with favorable short-term outcomes, it involves a demanding learning curve that requires a high level of training, skill and dexterity.

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. Modelling techniques were divided into two groups: 1) classical statistical methods, including Logistic Regression (LR) and Naïve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR. For the 5-years end-point, LR (AUC = 0.654) outperformed several machine learning techniques, such as MLP (AUC = 0.599), GB (AUC = 0.600), SVM (AUC = 0.624) or RF (AUC = 0.644), among others. Moreover, LR also outperformed standard scores. The same pattern was reproduced for the others 3 end-points. Complex machine learning methods were not able to improve the performance of liver allocation, probably due to the implicit limitations associated to the collection process of the database.

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. METHODS: A group of 12 international MIDH experts, 1 research coordinator, and 8 junior faculty was assembled. Comprehensive literature search was made and studies classified using the SIGN method. Based on literature review and experts opinions, tentative recommendations were made by experts subgroups and submitted to the whole experts group using on-line Delphi Rounds with the goal of obtaining >90% Consensus. Pre-conference meeting formulated final recommendations that were presented during the plenary conference held in Seoul on September 7, 2019 in front of a Validation Committee composed of LDLT experts not practicing MIDH and an international audience. RESULTS: Eighteen Clinical Questions were addressed resulting in 44 recommendations. All recommendations reached at least a 90% consensus among experts and were afterward endorsed by the validation committee. CONCLUSIONS: The Expert Consensus on MIDH has produced a set of clinical guidelines based on available evidence and clinical expertise. These guidelines are presented for a safe implementation and development of MIDH in LDLT Centers with the goal of optimizing donor safety, donor care, and recipient outcomes.

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. ANNs are excellent tools for finding patterns which are far too complex for a clinician and are capable of generating near-perfect predictions on the data on which they are fit, yielding excellent prediction capabilities reaching 95% for 3 months graft survival. On the other hand, RF can overcome ANNs in some of their limitations, mainly because of the lack of information on the variables they provide. Random forest algorithms may allow for improved confidence with the use of marginal organs and better outcome after transplantation. SUMMARY: ANNs and random forest can handle a multitude of structured and unstructured parameters, and establish non explicit relationships among risk factors of clinical relevance.

Preliminary results from the use of intraoperative real-time biliary oxygen monitoring in liver transplantation.

BACKGROUND AND AIMS: Biliary anastomosis is a frequent area of complications after liver transplantation (LT) and a potential area of "microangiopathy". The concept of a "marginal bile duct" is unexplored. The main aim was to make a preliminary evaluation of the utility of an innovative real-time oxygen microtension (pO2mt) testing device for the assessment of bile duct viability during LT and to correlate these pO2mt values with microvascular tissue quality by histopathology and outcomes. PATIENTS AND METHODS: Observational prospective cohort study with 23 patients. Oxygen microtension measurements were made placing a micropO2 probe in different areas of recipient and donor's bile duct intraoperative. RESULTS: Mean pO2mt in the graft bile duct at the level of the anastomosis 103.82 (31-157) mm Hg, being 121.52 (55-174) mm Hg 1.5 cm proximal to the hilar plate (P < 0.001). Mean pO2mt in the recipient's bile duct was 117.87 (62-185) mm Hg, while a value of 137.30 (81-198) mm Hg was observed 1.5 cm distal to the anastomosis (P < 0.001). Cystic duct resection (12 cases) was also related with higher pO2mt values at anastomosis [117.8 (93-157) vs 88.54 (31-124) mm Hg] and distal to anastomosis [135.6 (111-174) vs 106.2 (55-133) mm Hg; P < 0.001]. Patients with 1-, 3-, and 12-month biliary complications had significantly lower pO2mt in the intraoperative measurements. CONCLUSION: Our preliminary results show that distal borders of donor and recipient bile ducts may be low-vascularized areas. Tissue pO2mt is significantly higher in areas close to the hilar plate and to the duodenum in donor and recipient's sides, respectively. Bile duct injury and biliary complications are associated with worse tissue pO2mt.

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). The ANN was trained and tested using KCH pairs for both 3- and 12-month survival models. End points were probability of graft survival (correct classification rate [CCR]) and nonsurvival (minimum sensitivity [MS]). The final model is a rule-based system for facilitating the decision about the most appropriate D-R matching. Models designed for KCH had excellent prediction capabilities for both 3 months (CCR-area under the curve [AUC] = 0.94; MS-AUC = 0.94) and 12 months (CCR-AUC = 0.78; MS-AUC = 0.82), almost 15% higher than the best obtained by other known scores such as Model for End-Stage Liver Disease and balance of risk. Moreover, these results improve the previously reported ones in the multicentric MADR-E database. In conclusion, the use of ANN for D-R matching in LT in other health care systems achieved excellent prediction capabilities supporting the validation of these tools. It should be considered as the most advanced, objective, and useful tool to date for the management of waiting lists. Liver Transplantation 24 192-203 2018 AASLD.