Liver transplantation as a therapeutic option for hepatocellular carcinoma

Better outcomes of the patients receiving liver transplantation for viral hepatitis and hepatocellular carcinoma (HCC) are achieved by improved patient selection and perioperative treatment with antiviral agents including lamivudine, ribavirin and interferon. Patient selection is accomplished by high-quality imaging as well as exclusion of patients with large tumors, obvious extrahepatic disease or macroscopic vascular invasion. Using such criteria, a 5-year survival of 92% has been reached in the Queensland Liver Transplant Service on a small number of highly selected patients with HCC. The treatment algorithm of Makuuchi has guided us in recommending resection, estimating to what extent the liver resection can be performed safely, and timing liver transplantation when it is the only option. Adult-to-adult living-donor liver transplantation is being performed safely in many centers worldwide. The transplantation of liver from living donors to HCC patients, when standard criteria for the likelihood of good outcomes are fulfilled, will increase in Japan in the near future. Copyright (C) 2002 S. Karger AG, Basel.

Cadherin/catenin complex appears to be intact in hepatocellular carcinomas from Australia and South Africa

Background and aim: E-cadherin binds to beta-catenin to form the cadherin/catenin complex required for strong cell adhesion. Inactivation of this complex in tumors facilitates invasion into surrounding tissues. Alterations of both proteins have been reported in hepatocellular carcinomas (HCC). However, the interactions between E-cadherin and beta-catenin in HCC from different geographical groups have not been explored. The aim of the present study was to assess the role of E-cadherin and beta-catenin in Australian and South African patients with HCC. Methods: DNA was extracted from malignant and non-malignant liver tissue from 37 Australian and 24 South African patients, and from histologically normal liver from 20 transplant donors. Chromosomal instability at 16q22, promoter methylation at E-cadherin, beta-catenin mutations and E-cadherin and beta-catenin protein expression was assessed using loss of heterozygosity, methylation-specific polymerase chain reaction, denaturing high-performance liquid chromatography and immunohistochemistry, respectively. Results: Loss of heterozygosity at 16q22 was prevalent in South African HCC patients (50%vs 11%; P < 0.05, chi(2)). In contrast, E-cadherin promoter hypermethylation was common in Australian cases in both malignant (30%vs 13%; P = not significant, chi(2)) and non-malignant liver (57%vs 8%, respectively, P < 0.001, chi(2)). Methylation of non-malignant liver was more likely to be detected in patients over the age of 50 years (P < 0.001, chi(2)), the overall mean age for our cohort of patients. Only one beta-catenin mutation was identified. E-cadherin protein expression was reduced in one HCC, while abnormalities in protein expression were absent in beta-catenin. Conclusion: Contrary to previous observations in HCC from other countries, neither E-cadherin nor beta-catenin appears to play a role in hepatocarcinogenesis in Australian and South African patients with HCC. (C) 2004 Blackwell Publishing Asia Pty Ltd.

System for foreign exchange trading using genetic algorithms and reinforcement learning

Foreign exchange trading has emerged recently as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. A major issue for traders in the deregulated Foreign Exchange Market is when to sell and when to buy a particular currency in order to maximize profit. This paper presents novel trading strategies based on the machine learning methods of genetic algorithms and reinforcement learning.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Radiofrequency ablation of liver tumors - A systematic review

Objectives: To systematically review radiofrequency ablation (RFA) for treating liver tumors. Data Sources: Databases were searched in July 2003. Study Selection: Studies comparing RFA with other therapies for hepatocellular carcinoma (HCC) and colorectal liver metastases (CLM) plus selected case series for CLM. Data Extraction: One researcher used standardized data extraction tables developed before the study, and these were checked by a second researcher. Data Synthesis: For HCC, 1.3 comparative studies were included, 4 of which were randomized, controlled trials. For CLM, 13 studies were included, 2 of which were nonrandomized comparative studies and 11 that were case series. There did not seem to be any distinct differences in the complication rates between RFA and any of the other procedures for treatment of HCC. The local recurrence rate at 2 years showed a statistically significant benefit for RFA over percutaneous ethanol injection for treatment of HCC (6% vs 26%, 1 randomized, controlled trial). Local recurrence was reported to be more common after RFA than after laser-induced thermotherapy, and a higher recurrence rate and a shorter time to recurrence were dassociated with RFA compared with surgical resection (1 nonrandomized study each). For CLM, the postoperative complication rate ranged from 0% to 33% (3 case series). Survival after diagnosis was shorter in the CLM group treated with RFA than in the surgical resection group (1 nonrandomized study). The CLM local recurrence rate after RFA ranged from 4% to 55% (6 case series). Conclusions: Radiofrequency ablation may be more effective than other treatments in terms of less recurrence of HCC and may be as sale, although the evidence is scant. There was not enough evidence to determine the safety or efficacy of RFA for treatment of CLM.

Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster

Microsatellite instability and loss of heterozygosity at DNA mismatch repair gene loci occurs during hepatic carcinogenesis

DNA mismatch repair is an important mechanism involved in maintaining the fidelity of genomic DNA. Defective DNA mismatch repair is implicated in a variety of gastrointestinal and other turners; however, its role in hepatocellular carcinoma (HCC) has not been assessed. Formalin-fixed, paraffin-embedded archival pathology tissues from 46 primary liver tumors were studied by microdissection and microsatellite analysis of extracted DNA to assess the degree of microsatellite instability, a marker of defective mismatch repair, and to determine the extent and timing of allelic loss of two DNA mismatch repair genes, human Mut S homologue-2 (hMSH2) and human Mut L homologue-1 (hMLH1), and the tumor suppressor genes adenomatous polyposis coli gene (APC), p53, and DPC4. Microsatellite instability was detected in 16 of the tumors (34.8%). Loss of heterozygosity at microsatellites linked to the DNA mismatch repair genes, hMSH2 and/or hMLH1, was found in 9 cases (19.6%), usually in association with microsatellite instability. Importantly, the pattern of allelic loss was uniform in 8 of these 9 tumors, suggesting that clonal loss had occurred. Moreover, loss at these loci also occurred in nonmalignant tissue adjacent to 4 of these tumors, where it was associated with marked allelic heterogeneity. There was relatively infrequent loss of APC, p53, or DPC4 loci that appeared unrelated to loss of hMSH2 or hMLH1 gene loci. Loss of heterozygosity at hMSH2 and/or hMLH1 gene loci, and the associated microsatellite instability in premalignant hepatic tissues suggests a possible causal role in hepatic carcinogenesis in a subset of hepatomas.

Review of genetic and epigenetic alterations in hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is associated with multiple risk factors and is believed to arise from pre-neoplastic lesions, usually in the background of cirrhosis. However, the genetic and epigenetic events of hepatocarcinogenesis are relatively poorly understood. HCC display gross genomic alterations, including chromosomal instability (CIN), CpG island methylation, DNA rearrangements associated with hepatitis B virus (HBV) DNA integration, DNA hypomethylation and, to a lesser degree, microsatellite instability. Various studies have reported CIN at chromosomal regions, 1p, 4q, 5q, 6q, 8p, 10q, 11p, 16p, 16q, 17p and 22q. Frequent promoter hypermethylation and subsequent loss of protein expression has also been demonstrated in HCC at tumor suppressor gene (TSG), p16, p14, p15, SOCS1, RIZ1, E-cadherin and 14-3-3 sigma. An interesting observation emerging from these studies is the presence of a methylator phenotype in hepatocarcinogenesis, although it does not seem advantageous to have high levels of microsatellite instability. Methylation also appears to be an early event, suggesting that this may precede cirrhosis. However, these genes have been studied in isolation and global studies of methylator phenotype are required to assess the significance of epigenetic silencing in hepatocarcinogenesis. Based on previous data there are obvious fundamental differences in the mechanisms of hepatic carcinogenesis, with at least two distinct mechanisms of malignant transformation in the liver, related to CIN and CpG island methylation. The reason for these differences and the relative importance of these mechanisms are not clear but likely relate to the etiopathogenesis of HCC. Defining these broad mechanisms is a necessary prelude to determine the timing of events in malignant transformation of the liver and to investigate the role of known risk factors for HCC.

Asymmetric page split generalized index search trees for formal concept analysis

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.