Hepatitis C virus treatment by direct-acting antivirals in successfully treated hepatocellular carcinoma and possible mutual impact.

BACKGROUND AND AIMS: Treatment of hepatitis C virus (HCV) after successfully treated hepatocellular carcinoma (HCC) becomes possible with the introduction of direct-acting antivirals because of their favorable efficacy, safety, and short period of treatment. Few data are available on the results of treatment using different direct-acting antiviral regimens in successfully treated HCC and a lot of debate about its role in tumor recurrence. METHODS: Sixty-two HCV-related HCC patients were enrolled in the study after successfully treated HCC; the studied population included either Child-Pugh 'A' or 'B7'. The patients were subcategorized to receive one of the following regimens: group 1: sofosbuvir (SOF)+ribavirin (RBV) for 24 weeks, group 2: SOF+simeprevir for 12 weeks, group 3: SOF+daclatasvir for 24 weeks, and group 4: SOF+daclatasvir+RBV for 12 weeks. The overall median follow-up period is 12 months after treatment initiation. RESULTS: All treatment regimens were tolerable for all patients, with no reported major adverse events during treatment. The overall sustained virologic response rate was 64.5%, with the highest result in group 4 and the lowest result in group 1; 87.5 and 26.7%, respectively. HCC recurrence was observed in 42% of patients; 80.7% of these patients developed recurrence within 6 months of treatment initiation. CONCLUSION: Treatment of HCV in successfully treated HCC is feasible, with the best results achieved using multiple direct-acting antivirals and RBV; a high rate of HCC recurrence was observed, especially within the first 6 months of treatment initiation (ClinicalTrials.gov no: NCT02771405).

Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients.

Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0-F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy.

Laparoscopic urological surgery: a review.

In this manuscript some of the more common laparoscopic procedures and their indications will be reviewed.