Schistosoma mansoni infection and the occurrence, characteristics, and survival of patients with hepatocellular carcinoma: an observational study over a decade.

Schistosoma mansoni infection (SMI) is suspected to be directly and indirectly involved in hepato-carcinogenesis. This study evaluated the association of a previous SMI with hepatocellular carcinoma (HCC) development, patients, tumor characteristics, treatment outcomes, and survival. This observational study included patients with HCC with and without previous SMI who presented to the multidisciplinary HCC clinic, Kasr-Alainy hospital (November 2009 to December 2019). It also included 313 patients with liver cirrhosis without HCC. Clinical and laboratory features of the patients (complete blood count, liver/renal functions , alpha-fetoprotein, and hepatitis B/C status), tumor characteristics (Triphasic CT and/or dynamic MRI), liver stiffness (transient elastography), HCC treatment outcome, and overall survival were studied. This study included 1446 patients with HCC; 688(47.6%) composed group-1, defined by patients having a history of SMI, and 758(52.4%) were in group-2 and without history of SMI. Male sex, smoking, diabetes mellitus, splenomegaly, deteriorated performance status, synthetic liver functions, and platelet count were significantly higher in group-1. The groups did not differ with regard to liver stiffness, tumor characteristics, or the occurrence of post-HCC treatment hepatic decompensation or recurrence. HCC treatment response was better in group-2. Group-1 showed lower sustained virological response to hepatitis C direct-acting antivirals (DAAs) compared with group-2 (60% versus 84.3%, respectively, P = 0.027). Prior SMI was associated with HCC (adjusted odds ratio = 1.589, 95% confidence interval = 1.187-2.127), and it was concluded that it increases the risk of HCC. In addition, it significantly affects the performance status, laboratory characteristics, response to DAAs, and overall survival.

Hepatocellular Carcinoma Multidisciplinary Clinic-Cairo University (HMC-CU) score: A new simple score for diagnosis of HCC.

BACKGROUND AND STUDY AIMS: The risk of hepatocarcinogenesis depends on background liver factors, of which fibrosis is a major determinant. Serum markers and scores are of increasing importance in non-invasive diagnosis of hepatic fibrosis. Our aim was to predict the occurrence of hepatocellular carcinoma (HCC) using a non-invasive fibrosis score calculated using routine patient data. PATIENTS AND MTHODS: Our retrospective study included 1,291 hepatitis C related-HCC Egyptian patients (Group 1) recruited from the multidisciplinary HCC clinic, Faculty of Medicine, Cairo University in the period between February 2009 and June 2016 and 1072 chronic hepatitis C-naïve patients (Group 2) with advanced fibrosis (≥F3) and cirrhosis (F4). King score, Fibro Q score, Aspartate aminotransferase-to-platelet ratio index (APRI), AST to ALT ratio (AAR), LOK score, Göteborg University Cirrhosis Index (GUCI), Fibro-α and Biotechnology Research Center (BRC) scores were calculated for all patients. Regression analysis and receiver operating characteristics (ROC) were used to calculate the sensitivity, specificity and predictive values for significant scores with the best cut-off for predicting HCC. A regression equation was used to calculate predicted probabilities of HCC using the following variables; age, gender, haemoglobin, international normalised ratio (INR), albumin and alpha fetoprotein. The appropriate score cut-off points yielding optimal sensitivity and specificity were determined by ROC curve analysis. RESULTS: There was a highly significant difference between the two groups for all calculated scores (P = 0.0001). Our new score, the Hepatocellular Carcinoma Multidisciplinary Clinic-Cairo University (HMC-CU) score (Logit probability of HCC =  - 2.524 + 0.152*age - 0.121*Hb - 0.696*INR - 1.059*Alb + 0.022*AFP + 0.976*Sex. Male = 1, Female = 0), with a cut-off of 0.559 was superior to other scores for predicting HCC, having a sensitivity of 90% and specificity of 80.6%. CONCLUSION: The HMC-CU score is a promising, easily calculated, accurate, cost-effective score for HCC prediction in chronic HCV patients with advanced liver fibrosis.

Data Mining and Machine Learning Algorithms Using IL28B Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C.

IL28B single nucleotide polymorphism (rs12979860) is an etiology-independent predictor of hepatitis C virus (HCV)-related hepatic fibrosis. Data mining is a method of predictive analysis which can explore tremendous volumes of information from health records to discover hidden patterns and relationships. The current study aims to evaluate and compare the prediction accuracy of scoring system like aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) index versus data mining for the prediction of HCV-related advanced fibrosis. This retrospective study included 427 patients with chronic hepatitis C. We used data mining analysis to construct a decision tree by reduced error (REP) technique, followed by Auto-WEKA tool to select the best classifier out of 39 algorithms to predict advanced fibrosis. APRI and FIB-4 had sensitivity-specificity parameters of 0.523-0.831 and 0.415-0.917, respectively. REPTree algorithm was able to predict advanced fibrosis with sensitivity of 0.749, specificity of 0.729, and receiver operating characteristic (ROC) area of 0.796. Out of the 16 attributes, IL28B genotype was selected by the REPTree as the best predictor for advanced fibrosis. Using Auto-WEKA, the multilayer perceptron (MLP) neural model was selected as the best predictive algorithm with sensitivity of 0.825, specificity of 0.811, and ROC area of 0.880. Thus, MLP is better than APRI, FIB-4, and REPTree for predicting advanced fibrosis for patients with chronic hepatitis C.