ABSTRACT
BACKGROUND: In the Barcelona Clinic Liver Cancer (BCLC) system, only sorafenib is suggested for HCC patients having performance status (PS) 1 or 2 even if they have treatable lesions. In the current study, we aimed to explore the outcome of using aggressive treatment for HCC patients with PS 1 and 2. MATERIALS AND METHODS: Five hundred and twenty four patients with HCC were enrolled in this study and divided into 2 groups: 404 PS 1 and 120 PS 2. Of the included 524 patients, 136 recceived non-aggressive supportive treatment and sorafenib, while 388 patients were offered aggressive treatment in the form of surgical resection, transplantation, percutaneous ablation, trans-arterial chemoembolization and/or chemoperfusion. All the patients were followed up for a period of 2 years to determine their survival. RESULTS: Most HCC patients were CHILD A and B grades (89.4% versus 85.0%, for PS1 and PS2, respectively). Patients with PS1 were significantly younger. Out of the enrolled 524 patients, 388 were offered aggressive treatment, 253 (65.2%) having their lesions fully ablated, 94 (24.2%) undergoing partial ablation and 41 patients with no ablation (10.6%). The median survival of the patients with PS 1 who were offered aggressive treatment was 20 months versus 9 months only for those who were offered supportive treatment and sorafenib (<0.001). Regarding HCC patients with PS 2, the median survivals were similarly 19.7 months versus 8.7 months only (<0.001). CONCLUSIONS: Aggressive treatment of HCC patients with PS 1 and 2 significantly improves their survival. Revising the BCLC guidelines regarding such patients is recommended.
Subject(s)
Antineoplastic Agents/therapeutic use , Carcinoma, Hepatocellular/mortality , Chemoembolization, Therapeutic/mortality , Liver Neoplasms/mortality , Niacinamide/analogs & derivatives , Phenylurea Compounds/therapeutic use , Carcinoma, Hepatocellular/pathology , Carcinoma, Hepatocellular/therapy , Combined Modality Therapy , Female , Follow-Up Studies , Humans , Liver Neoplasms/pathology , Liver Neoplasms/therapy , Male , Middle Aged , Neoplasm Staging , Niacinamide/therapeutic use , Prognosis , Retrospective Studies , Sorafenib , Survival RateABSTRACT
BACKGROUND: The reversion-inducing-cysteine-rich protein with kazal motifs (RECK) gene is a transformation suppressor gene that can negatively regulate matrix metalloproteinases (MMPs) and inhibit tumor invasion, angiogenesis, and metastasis. So, the aim of this study was to analyze the effect of RECK gene rs 11788747 single nucleotide polymorphism (SNP) on hepatocellular carcinoma (HCC) susceptibility and its relation to various clinical and laboratory data of the patients. METHODS: This is a case-control study including 200 HCC patients and 200 healthy controls. RECK rs 11788747 genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). RESULTS: RECK rs 11788747 A/G and G/G genotypes frequencies were significantly higher in HCC patients compared to the healthy controls. The HCC patients possessing at least one polymorphic G allele were significantly at a higher risk of developing lymph nodes involvement and distant metastasis. CONCLUSION: This study revealed the role of RECK rs 11788747 SNP in HCC in Egyptian patients, which consequently might be used as a prognostic tool and could be added to its therapeutic strategies.
Subject(s)
Carcinoma, Hepatocellular/genetics , GPI-Linked Proteins/genetics , Genetic Predisposition to Disease , Liver Neoplasms/genetics , Polymorphism, Single Nucleotide/genetics , Case-Control Studies , Confidence Intervals , Female , Humans , Male , Middle Aged , Odds RatioABSTRACT
BACKGROUND: Hepatocellular carcinoma (HCC) is the second most common malignancy in Egypt. Data mining is a method of predictive analysis which can explore tremendous volumes of information to discover hidden patterns and relationships. Our aim here was to develop a non-invasive algorithm for prediction of HCC. Such an algorithm should be economical, reliable, easy to apply and acceptable by domain experts. METHODS: This cross-sectional study enrolled 315 patients with hepatitis C virus (HCV) related chronic liver disease (CLD); 135 HCC, 116 cirrhotic patients without HCC and 64 patients with chronic hepatitis C. Using data mining analysis, we constructed a decision tree learning algorithm to predict HCC. RESULTS: The decision tree algorithm was able to predict HCC with recall (sensitivity) of 83.5% and precession (specificity) of 83.3% using only routine data. The correctly classified instances were 259 (82.2%), and the incorrectly classified instances were 56 (17.8%). Out of 29 attributes, serum alpha fetoprotein (AFP), with an optimal cutoff value of ≥50.3 ng/ml was selected as the best predictor of HCC. To a lesser extent, male sex, presence of cirrhosis, AST>64U/L, and ascites were variables associated with HCC. CONCLUSION: Data mining analysis allows discovery of hidden patterns and enables the development of models to predict HCC, utilizing routine data as an alternative to CT and liver biopsy. This study has highlighted a new cutoff for AFP (≥50.3 ng/ml). Presence of a score of >2 risk variables (out of 5) can successfully predict HCC with a sensitivity of 96% and specificity of 82%.