Radiologic response as a prognostic factor in advanced hepatocellular carcinoma with macroscopic vascular invasion after transarterial chemoembolization and radiotherapy

Introduction: We evaluated the radiologic response rate of combined transarterial chemoembolization (TACE) plus radiotherapy (RT) in treatment-naïve patients with liver-confined hepatocellular carcinoma (HCC) with macroscopic vascular invasion (MVI) and analyzed its clinical importance in overall survival (OS) outcomes. Methods: Patients who were treated with TACE plus RT as a first-line treatment for HCC with MVI between January 2010 and December 2015 were retrospectively reviewed. Radiologic response was assessed according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST) at 2- and 4-months after completion of RT. Landmark analysis at 2- and 4-months and time-dependent Cox regression analysis using response as a time-dependent covariate were performed for univariable and multivariable analyses. Results: The 2-month landmark analysis included 427 patients, and the 4-month landmark analysis included 355 patients after excluding patients without imaging studies for response evaluation at 4 months. Radiologic responses were observed in 210 (49.2%) patients at 2 months and 181 (51.8%) at 4 months. In multivariable analyses, radiologic response was identified as an independent prognosticator for OS at 2 months (median OS: responders, 23.1 months vs. non-responders, 8.0 months; hazard ratio [HR], 3.194; P < 0.001) and 4 months (median OS: responders, 26.5 months vs. non-responders, 9.3 months; HR, 4.534; P < 0.001). Conclusion: Radiologic response assessed by mRECIST was a significant prognostic factor for OS in patients with advanced-stage HCC showing MVI treated with combined TACE plus RT.

Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion

Background The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion. Methods Nine hundred and three patients who underwent curative liver resection for HCC participated in this study. They were randomly divided into derivation (n = 679) and validation (n = 224) cohorts. The ANN model was developed in the derivation cohort and subsequently verified in the validation cohort. Results PHER morbidity in the derivation and validation cohorts was 34.8 and 39.2%, respectively. A multivariable analysis revealed that hepatitis B virus deoxyribonucleic acid load, γ-glutamyl transpeptidase level, α-fetoprotein level, tumor size, tumor differentiation, microvascular invasion, satellite nodules, and blood loss were significantly associated with PHER. These factors were incorporated into an ANN model, which displayed greater discriminatory abilities than a Cox’s proportional hazards model, preexisting recurrence models, and commonly used staging systems for predicting PHER. The recurrence-free survival curves were significantly different between patients that had been stratified into two risk groups. Conclusion When compared to other models and staging systems, the ANN model has a significant advantage in predicting PHER for HCC patients without macroscopic vascular invasion.

Prognostic factors of survival and comparison of surgically resected combined hepatocellular-cholangiocellular carcinoma, hepatocellular carcinoma, and intrahepatic cholangiocarcinoma

Background: Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) are the most common primary malignancies of the liver. The combined form of these two tumors (i.e., cHC-CC) is a considerably rare type of liver cancer displaying both malignant components. Methods: In this research, 53 patients were evaluated retrospectively, who had undergone an operation for primary liver tumors in a single tertiary center, in terms of demographics, operation, tumor features, histopathological analysis, and their relationship with survival. Results: The study groups consisted of 20 ( 37.7%) and 33 (62.3%) females and males, respectively, with a mean age of 62.3 years. It was revealed that the survival rate was significantly higher in HCC, compared to other groups (P<0.05). Moreover, alpha-fetoprotein (AFP) was significantly higher in the HCC group than in the ICC group, and carbohydrate antigen 19-9 levels and the presence of jaundice and perineural invasion were significantly higher in the ICC group, compared to HCC patients. In the HCC group, macroscopic vascular invasion, perineural invasion, and T staging were statistically significant. It was also found that in the ICC group, the macroscopic vascular invasion was statistically significant, and in the cHC-ICC group, the increased levels of AFP showed a statistically significant effect on survival (P<0.05). Conclusions: To the best of our knowledge, the current research was one of the very few studies performed focusing on each group of liver tumors in a single study. Based on the findings of this research, there were statistically significant results in all three groups and their comparison with each other.

Artificial Neural Network Model to Predict Post-Hepatectomy Early Recurrence of Hepatocellular Carcinoma without Macroscopic Vascular Invasion

Background: The accurate prediction of post-hepatectomy early recurrence (PHER) for hepatocellular carcinoma (HCC) is of great significance in determining postoperative adjuvant treatment and monitoring. This research aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion.Methods: 903 patients who underwent curative liver resection for HCC were collected. They were randomly divided into a derivation cohort (n = 679) and a validation cohort (n = 224). The ANN model was then developed in the derivation cohort and verified in the validation cohort.Results: The morbidity of PHER in the derivation and validation cohorts was 34.8% and 39.2%, respectively. Multivariate analysis revealed that hepatitis B virus DNA load, γ-glutamyl transpeptadase, α-fetoprotein, tumor diameter, tumor differentiation, microvascular invasion, satellite nodules and blood loss were significantly associated with PHER. Incorporating these factors, the ANN model had greater discriminatory abilities than conventional Cox model, existing recurrence models and commonly used staging systems for predicting PHER. Stratification into two risk groups indicated a statistically significant discrepancy in recurrence-free survival curves. Conclusion: The ANN model has a significant advantage in predicting PHER for HCC patients without macroscopic vascular invasion when compared to other models and staging systems.