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Background The clinical value of alpha-fetoprotein (AFP) in patients with AFP-negative (

Background: The effect of anti-viral treatment (AVT) initiated before surgery (pre-operative AVT) on HBV-related hepatocellular carcinoma (HCC) has been controversial. This study aimed to elucidate the prognostic significance of pre-operative AVT for HCC patients who received hepatectomy. Materials and Methods: A large-scale retrospective study was conducted based on a cohort consisting of 1937 HBV-related HCC patients who underwent R0 liver resection between January 2011 and December 2012. Propensity score matching (PSM) method was adopted to balance covariates and landmark survival analyses were performed to visualize effects in different phases after surgery. Results: After PSM, a total of matched 744 patients (372 in each group) were recruited. The patients in the pre-operative AVT group had lower HBV-DNA loading levels and better recurrence-free survival (RFS) than those in the non-AVT group. The 1, 3, 5-year RFS rates of two groups were 67.3%, 49.0%, and 43.1% vs. 66.7%, 41.1% and 18.5%, respectively (P5cm), esophageal and gastric varices, lymph node metastasis were independent risk factors of RFS, and larger tumor (>5cm) and ascites were independent risk factors of OS. Conclusions: Pre-operative AVT could significantly improve the RFS, and could not improve short-term OS (< 36 months) but could better long-term survival of the patients with HBV-HCC after surgery.

Qizhen Huang,1,* Kongying Lin,2,* Lei Wang,1,* Jianxing Zeng,2 Hongzhi Liu,2 Zongren Ding,2 Yongyi Zeng,2 Jingfeng Liu2 1Department of Radiation Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, People’s Republic of China; 2Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jingfeng LiuDepartment of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, People’s Republic of ChinaEmail drjingfeng@126.comPurpose: To evaluate the effect of postoperative adjuvant transarterial chemoembolization (PA-TACE) on the prognosis of hepatocellular carcinoma (HCC) with macroscopic bile duct tumor thrombus (BDTT).Patients and Methods: This study included 109 patients who underwent R0 resection for HCC with BDTT between January 2008 and December 2017: non-TACE (48) and PA-TACE (61). Propensity-score matching (PSM) was conducted in a 1:1 ratio. Recurrence and overall survival (OS) rates were analyzed using the Kaplan–Meier method. Independent risk factors were identified by univariate and multivariate Cox regression analyses. Subgroup analysis was performed by risk-factor stratification.Results: The recurrence rates in the non-TACE and PA-TACE groups were different at 6 months (50.9% vs 26.9%, P=0.03) before PSM and at 6 months (59.3% vs 26.5%, P=0.02) and 12 months (81.4% vs 37.5%, P=0.022) after PSM. OS rates of the non-TACE and PA-TACE groups were different at 6 months (74.0% vs 91.6%, P< 0.001) and 12 months (61.1% vs 77.6%, P=0.01) before PSM and at 6 months (73.0% vs 96.8%, P=0.01), 12 months (52.1% vs 89.6%, P=0.001), and 18 months (33.8% vs 64.4%, P=0.034) after PSM. PA-TACE was an independent prognostic factor for both recurrence and OS before and after PSM. Subgroup analysis showed that patients with no HBV infection, tumors > 5 cm, macrovascular invasion, alpha-fetoprotein (AFP) > 400 ng/mL, or gamma-glutamyl transferase (GGT) > 150 U/L benefited significantly from PA-TACE in terms of recurrence rates (all P< 0.05). Patients with no HBV infection, multiple tumors, tumors > 5 cm, macrovascular invasion, or AFP > 400 ng/mL benefited significantly from PA-TACE in terms of OS (all P< 0.05).Conclusion: PA-TACE could prolong the short-term prognosis of HCC with macroscopic BDTT and should be recommended for patients with no HBV infection, multiple tumors, tumors > 5 cm, poor differentiation, macrovascular invasion, AFP > 400 ng/mL, or GGT > 150 U/L.Keywords: hepatocellular carcinoma, bile duct tumor thrombus, transarterial chemoembolization, recurrence, survival

Purpose To develop and validate a nomogram for individualized prediction of the long-term prognosis of patients with non-B, non-C hepatocellular carcinoma (NBNC-HCC) who underwent hepatectomy. Materials and methods Five hundred ninety-four patients who met the criteria were included in the research and randomly categorized into the training or validation cohort. The nomogram was constructed on the basis of the independent risk variables that were acquired via multivariate Cox proportional hazard regression analysis. Several complementary methods included the Harrell c-index, time-dependent areas under the receiver operating characteristic curve (tdAUC), and calibration plot, and the Kaplan-Meier curve with Log rank test were used to test predictive performance of the model. The clinical utility of the model was tested by the decision cure analysis (DCA). Results Tumor diameter, tumor number, elevated serum gamma-glutamyl transpeptidase (GGT) level, microvascular invasion (MVI), and macrovascular invasion were independent risk factors of prognosis of NBNC-HCC. C-indexes of the nomogram were 0.702 (95% confidence interval [CI], 0.662-0.741) in the training cohort and 0.700 (95% CI, 0.643-0.758) in the validation cohort, and median tdAUC values of the nomogram were 0.743 (range, 0.736-0.775) in the training cohort and 0.751 (range, 0.686-0.793) in the validation cohort, which were both higher than those in the conventionally used Barcelona Clinic Liver Cancer staging system, American Joint Committee on Cancer, and eighth edition and the model of Zhang et al. The calibration plot depicted a good consistency between prediction of the model and observed outcome. The Kaplan-Meier curve analysis showed that the model was able to separate patients into three distinct risk subgroups. The DCA analysis also demonstrated that the nomogram was clinically useful. Conclusion We developed and validated a nomogram that was accurate and clinically useful in patients with NBNC-HCC who underwent hepatectomy.

Hepatocellular carcinoma (HCC) is a particularly heterogeneous tumor. It has a very poor prognosis. Pyroptosis has been demonstrated in recent years to be an inflammatory form of programmed cell death. However, the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of HCC is still unclear. The development of a specific PRGs prognostic model is important if we want to improve therapeutic effect of tumor. In this study, we identified 42 PRGs that were differentially expressed between HCC and peripheral normal tissues and exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). The result show that there were significant differences in clinical features (including T stage, grade, gender, stage) among different clusters. KM curve analysis show that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. By applying the univariate analysis and multivariate analysis method, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P

Background Inflammation is implicated in tumorigenesis and has been reported as an important prognostic factor in cancers. In this study, we aimed to develop and validate a novel inflammation score (IFS) system based on 12 inflammatory markers and explore its impact on intrahepatic cholangiocarcinoma (ICC) survival after hepatectomy. Methods Clinical data of 446 ICC patients undergoing surgical treatment were collected from the Primary Liver Cancer Big Data, and then served as a training cohort to establish the IFS. Furthermore, an internal validation cohort including 175 patients was used as internal validation cohort of the IFS. A survival tree analysis was used to divide ICC patients into three groups (low-, median-, and high- IFS-score groups) according to different IFS values. Kaplan-Meier (KM) curves were used to compare the overall survival (OS) and recurrence-free survival (RFS) rates among three different groups. Cox regression analyses were applied to explore the independent risk factors influencing OS and RFS. Results In the training cohort, 149 patients were in the low-IFS-score group, 187 in the median-IFS-score group, and 110 in the high-IFS-score group. KM curves showed that the high-IFS-score group had worse OS and RFS rates than those of the low- and median-IFS-score groups (P Conclusion Our results revealed the IFS was an independent risk factor for OS and RFS in patients with ICC after hepatectomy and could serve as an effective prognostic prediction system in daily clinical practice.

Purpose We aimed to develop and validate a radiomics model for differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI). Methods We retrospectively enrolled 149 HCC and 75 FNH patients treated between May 2015 and May 2019 at our center. Patients were randomly allocated to a training (n=156) and validation set (n=68). In total, 2260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forest, least absolute shrinkage, and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the models was compared. Results Eight radiomics features were chosen for the radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen for the clinical model. A combined model was built using the factors from the previous models. The classification accuracy of the combined model differentiated HCC from FNH in both the training and validation sets (0.956 and 0.941, respectively). The area under the receiver operating characteristic curve of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p=0.002) and validation (0.972 vs. 0.903, p=0.032) sets. Conclusions The combined model provided a non-invasive quantitative method for differentiating HCC from FNH in non-cirrhotic liver with high accuracy. Our model may assist clinicians in the clinical decision-making process.

Background: Mortality due to hepatocellular carcinoma (HCC), which is the most common liver cancer, is often overestimated because of deaths from other causes. This study was conducted to estimate the probability of cancer-specific mortality (CSM) of patients with HCC and establish a competing risk nomogram for predicting the CSM of among these patients HCC using a large population-based cohort. Methods: Patients diagnosed with HCC between 2004 and 2015 were identified from the Surveillance Epidemiology and End Results Program. CSM and overall survival (OS) were the endpoints of the study. A competing risk nomogram for predicting CSM was built using the Fine and Gray regression model, and the nomogram for predicting OS was constructed with the Cox proportional hazard regression model, and 10-fold cross-validation was performed for the entire set. Results: A total of 34,957 patients were included in the study and randomly divided into a training set and validation set at a ratio of 7:3. Multivariate analysis identified age, race, surgical therapy, chemotherapy, radiotherapy, tumour diameter, and tumour staging as the independent predictive factors of CSM. In addition to these factors, sex and marital status were also identified as independent predictive factors of OS. Using these factors, corresponding nomograms were constructed for CSM and OS. In the validation set, the 5 year concordance-indices of the two nomogram models were estimated as 0.746 and 0.74. Calibration curves revealed good consistency between model predictions and observed outcomes. Furthermore, based on the results of cumulative incidence function analysis and Kaplan-Meier analysis, patients were categorised into four distinct risk subgroups, supporting the predictive performance of the models. Conclusions: In this population-based analysis, we developed and validated nomograms for individualized prediction of CSM and OS in patients with HCC.

Objectives This study was conducted to estimate the probability of cancer-specific survival (CSS) of HCC and establish a competing risk nomogram for predicting the CSS of HCC using a large population-based cohort. Methods Patients diagnosed with HCC between 2004 and 2015 were identified from the Surveillance Epidemiology and End Results Program. The CSS and overall survival (OS) were the endpoints of the study. A competing risk nomogram for predicting CSS was built with Fine and Gray’s competing risk model, and the nomogram for predicting OS was constructed with Cox proportional hazard regression models. The predictive performance of the model was tested in terms of discrimination and calibration. Results A total of 34,957 patients were included in the study and randomly divided into a training set and validation set at a ratio of 9:1. Multivariate analysis identified age, race, sex, surgical therapy, chemotherapy, radiotherapy, tumour diameter, and tumour staging as independent predictive factors of CSS. Additionally, marital status was identified as an independent predictive factor of OS. Using these factors, corresponding nomograms were constructed for CSS and OS. In the validation set, the concordance-index of the two nomogram models reached 0.810 and 0.750, respectively. Calibration curves revealed good consistency between the prediction of models and observed outcome. Furthermore, cumulative incidence function analysis and Kaplan-Meier analysis divided patients into four distinct risk subgroups, supporting the predictive performance of the models. Conclusions In this population-based analysis, we developed and validated nomograms for individualized prediction of CSS and OS in patients with HCC.