A Novel Score Predicts HBV-Related Hepatocellular Carcinoma Recurrence After Hepatectomy: a Retrospective Multicenter Study

Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery

BACKGROUND AND AIMS

HCC recurrence frequently occurs after curative surgery. Histological microvascular invasion (MVI) predicts recurrence but cannot provide preoperative prognostication, whereas clinical prediction scores have variable performances.

APPROACH AND RESULTS

Recurr-NET, a multimodal multiphasic residual-network random survival forest deep-learning model incorporating preoperative CT and clinical parameters, was developed to predict HCC recurrence. Preoperative triphasic CT scans were retrieved from patients with resected histology-confirmed HCC from 4 centers in Hong Kong (internal cohort). The internal cohort was randomly divided in an 8:2 ratio into training and internal validation. External testing was performed in an independent cohort from Taiwan.Among 1231 patients (age 62.4y, 83.1% male, 86.8% viral hepatitis, and median follow-up 65.1mo), cumulative HCC recurrence rates at years 2 and 5 were 41.8% and 56.4%, respectively. Recurr-NET achieved excellent accuracy in predicting recurrence from years 1 to 5 (internal cohort AUROC 0.770-0.857; external AUROC 0.758-0.798), significantly outperforming MVI (internal AUROC 0.518-0.590; external AUROC 0.557-0.615) and multiple clinical risk scores (ERASL-PRE, ERASL-POST, DFT, and Shim scores) (internal AUROC 0.523-0.587, external AUROC: 0.524-0.620), respectively (all p < 0.001). Recurr-NET was superior to MVI in stratifying recurrence risks at year 2 (internal: 72.5% vs. 50.0% in MVI; external: 65.3% vs. 46.6% in MVI) and year 5 (internal: 86.4% vs. 62.5% in MVI; external: 81.4% vs. 63.8% in MVI) (all p < 0.001). Recurr-NET was also superior to MVI in stratifying liver-related and all-cause mortality (all p < 0.001). The performance of Recurr-NET remained robust in subgroup analyses.

CONCLUSIONS

Recurr-NET accurately predicted HCC recurrence, outperforming MVI and clinical prediction scores, highlighting its potential in preoperative prognostication.

Clinical significance of half-hepatic blood flow occlusion technology in patients with hepatocellular carcinoma with cirrhosis

BACKGROUND

Most patients with primary hepatocellular carcinoma (HCC) have a history of chronic hepatitis B and usually present with varying degrees of cirrhosis. Owing to the special nature of liver anatomy, the blood vessel wall in the liver parenchyma is thin and prone to bleeding. Heavy bleeding and blood transfusion during hepatectomy are independent risk factors for liver cancer recurrence and death. Various clinical methods have been used to reduce intraoperative bleeding, and the Pringle method is most widely used to prevent blood flow to the liver.

AIM

To investigate the effect of half-hepatic blood flow occlusion after patients with HCC and cirrhosis undergo hepatectomy.

METHODS

This retrospective study included 88 patients with HCC and liver cirrhosis who underwent hepatectomy in our hospital from January 2017 to September 2020. Patients were divided into two groups based on the following treatment methods: the research group (n = 44), treated with half-hepatic blood flow occlusion technology and the control group (n = 44), treated with total hepatic occlusion. Differences in operation procedure, blood transfusion, liver function, tumor markers, serum inflammatory response, and incidence of surgical complications were compared between the groups.

RESULTS

The operation lasted longer in the research group than in the control group (273.0 ± 24.8 min vs 256.3 ± 28.5 min, P < 0.05), and the postoperative anal exhaust time was shorter in the research group than in the control group (50.0 ± 9.7 min vs 55.1 ± 10.4 min, P < 0.05). There was no statistically significant difference in incision length, surgical bleeding, portal block time, drainage tube indwelling time, and hospital stay between the research and control groups (P > 0.05). Before surgery, there were no significant differences in serum alanine transaminase (ALT), aspartate aminotransferase (AST), total bilirubin, and prealbumin levels between the research and control groups (P > 0.05). Conversely, 24 and 72 h after the operation the respective serum ALT (378.61 ± 77.49 U/L and 246.13 ± 54.06 U/L) and AST (355.30 ± 69.50 U/L and 223.47 ± 48.64 U/L) levels in the research group were significantly lower (P < 0.05) than those in the control group (ALT, 430.58 ± 83.67 U/L and 281.35 ± 59.61 U/L; AST, 416.49 ± 73.03 U/L and 248.62 ± 50.10 U/L). The operation complication rate did not significantly differ between the research group (15.91%) and the control group (22.73%; P > 0.05).

CONCLUSION

Half-hepatic blood flow occlusion technology is more beneficial than total hepatic occlusion in reducing liver function injury in hepatectomy for patients with HCC and cirrhosis.

Anatomical sites (Takasaki's segmentation) predicts the recurrence-free survival of hepatocellular carcinoma

Background

Until now, several classification staging system and treatment algorithm for hepatocelluar carcinoma (HCC) has been presented. However, anatomical location is not taken into account in these staging systems. The aim of this study is to investigate whether anatomical sites could predict the postoperative recurrence of HCC patients.

Methods

294 HCC patients were enrolled in this retrospective study. A novel score classification based on anatomical sites was established by a Cox regression model and validated in the internal validation cohort.

Results

HCC patients were stratified according to the novel score classification into three groups (score 0, score 1–3 and score 4–6). The predictive accuracy of the novel recurrence score for HCC patients as determined by the area under the receiver operating characteristic curves (AUCs) at 1, 3, and 5 years (AUCs 0.703, 0.706, and 0.605) was greater than that of the other representative classification systems. These findings were supported by the internal validation cohort. For patients with Barcelona Clinic Liver Cancer (BCLC) 0 and A stage, our data demonstrated that there was no significant difference in recurrence-free survival (RFS) between patients with score 0 and liver transplantation recipients. Additionally, we introduced this novel classification system to guide anatomical liver resection for centrally located liver tumors.

Conclusion

The novel score classification may provide a reliable and objective model to predict the RFS of HCC after hepatic resection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12893-021-01275-3.

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.

Nomograms based on clinicopathological factors and inflammatory indicators for prediction of early and late recurrence of hepatocellular carcinoma after surgical resection for patients with chronic hepatitis B

BACKGROUND

Few studies have focused on the prognostic values of inflammation-related factors for different phases of recurrence in hepatocellular carcinoma (HCC). We aimed to identify the different risk factors for overall, early, and late recurrence, and to establish nomograms based on inflammation-related parameters for predicting the risks of recurrence in a group of HCC patients undergoing hepatectomy.

METHODS

We retrospectively enrolled 383 HCC patients with chronic hepatitis B (CHB) who underwent hepatectomy. Univariate and multivariate Cox analyses were conducted to identify independent risk factors for recurrence. Nomograms for overall, early, and late recurrence-free survival (RFS) were established. The discrimination and calibration abilities of the nomograms were evaluated by concordance indexes (C-index), calibration plots, and Kaplan-Meier curves. Finally, receiver operating characteristic (ROC) curves were used to compare the derived nomograms with other existing models.

RESULTS

Fibrinogen, lymphocyte-to-monocyte ratio, and S-index inflammation-related factors were independently related to overall and early RFS, but only the S-index correlated with late recurrence. Nomograms with tumor number, diameter, and pathological differentiation for overall and early RFS were established, while nomogram for late recurrence was constructed with tumor number and Child-Pugh grade. The C-indexes for overall, early, and late RFS were 0.679, 0.677, and 0.728, respectively. The calibration plots fit well. The nomograms showed superior discrimination capacities and better performance prediction with larger areas under the curve for recurrence.

CONCLUSIONS

The developed nomograms that integrated inflammation-related factors showed high predictive accuracy for overall, early, and late recurrence in HCC patients with CHB after hepatectomy.

Relationship between platelet-based models and the prognosis of patients with malignant hepatic tumors

Platelets (PLTs) are involved in tumor growth, metabolism and vascular activation. PLT-based models have been reported to have significant value on the recurrence of malignant hepatic tumors. The present study aimed to investigate the effect of PLT count and 18 PLT-based models on the prognosis of patients with malignant hepatic tumors. The clinical data from 189 patients with malignant hepatic tumors were retrospectively analyzed and used to calculate the scores of the 18 PLT-based models. Receiver operating characteristic curve was used to determine the suitable cut-off values of mortality and recurrence in patients with malignant hepatic tumors. The overall survival and cumulative recurrence rates of patients were calculated using Kaplan-Meier survival curves and the difference was analyzed using log-rank test. Multivariate analysis was performed to determine the independent risk factors of recurrence-free survival and overall survival. In the present study, 11 models were considered as predictors of mortality (P<0.05) and six models were considered as predictors of recurrence (P<0.05). The results from multivariate analysis demonstrated that vascular cancer embolus, uric acid >231 µmol/l, hemoglobin >144 g/l and the Lok index model >0.695 were considered as independent risk factors of mortality (P<0.05). Furthermore, vascular cancer embolus, PLT to lymphocyte ratio (PLR) >175 and fibrosis-4 (FIB-4) >4.82 were independent factors of recurrence (P<0.05). In addition, the results from this study indicated that the Lok-index could be considered as a predictor of the overall survival rate. In conclusion, the FIB-4 and PLR model may be valuable for predicting the recurrence-free rate of patients with malignant hepatic tumors.

Platelets and Hepatocellular Cancer: Bridging the Bench to the Clinics

Growing interest is recently being focused on the role played by the platelets in favoring hepatocellular cancer (HCC) growth and dissemination. The present review reports in detail both the experimental and clinical evidence published on this topic. Several growth factors and angiogenic molecules specifically secreted by platelets are directly connected with tumor progression and neo-angiogenesis. Among them, we can list the platelet-derived growth factor, the vascular endothelial growth factor, the endothelial growth factor, and serotonin. Platelets are also involved in tumor spread, favoring endothelium permeabilization and tumor cells' extravasation and survival in the bloodstream. From the bench to the clinics, all of these aspects were also investigated in clinical series, showing an evident correlation between platelet count and size of HCC, tumor biological behavior, metastatic spread, and overall survival rates. Moreover, a better understanding of the mechanisms involved in the platelet-tumor axis represents a paramount aspect for optimizing both current tumor treatment and development of new therapeutic strategies against HCC.

Integrated tumor stromal features of hepatocellular carcinoma reveals two distinct subtypes with prognostic/predictive significance

Current clinical classification of hepatocellular carcinoma (HCC) is unable to predict prognosis efficiently. Our aim is to classify HCC into clinically/biologically relevant subtypes according to stromal factors. We detected seven types of stromal features in tumors from 161 HCC patients by immunohistochemical staining and Hematoxylin-eosin staining. Five stromal features were selected out of seven types of stromal features to construct stromal type based on LASSO COX regression model. Then, integrating multiple clinicopathologic characteristics and stromal type, we built two nomograms for overall survival (OS) and disease-free survival (DFS). Further validation of the stromal type and nomograms were performed in the testing cohort (n = 160) and validation cohort (n = 120). Using the LASSO model, we classified HCC patients into stromal type A subgroup (CD34^lowTIL-stromal-ratio^highStromal-tumor-ratio^lowα-SMA^weakStroma^mature) and stromal type B subgroup (CD34^highTIL-stromal-ratio^lowStromal-tumor-ratio^highα-SMA^strongStroma^immature). The stromal type was an independent prognostic factor for OS and DFS in the training, testing and validation cohorts. Two nomograms (for OS and DFS) that integrated the stromal type and clinicopathologic risk factors also showed good predictive accuracy and discriminatory power. In addition, immune cell recruitment in the tumor microenvironment (TME) was conditioned by the tumor stromal type. In conclusion, the newly developed tumor stromal type was an effective predictor of OS and DFS. Furthermore, the stromal type is associated with the immune phenotype in the TME.