Therapy of Intermediate-Stage Hepatocellular Carcinoma: Current Evidence and Clinical Practice

A Post-International Gastrointestinal Cancers' Conference (IGICC) Position Statements

Hepatocellular carcinoma (HCC), the most prevalent liver tumor, is usually linked with chronic liver diseases, particularly cirrhosis. As per the 2020 statistics, this cancer ranks 6th in the list of most common cancers worldwide and is the third primary source of cancer-related deaths. Asia holds the record for the highest occurrence of HCC. HCC is found three times more frequently in men than in women. The primary risk factors for HCC include chronic viral infections, excessive alcohol intake, steatotic liver disease conditions, as well as genetic and family predispositions. Roughly 40-50% of patients are identified in the late stages of the disease. Recently, there have been significant advancements in the treatment methods for advanced HCC. The selection of treatment for HCC hinges on the stage of the disease and the patient's medical status. Factors such as pre-existing liver conditions, etiology, portal hypertension, and portal vein thrombosis need critical evaluation, monitoring, and appropriate treatment. Depending on the patient and the characteristics of the disease, liver resection, ablation, or transplantation may be deemed potentially curative. For inoperable lesions, arterially directed therapy might be an option, or systemic treatment might be deemed more suitable. In specific cases, the recommendation might extend to external beam radiation therapy. For all individuals, a comprehensive, multidisciplinary approach should be adopted when considering HCC treatment options. The main treatment strategies for advanced HCC patients are typically combination treatments such as immunotherapy and anti-VEGFR inhibitor, or a combination of immunotherapy and immunotherapy where appropriate, as a first-line treatment. Furthermore, some TKIs and immune checkpoint inhibitors may be used as single agents in cases where patients are not fit for the combination therapies. As second-line treatments, some treatment agents have been reported and can be considered.

Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

We aimed to develop and validate a pyradiomics model for preoperative prediction of initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). To this end, computed tomography (CT) images were acquired from multi-centers. Numerous pyradiomics features were extracted and machine learning approach was used to build a model for predicting initial response of TACE treatment. The predictive accuracy, overall survival (OS), and progression-free survival (PFS) were analyzed. Gene Set Enrichment Analysis (GSEA) was further used to explore signaling pathways in The Cancer Genome Atlas (TCGA)-HCC cohort. Overall, 24 of the 1,209 pyradiomic features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. The pyradiomics signature showed high predictive accuracy across the discovery set (AUC: 0.917, 95% confidence interval [CI]: 86.93-96.39), validation set 1 (AUC: 0.902, 95% CI: 84.81-95.59), and validation set 2 (AUC: 0.911; 95% CI: 83.26-98.98). Based on the classification of pyradiomics model, we found that a group with high values base on pyramidomics score showed good PFS and OS (both P<0.001) and was negatively correlated with glycolysis pathway. The proposed pyradiomics signature could accurately predict initial treatment response and prognosis, which may be helpful for clinicians to better screen patients who are likely to benefit from TACE.

Role of interventional oncology in hepatocellular carcinoma: Future best practice beyond current guidelines

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally. Liver transplant remains the goal of curative treatment, but limited supply of organs decreases accessibility and prolongs waiting time to transplantation. Therefore, interventional oncology therapies have been used to treat the majority of HCC patients, including those awaiting transplant. The Barcelona Clinic Liver Cancer (BCLC) classification is the most widely used staging system in management of HCC that helps allocate treatments. Since its inception in 1999, it was updated for the fifth time in November 2021 and for the first time shaped by expert opinions outside the core BCLC group. The most recent version includes additional options for early-stage disease, substratifies intermediate disease into three groups, and lists alternates to Sorafenib that can double the expected survival of advanced-stage disease. The group also proposed a new BCLC staging schema for disease progression, and endorsed treatment stage migration (TSM) directly into the main staging and treatment algorithm. This article reviews the recent developments underlying the current BCLC guidelines and highlights ongoing research, particularly involving radioembolization, that will shape future best practice.

Encouraging specific biomarkers-based therapeutic strategies for hepatocellular carcinoma

The prevention, early discovery and effective treatment of patients with hepatocellular carcinoma (HCC) remain a global medical challenge. At present, HCC is still mainly treated by surgery, supplemented by vascular embolization, radio frequency, radiotherapy, chemotherapy and biotherapy. The application of multikinase inhibitor sorafenib, chimeric antigen receptor T cells, or PD-1/PD-L1 inhibitors can prolong the median survival of HCC patients. However, the treatment efficacy is still unsatisfactory due to HCC metastasis and postoperative recurrence. During the process of hepatocyte malignant transformation, HCC tissues can express and secrete many types of specific biomarkers, or oncogenic antigen molecules into blood, for example, alpha-fetoprotein, glypican-3, Wnt3a (one of the key signaling molecules in the Wnt/β-catenin pathway), insulin-like growth factor (IGF)-II or IGF-I receptor, vascular endothelial growth factor, secretory clusterin and so on. In addition, combining immunotherapy with non-coding RNAs might improve anti-cancer efficacy. These biomarkers not only contribute to HCC diagnosis or prognosis, but may also become molecular targets for HCC therapy under developing or clinical trials. This article reviews the progress in emerging biomarkers in basic research or clinical trials for HCC immunotherapy.

Hepatocellular Carcinoma in 2021: An Exhaustive Update

Primary liver cancer is a challenging global health concern with an estimated more than a million persons to be affected annually by the year 2025. The commonest type is hepatocellular carcinoma (HCC), which has been increasing in incidence the world over, mostly due to chronic viral hepatitis B infection. In the last decade, paradigm changes in the etiology, understanding of molecular biology, and pathogenesis, including the role of gut microbiota; medical and surgical treatments, and outcome trends are notable. The application of omics-based technology has helped us unlock the molecular and immune landscape of HCC, through which novel targets for drug treatment such as immune-checkpoint inhibitors have been identified. Novel tools for the surveillance and diagnosis of HCC include protein-, genomics-, and composite algorithm-based clinical/biomarker panels. Magnetic resonance imaging-based novel techniques have improved HCC diagnosis through ancillary features that enhance classical criteria while positron emission tomography has shown value in prognostication. Identification of the role of gut microbiota in the causation and progression of HCC has opened areas for novel therapeutic research. A select group of patients still benefit from modified surgical and early interventional radiology treatments. Improvements in radiotherapy protocols, identification of parameters of futility among radiological interventions, and the emergence of novel first-line systemic therapies that include a combination of antiangiogenic and immune-checkpoint inhibitors have seen a paradigm change in progression-free and overall survival. The current review is aimed at providing exhaustive updates on the etiology, molecular biology, biomarker diagnosis, imaging, and recommended treatment options in patients with HCC.

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Objectives

We aimed to develop radiology-based models for the preoperative prediction of the initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) since the integration of radiomics and deep learning (DL) has not been reported for TACE.

Methods

Three hundred and ten intermediate-stage HCC patients who underwent TACE were recruited from three independent medical centers. Based on computed tomography (CT) images, recursive feature elimination (RFE) was used to select the most useful radiomics features. Five radiomics conventional machine learning (cML) models and a DL model were used for training and validation. Mutual correlations between each model were analyzed. The accuracies of integrating clinical variables, cML, and DL models were then evaluated.

Results

Good predictive accuracies were showed across the two cohorts in the five cML models, especially the random forest algorithm (AUC = 0.967 and 0.964, respectively). DL showed high accuracies in the training and validation cohorts (AUC = 0.981 and 0.972, respectively). Significant mutual correlations were revealed between tumor size and the five cML models and DL model (each P < 0.001). The highest accuracies were achieved by integrating DL and the random forest algorithm in the training and validation cohorts (AUC = 0.995 and 0.994, respectively).

Conclusion

The radiomics cML models and DL model showed notable accuracy for predicting the initial response to TACE treatment. Moreover, the integrated model could serve as a novel and accurate method for prediction in intermediate-stage HCC.