Hepatocellular carcinoma is leading in cancer-related disease burden among hospitalized baby boomers

Racial disparities in clinical presentation, surgical procedures, and hospital outcomes among patients with hepatocellular carcinoma in the United States

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths in the United States (US), with substantial disparities observed in cancer incidence and survival among racial groups. This study provides analyses on race and ethnicity disparities for patients with HCC.

METHODS

This is a cross-sectional analysis of data from the National Inpatient Sample (NIS) between 2011 and 2016, utilizing the STROBE guidelines. Multivariate logistic regression analyses were used to examine the risk-adjusted associations between race and pre-treatment clinical presentation, surgical procedure allocation, and post-treatment hospital outcomes. All clinical parameters were identified using ICD-9-CM and ICD-10-CM diagnosis and procedure codes.

RESULTS

83,876 weighted HCC hospitalizations were reported during the study period. Patient demographics were divided according to NIS racial/ethnic categorization, which includes Caucasian (57.3%), African American (16.9%), Hispanic (15.7%), Asian or Pacific Islanders (9.3%), and Native American (0.8%). Association between greater odds of hospitalization and Elixhauser Comorbidity Index > 4 was significantly higher among Native Americans (aOR=1.79; 95% CI: 1.23-2.73), African Americans (aOR=1.24; 95% CI: 1.12-1.38), and Hispanics (aOR=1.11; 95% CI, 1.01-1.24). Risk-adjusted association between race and receipt of surgical procedures demonstrated that the odds of having surgery was significantly lower for African Americans (aOR=0.64; 95% CI: 0.55-0.73) and Hispanics (aOR=0.70; 95% CI: 0.59-0.82), while significantly higher for Asians/Pacific Islanders (aOR=1.36; 95% CI: 1.28-1.63). Post-operative complications were significantly lower for African Americans (aOR=0.68; 95% CI: 0.55-0.86) while the odds of in-hospital mortality were significantly higher for African Americans (aOR=1.28; 95% CI: 1.11-1.49) and Asians/Pacific Islanders (aOR=1.26; 95% CI: 1.13-1.62).

CONCLUSIONS

After controlling for potential confounders, there were significant racial disparities in pre-treatment presentations, surgical procedure allocations, and post-treatment outcomes among patients with HCC. Further studies are needed to determine the underlying factors for these disparities to develop targeted interventions to reduce these disparities of care.

Epidemiology and Outcomes of Hospitalizations Due to Hepatocellular Carcinoma

Background Hepatocellular Carcinoma (HCC) is a severe complication of cirrhosis and the incidence of HCC has been increasing in the United States (US). We aim to describe the trends, characteristics, and outcomes of hospitalizations due to HCC across the last decade. Methods We derived a study cohort from the Nationwide Inpatient Sample (NIS) for the years 2008-2017. Adult hospitalizations due to HCC were identified using the International Classification of Diseases (9th/10th Editions) Clinical Modification diagnosis codes (ICD-9-CM/ICD-10-CM). Comorbidities were also identified by ICD-9/10-CM codes and Elixhauser Comorbidity Software (Agency for Healthcare Research and Quality, Rockville, Maryland, US). Our primary outcomes were in-hospital mortality and discharge to the facility. We then utilized the Cochran-Armitage trend test and multivariable survey logistic regression models to analyze the trends, outcomes, and predictors. Results A total of 155,436 adult hospitalizations occurred due to HCC from 2008-2017. The number of hospitalizations with HCC decreased from 16,754 in 2008 to 14,715 in 2017. Additionally, trends of in-hospital mortality declined over the study period but discharge to facilities remained stable. Furthermore, in multivariable regression analysis, predictors of increased mortality in HCC patients were advanced age (OR 1.1; 95%CI 1.0-1.2; p< 0.0001), African American (OR 1.3; 95%CI 1.1-1.4;p< 0.001), Rural/ non-teaching hospitals (OR 2.7; 95%CI 2.4-3.3; p< 0.001), uninsured (OR 1.9; CI 1.6-2.2; p< 0.0001) and complications like septicemia and pneumonia as well as comorbidities such as hypertension, diabetes mellitus, and renal failure. We observed similar trends in discharge to facilities. Conclusions In this nationally representative study, we observed a decrease in hospitalizations of patients with HCC along with in-hospital mortality; however, discharge to facilities remained stable over the last decade. We also identified multiple predictors significantly associated with increased mortality, some of which are potentially modifiable and can be points of interest for future studies.

Multiparametric Analyses of Hepatocellular Carcinoma Somatic Mouse Models and Their Associated Tumor Microenvironment

The rising incidence and increasing mortality of hepatocellular carcinoma (HCC), combined with its high tumor heterogeneity, lack of druggable targets, and tendency to develop resistance to chemotherapeutics, make the development of better models for this cancer an urgent challenge. To better mimic the high diversity within the HCC genetic landscape, versatile somatic murine models have recently been developed using the hydrodynamic tail vein injection (HDTVi) system. These represent novel in vivo tools to interrogate HCC phenotype and response to therapy, and importantly, allow further analyses of the associated tumor microenvironment (TME) shaped by distinct genetic backgrounds. Here, we describe several optimized protocols to generate, collect, and experimentally utilize various samples obtained from HCC somatic mouse models generated by HDTVi. More specifically, we focus on techniques relevant to ex vivo analyses of the complex liver TME using multiparameter flow cytometric analyses of over 21 markers, immunohistochemistry, immunofluorescence, and histochemistry. We describe the transcriptional assessment of whole tissue, or of isolated immune subsets by flow-cytometry-based cell sorting, and other protein-oriented analyses. Together, these streamlined protocols allow the optimal use of each HCC murine model of interest and will assist researchers in deciphering the relations between cancer cell genetics and systemic and local changes in immune cell landscapes in the context of HCC progression. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Generation of HCC mouse models by hydrodynamic tail vein injection Basic Protocol 2: Assessment of HCC tumor progression by magnetic resonance imaging Basic Protocol 3: Mouse sacrifice and sample collection in HCC mouse models Support Protocol 1: Preparation of serum or plasma from blood Basic Protocol 4: Single-cell preparation and HCC immune landscape phenotyping by flow cytometry Alternate Protocol 1: Flow cytometric analysis of circulating immune cells Support Protocol 2: Generation, maintenance, and characterization of HCC cell lines Support Protocol 3: Fluorescence-activated cell sorting of liver single-cell preparation Basic Protocol 5: Preparation and immunohistochemical analysis of tumor tissues from HCC-bearing liver Alternate Protocol 2: Preparation and analyses for immunofluorescence staining of HCC-bearing liver Support Protocol 4: Liver-specific phenotypic analyses of liver sections Support Protocol 5: Immunohistochemical quantification in liver sections Basic Protocol 6: Preparation of snap-frozen tumor tissue from extracted liver and transcriptional analyses of bulk tumor or sorted cells Alternate Protocol 3: Protein analyses from HCC samples and serum or plasma.

A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection

Survey is one of the crucial data retrieval methods in the literature. However, surveys often contain missing data and redundant features. Therefore, missing feature completion and feature selection have been widely used for knowledge extraction from surveys. We have a hypothesis to solve these two problems. To implement our hypothesis, a classification method is presented. Our proposed method consists of missing feature completion with a statistical moment (average) and feature selection using a novel swarm optimization method. Firstly, an average based supervised feature completion method is applied to Hepatocellular Carcinoma survey (HCC). The used HCC survey consists of 49 features. To select meaningful features, a chaotic Darcy optimization based feature selection method is presented and this method selects 31 most discriminative features of the completed HCC dataset. 0.9879 accuracy rate was obtained by using the proposed chaotic Darcy optimization-based HCC survival classification method.