Radioembolization plus Immune Checkpoint Inhibitor Therapy Compared with Radioembolization plus Tyrosine Kinase Inhibitor Therapy for the Treatment of Hepatocellular Carcinoma.

PURPOSE: To investigate if combination therapy with immune checkpoint inhibitor (ICI) and yttrium-90 (90Y) radioembolization results in superior outcomes than those yielded by tyrosine kinase inhibitor (TKI) therapy and 90Y for the treatment of intermediate- to advanced-stage hepatocellular carcinoma (HCC). METHODS: A retrospective review of patients presented at an institutional multidisciplinary liver tumor board between January 1, 2012 and August 1, 2023 was conducted. In total, 44 patients with HCC who underwent 90Y 4 weeks within initiation of ICI or TKI therapy were included. Propensity score matching was conducted to account for baseline demographic differences. Kaplan-Meier analysis was used to compare median progression-free survival (PFS) and overall survival (OS), and univariate statistics identified disease response and control rate differences. Duration of imaging response was defined as number of months between the first scan after therapy and the first scan showing progression as defined by modified Response Evaluation Criteria in Solid Tumors (mRECIST) or immune Response Evaluation Criteria in Solid Tumors (iRECIST). Adverse events were analyzed per Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. RESULTS: Patients in the 90Y+ICI therapy group had better objective response rates (ORRs) (89.5% vs 36.8%; P < .001) and disease control rates (DCRs) (94.7% vs 63.2%; P < .001) by mRECIST and iRECIST (ORR: 78.9% vs 36.8%; P < .001; DCR: 94.7% vs 63.2%; P < .001). Median PFS (8.3 vs 4.1 months; P = .37) and OS (15.8 vs 14.3 months; P = .52) were not statistically different. Twelve patients (63.1%) in the 90Y+TKI group did not complete systemic therapy owing to adverse effects compared with 1 patient (5.3%) in the 90Y+ICI group (P < .001). Grade 3/4 adverse events were not statistically different (90Y+TKI: 21.1%; 90Y+ICI: 5.3%; P = .150). CONCLUSIONS: Patients with HCC who received 90Y+ICI had better imaging response and fewer regimen-altering adverse events than those who received 90Y+TKI. No significant combination therapy adverse events were attributable to radioembolization.

Individual Socioeconomic Factors Have a Greater Impact on End-of-Life Care Outcomes Than Regional Factors.

Background: End-of-life (EoL) care provided to Americans in urban and rural settings is distinct in terms of both available and delivered services. However, much less is known about which geographic, demographic, and health indicators are associated with disparities in EoL care and how individual versus regional characteristics influence quality of care (QoC). Objective: This study aimed to assess how regionality, rurality, and individual socioeconomic factors are associated with QoC in the last month of life (LML). Design: Nationally representative cross-sectional study using the proxy-completed LML questionnaire as part of the National Health and Aging Trends Study (NHATS). The data were linked at the zip code level to geographic and economic indicators. Settings/Subjects: A total of 2778 NHATS enrollees who died from 2012 to 2020. Measurements: Measurements included population density, socioeconomic indicators, health factors, and health outcomes. The primary independent variable was proxy-reported QoC during the LML (excellent vs. not excellent). Results: In our sample, 52.1% (n = 1447) reported not excellent care and 47.9% (n = 1331) reported excellent care. These populations varied in their demographic and socioeconomic characteristics. After accounting for survey weighting and design, decedents in the top (odds ratio [OR]: 1.58; 95% confidence interval [CI]: 1.08-2.32) income quartile had significantly greater odds of receiving excellent care than decedents in the bottom quartile. Decedents in zip codes with top quartile health outcome metrics had significantly greater odds of receiving excellent care (OR: 1.64; 95% CI: 1.17-2.29) than decedents in zip codes with bottom quartile health outcomes. County rurality index and county health factors were not correlated with QoC in the LML. Conclusions: High QoC at the EoL may be more associated with individual socioeconomic factors than regional indicators, including degrees of rurality. Clinicians should strive to recognize the interplay of individual characteristics and regional indicators to provide more personalized care.