The role of tumor-associated macrophages (TAMs) in the pathogenesis of hepatocellular carcinoma (HCC) is poorly understood. Most studies rely on platforms that remove intrahepatic macrophages from the microenvironment prior to evaluation. Cell isolation causes activation and phenotypic changes that may not represent their actual biology and function in situ. State-of-the-art methods provides new strategies to study TAMs without losing the context of tissue architecture and spatial relationship with neighboring cells. These technologies, such as multispectral imaging (e.g., Vectra Polaris), mass cytometry by time-of-flight (e.g., Fluidigm CyTOF), cycling of fluorochromes (e.g., Akoya Biosciences CODEX/PhenoCycler-Fusion, Bruker Canopy, Lunaphore Comet, and CyCIF) and digital spatial profiling or transcriptomics (e.g., GeoMx or Visium, Vizgen Merscope) are being utilized to accurately assess the complex cellular network within the tissue microenvironment. In cancer research, these platforms enable characterization of immune cell phenotypes and expression of potential therapeutic targets, such as PDL-1 and CTLA-4. Newer spatial profiling platforms allow for detection of numerous protein targets, in combination with whole transcriptome analysis, in a single liver biopsy tissue section. Macrophages can also be specifically targeted and analyzed, enabling quantification of both protein and gene expression within specific cell phenotypes, including TAMs. This review describes the workflow of each platform, summarizes recent research using these approaches, and explains the advantages and limitations of each.

Background: Penile squamous cell carcinoma is an aggressive malignancy with significant physical and psychological impacts. Socioeconomic factors influence prognosis in genitourinary cancers, making the investigation of insurance status critical for reducing cancer burden and promoting health equity. Materials and Methods: Men diagnosed with primary penile squamous cell carcinoma from 2007 to 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) national database. Participants were categorized based on insurance status: privately insured, Medicaid, and uninsured. Pearson’s chi-squared test assessed the distribution of observed frequencies between the patient demographics, socioeconomic status, tumor characteristics, and surgical variables across the insurance groups. Overall and cancer-specific survival was estimated using a multivariate Cox hazards proportional model analysis. Results: The multivariate Cox hazards proportional model showed that, compared to privately insured patients, Medicaid patients had an increased risk for overall death (hazard ratio [HR] = HR 1.54; 95% CI, 1.12–2.07). For cancer-specific mortality, Medicaid patients had an increased risk of death compared to privately insured patients (HR 1.58; 95% CI, 1.11–2.25). Conclusions: Medicaid does not mitigate the differences caused by health insurance status due to health insurance disparities for overall or cancer-specific mortality. Lower Medicaid reimbursements and out-of-pocket costs lead to a narrow network of physicians, hospitals, and treatment modalities that compromise health equity. Increasing awareness of health insurance disparities and improving access to care via a clinician–community–governmental partnership can potentially lead to improved predictive outcomes.