The presence of CD8+ T cells in the tumor microenvironment is associated with response to immunotherapy and can inform patient treatment decisions1-3. However, characterization of immune cells in the tumor microenvironment is subject to challenges of manual scoring and interpathologist scoring variability.
To support the quantification and scoring of CD8+ T cells, PathAI has developed machine learning (ML)-based models that can identify and quantify CD8+ lymphocytes within the stroma and parenchyma regions of melanoma and other tumor types. Here, we focus on the ML model for melanoma showing recent results for ML-based identification and quantification of CD8+ lymphocytes and concordance with manual pathologic assessment in data derived from multiple clinical trials. Glass et al.
To support the quantification and scoring of CD8+ T cells, PathAI has developed machine learning (ML)-based models that can identify and quantify CD8+ lymphocytes within the stroma and parenchyma regions of melanoma and other tumor types. Here, we focus on the ML model for melanoma showing recent results for ML-based identification and quantification of CD8+ lymphocytes and concordance with manual pathologic assessment in data derived from multiple clinical trials. Glass et al.
