Classification of spatial patterns of lymphocyte infiltration in gliomas from whole slide imaging

Abstract Primary malignant brain tumors are among the most lethal cancers and are associated with poor survival. T cells are crucial components of the immune response against tumors. However, the spatial organization of T cells in brain tumors and their potential association with outcomes is poorly understood. In this study, we investigated the spatial distribution of T cells in human gliomas on microscopic images obtained after immunostaining the CD3 protein (T cell marker) on tumor tissue sections. First, recurrent, distinct, infiltration patterns of CD3 positive (CD3+) T cells were manually annotated by an expert pathologist. To predict these patterns, we implemented a two-step strategy. In the first step, we aimed to distinguish microscopic images with or without CD3 + T cells using two input types. A 2D convolutional neural network (CNN) was trained on density maps derived from CD3 + segmentation, while an XGBoost model was applied to features extracted by a VGG16-pretrained network. Both models performed well, achieving accuracy greater than 0.8. In the second step, we analyzed spatial patterns of lymphocyte aggregation on image patches. The lymphocyte presence and their aggregation types were further classified into two classes (T cell aggregation close to blood vessels versus diffuse infiltration of tumor tissue) with a final combined global accuracy of 0.81. These results demonstrate that the proposed novel approach allows for automated classification of tumor samples according to spatial patterns of T cell infiltration. This will enable future investigations of biological mechanisms underlying distinct immune responses and their potential association with outcomes.