Brain tumors are among the most lethal cancers, exhibiting low survival rates compared to all cancer types. In this study, we investigate CD3+ lymphocytes (based on immunohistochemistry stained microscopic images), crucial components of the immune response that contribute significantly to tumor defense mechanisms. Neurologists have identified specific infiltration patterns of CD3+ lymphocytes in gliomas. To predict these patterns, we implemented a two-step strategy. In the first step, we aimed to distinguish microscopic images with or without CD3+ lymphocytes using two input types. A 2D 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. This pattern analysis accurately predicted aggregation classes with an accuracy of more than 0.8. Perspective of this study will offer insights into survival outcomes.