Triple-negative breast cancer (TNBC) is defined by a lack of biomarkers in the tumor. This inherent lack of targets results in a lack of effective therapeutic tools. However, immunotherapies have shown promise in treating TNBC. Here, we present computer vision methods for automatic detection of immune cells and larger immune structures in TNBC. We demonstrate accurate cell detection and segmentation in highly-multiplexed, whole-slide images of TNBC biopsies. Additionally, we show preliminary spatial analyses that identify and characterize tertiary lymphoid structures within the tumor. Ultimately, we hope to implement these methods to predict responders and non-responders to immunotherapy regimens for TNBC.
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