Predicting response to immunotherapy using computer extracted features relating to spatial arrangement of tumor infiltrating lymphocytes in non-small cell lung cancer

Abstract

Embodiments include controlling a processor to perform operations, the operations comprising: accessing a digitized image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), detecting a member of a plurality of cellular nuclei represented in the image; classifying the member of the plurality of cellular nuclei as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; extracting spatial TIL features from the plurality of cellular nuclei, including a first subset of features based on the spatial arrangement of TIL nuclei, and a second, different subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; generating a set of graph interplay features based on the set of spatial TIL features; providing the set of graph interplay features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will respond to immunotherapy, based, at least in part, on the set of graph interplay features; classifying the region of tissue as likely to respond to immunotherapy or unlikely to respond to immunotherapy based, at least in part, on the probability; and displaying the classification.