On January 26th, two patents were awarded to inventors from the Center for Computational Imaging and Personalized Diagnostics (CCIPD) and their collaborators: “Predicting pathological complete response to neoadjuvant chemotherapy from baseline breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI)” and “Predicting response to immunotherapy using computer extracted features relating to spatial arrangement of tumor infiltrating lymphocytes in non-small cell lung cancer”. Congratulations to Anant Madabhushi, PhD, Donnell Institute Professor, biomedical engineering and Director of CCIPD; Andrew Janowczyk, PhD, research assistant professor, biomedical engineering and CCIPD; Cristian Barrera, graduate student, biomedical engineering and CCIPD researcher; alumni of CCIPD Nathaniel Braman, PhD, and Xiangxue Wang, PhD; and collaborators Kavya Ravichandran and Vamsidhar Velcheti, MD. Read details about the patents below.
United States Serial Number (USSN): 10,902,591, January 26, 2021.
Inventors: Madabhushi; Anant, Braman; Nathaniel, Janowczyk; Andrew, Ravichandran; Kavya
Abstract: Embodiments access a pre-neoadjuvant chemotherapy (NAC) radiological image of a region of tissue demonstrating breast cancer (BCa), the region of tissue including a tumoral region, the image having a plurality of pixels; extract a set of patches from the tumoral region; provide the set of patches to a convolutional neural network (CNN) configured to discriminate tissue that will experience pathological complete response (pCR) post-NAC from tissue that will not; receive, from the CNN, a pixel-level localized patch probability of pCR; compute a distribution of predictions across analyzed patches based on the pixel-level localized patch probability; classify the region of tissue as a responder or non-responder based on the distribution of predictions, and display the classification. Embodiments may further generate a probability mask based on the pixel-level localized patch probability; and generate a heatmap of likelihood of response to NAC based on the probability mask and the pre-NAC radiological image.
United States Serial Number (USSN): 10,902,256, January 26, 2021.
Inventors: Madabhushi; Anant, Wang; Xiangxue, Barrera; Cristian, Velcheti; Vamsidhar
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.
