Center for Computational Imaging and Personalized Diagnostics researchers awarded two patents on January 26, 2021

Monday, February 8, 2021 - 13:46

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.

 

Predicting pathological complete response to neoadjuvant chemotherapy from baseline breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI)

 

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.

 

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

 

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.