Case Western Reserve University

The Cleveland Clinic Foundation

Ventana Medical Systems, Inc.

Xiangxue Wang

Anant Madabhushi

Vamsidhar Velcheti

Cristian Barrera

Jim F Martin

Yao Nie

Pranjal Vaidya

Qinle Ba

Mohammadhassan Izady Yazdanabadi

Satarupa Mukherjee

German Corredor Prada

Methods and systems can include: accessing a digital pathology image; generating, using a first machine-learning model, a segmented image that identifies at least: a predicted diseased region and a background region in the digital pathology image; detecting depictions of a set of cells in the digital pathology image; generating, using a second machine-learning model, a cell classification for each cell of the set of cells, wherein the cell classification is selected from a set of potential classifications that indicate which, if any, of a set of biomarkers are expressed in the cell; detecting that a subset of the set of cells are within the background region; and updating the cell classification for each cell of at least some cells in the subset to be a background classification that was not included in the set of potential classifications.

Hybrid and accelerated ground-truth generation for duplex arrays

Embodiments discussed herein facilitate generation of a prognosis for a medical condition based on determination of one or more histomorphometric features for tiles of a whole slide image (WSI) that have been identified as the most prognostically significant tiles of the WSI. A first set of embodiments discussed herein relates to training of a fully convolutional network (FCN) to determine the prognostic significance of pixels of a WSI. A second set of embodiments discussed herein relates to determination of a prognosis based on analysis of regions identified as the most prognostically significant by a trained FCN.

Sequential integration of adversarial networks with handcrafted features (SANwicH): identifying sites of prognostic significance for predicting cancer recurrence

Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.

Predicting response to immunotherapy using computer extracted features of cancer nuclei from hematoxylin and eosin (HandE) stained images of non-small cell lung cancer (NSCLC)

Embodiments predict early stage NSCLC recurrence, and include an image acquisition circuit configured to access an image of a region of tissue demonstrating early-stage NSCLC including a plurality of cellular nuclei; a nuclei detecting and segmentation circuit configured to detect a member of the plurality; and classify the member as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; a spatial TIL feature circuit configured to extract spatial TIL features from the plurality, the spatial TIL features including a first subset of features based on the spatial arrangement of TIL nuclei, and a second subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; and an NSCLC recurrence classification circuit configured to compute a probability that region will experience recurrence based on the spatial TIL features; and generate a classification of the region as likely or unlikely to experience recurrence based on the probability.

Predicting recurrence in early stage non-small cell lung cancer (NSCLC) using spatial arrangement of clusters of tumor infiltrating lymphocytes and cancer nuclei

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.

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

Embodiments predict early stage NSCLC recurrence, and include processors configured to access a pathology image of a region of tissue demonstrating early stage NSCLC; extract a set of pathomic features from the pathology image; access a radiological image of the region of tissue; extract a set of radiomic features from the radiological image; generate a combined feature set that includes at least one member of the set of pathomic features, and at least one member of the set of radiomic features; compute a probability that the region of tissue will experience NSCLC recurrence based, at least in part, on the combined feature set; and classify the region of tissue as recurrent or non-recurrent based, at least in part, on the probability. Embodiments may display the classification, or generate a personalized treatment plan based on the classification.

Predicting recurrence in early stage non-small cell lung cancer (NSCLC) with integrated radiomic and pathomic features

Methods and apparatus predict non-small cell lung cancer (NSCLC) recurrence using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating NSCLC, a segmentation circuit that segments a cellular nucleus from the image, a feature extraction circuit that extracts a set of features from the image, a tumor infiltrating lymphocyte (TIL) identification circuit that classifies the segmented nucleus as a TIL or non-TIL, a graphing circuit that constructs a TIL graph and computes a set of TIL graph statistical features, and a classification circuit that computes a probability that the region will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A treatment plan may be provided based on the risk score.

Prediction of recurrence of non-small cell lung cancer with tumor infiltrating lymphocyte (TIL) graphs

Methods, apparatus, and other embodiments associated with predicting non-small cell lung cancer (NSCLC) patient response to adjuvant chemotherapy therapy using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an H&E image of a region of tissue demonstrating NSCLC pathology, a segmentation circuit that segments a region of interest (ROI) from the diagnostic radiological image, a feature extraction that extracts a set of discriminative features from the ROI, and a classification circuit that generates a probability that the ROI will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A prognosis or treatment plan may be provided based on the quantitative continuous image-based risk score.

Growing community of inventors

Cleveland Heights, OH, United States of America

Xiangxue Wang