Case Western Reserve University

The Cleveland Clinic Foundation

Niha Beig

Anant Madabhushi

Vamsidhar Velcheti

Mahdi Orooji

Mehdi Alilou

Sagar Rakshit

Pallavi Tiwari

Prateek Prasanna

Marwa Ismail

Embodiments discussed herein facilitate determination of tumor mutation status based on context and spatial information. One example embodiment can access a MRI scan of a tumor comprising voxels; extract radiomic feature(s) from the voxels; generate a spatial feature descriptor indicating probabilities the tumor has a first mutation status and a second mutation status, based on the MRI scan, a first population atlas for the first mutation status, and a second population atlas for the second mutation status; provide the radiomic feature(s) and the spatial feature descriptor to a machine learning model; and receive, via the machine learning model, a map indicating, for each voxel of the voxels, a probability of the first mutation status for that voxel and a probability of the second mutation status for that voxel, wherein the map is based at least on the one or more radiomic features and the spatial feature descriptor.

Spatial-and-context aware virtual biopsy radiogenomic maps to target tumor mutational status

Methods, apparatus, and other embodiments predict response to immunotherapy from computed tomography (CT) images of a region of tissue demonstrating non-small cell lung cancer (NSCLC). One example apparatus includes a set of circuits that includes an image acquisition circuit that accesses a CT image of a region of tissue demonstrating cancerous pathology, a tumoral definition circuit that generates a tumoral surface boundary that defines a tumoral volume, a peritumoral segmentation circuit that generates a peritumoral region based on the tumoral surface boundary, and that segments the peritumoral region into a plurality of annular bands, a radiomics circuit that extracts a set of discriminative features from the tumoral volume and at least one of the plurality of annular bands, and a classification circuit that classifies the ROI as a responder or a non-responder, based, at least in part, on the set of discriminative features.

Methods and apparatus for predicting benefit from immunotherapy using tumoral and peritumoral radiomic features

Methods, apparatus, and other embodiments predict response to pemetrexed based chemotherapy. One example apparatus includes an image acquisition circuit that acquires a radiological image of a region of tissue demonstrating NSCLC that includes a region of interest (ROI) defining a tumoral volume, a peritumoral volume definition circuit that defines a peritumoral volume based on the boundary of the ROI and a distance, a feature extraction circuit that extracts a set of discriminative tumoral features from the tumoral volume, and a set of discriminative peritumoral features from the peritumoral volume, and a classification circuit that classifies the ROI as a responder or a non-responder using a machine learning classifier based, at least in part, on the set of discriminative tumoral features and the set of discriminative peritumoral features.

Predicting response to pemetrexed chemotherapy in non-small cell lung cancer (NSCLC) with baseline computed tomography (CT) shape and texture features

Methods, apparatus, and other embodiments predict tumor infiltrating lymphocyte (TIL) density from pre-surgical computed tomography images of a region of tissue demonstrating non-small cell lung cancer (NSCLC). One example apparatus includes a set of circuits that includes an image acquisition circuit that accesses a radiological image of a region of tissue demonstrating cancerous pathology, where the radiological image has a plurality of pixels, and where the radiological image includes an annotated region of interest (ROI), a feature extraction circuit that extracts a set of radiomic features from the ROI, where the set of radiomic features includes at least two texture features and at least one shape feature, and a classification circuit that comprises a machine learning classifier that classifies the ROI as high tumor infiltrating lymphocyte (TIL) density, or low TIL density, based, at least in part, on the set of radiomic features.

Growing community of inventors

Cleveland Heights, OH, United States of America

Niha Beig