Case Western Reserve University

Mehdi Alilou

Anant Madabhushi

Mahdi Orooji

Vamsidhar Velcheti

Mirabela Rusu

Nathaniel Braman

Robert Gilkeson

Philip Linden

Niha Beig

Sagar Rakshit

Embodiments classify lung nodules by accessing a 3D radiological image of a region of tissue, the 3D image including a plurality of voxels and slices, a slice having a thickness; segmenting the nodule represented in the 3D image across contiguous slices, the nodule having a 3D volume and 3D interface, where the 3D interface includes an interface voxel; partitioning the 3D interface into a plurality of nested shells, a nested shell including a plurality of 2D slices, a 2D slice including a boundary pixel; extracting a set of intra-perinodular textural transition (Ipris) features from the 2D slices based on a normal of a boundary pixel of the 2D slices; providing the Ipris features to a machine learning classifier which computes a probability that the nodule is malignant, based, at least in part, on the set of Ipris features; and generating a classification of the nodule based on the probability.

Intra-perinodular textural transition (ipris): a three dimenisonal (3D) descriptor for nodule diagnosis on lung computed tomography (CT) images

Intra-perinodular textural transition (IPRIS): a three dimenisonal (3D) descriptor for nodule diagnosis on lung computed tomography (CT) images

Embodiments classify a region of tissue demonstrating non-small cell lung cancer using quantified vessel tortuosity (QVT). One example apparatus includes annotation circuitry configured to segment a lung region from surrounding anatomy in the region of tissue represented in a radiological image and segment a nodule from the lung region by defining a nodule boundary; vascular segmentation circuitry configured to generate a three dimensional (3D) segmented vasculature by segmenting a vessel associated with the nodule, and to identify a center line of the 3D segmented vasculature; QVT feature extraction circuitry configured to extract a set of QVT features from the radiological image; and classification circuitry configured to compute a probability that the region of tissue will respond to immunotherapy and generate a classification that the region of tissue is a responder or a non-responder based, at least in part, on the probability.

Predicting immunotherapy response in non-small cell lung cancer patients with quantitative vessel tortuosity

Embodiments associated with classifying a region of tissue using features extracted from nodules and surrounding structures. One example apparatus includes a feature extraction circuit configured to automatically extract a first set of quantitative features from a nodule represented in at least one CT image, and automatically extract a second set of quantitative features from the lung parenchyma region immediately surrounding the nodule represented in the at least one CT image; a feature selection circuit configured to select an optimally predictive feature set from the first set of quantitative features and the second set of quantitative features; and a training circuit configured to train a classifier using the optimally predictive feature set to assign malignancy risk to a lung nodule represented in a CT image of a region of tissue demonstrating lung nodules. A prognosis or treatment plan may be provided based on the malignancy risk.

Characterizing lung nodule risk with quantitative nodule and perinodular radiomics

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.

Computerized analysis of computed tomography (CT) imagery to quantify tumor infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC)

Methods, apparatus, and other embodiments associated with classifying a region of tissue using quantified vessel tortuosity are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating cancerous pathology, a delineation logic that distinguishes nodule tissue within the image from the background of the image, a perinodular zone logic that defines a perinodular zone based on the nodule, a feature extraction logic that extracts a set of features from the image including a set of tortuosity features, a probability logic that computes a probability that the nodule is benign, and a classification logic that classifies the nodule tissue based, at least in part, on the set of features or the probability. A prognosis or treatment plan may be provided based on the classification of the image.

Characterizing disease and treatment response with quantitative vessel tortuosity radiomics

Methods, apparatus, and other embodiments associated with classifying a region of tissue using textural analysis are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating GGO nodule pathology, a delineation logic that distinguishes GGO nodule tissue within the image from the background of the image, a texture logic that extracts a set of texture features from the image, a phenotype signature logic that computes a phenotypic signature from the image, a shape logic that extracts a set of shape features from the image, and a classification logic that classifies the GGO nodule tissue based, at least in part, on the set of texture features, the phenotypic signature, or the set of shape features. A prognosis for a patient may be provided based on the classification of the image.

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Cleveland, OH, United States of America

Mehdi Alilou