Case Western Reserve University

Mirabela Rusu

Anant Madabhushi

Mahdi Orooji

Nathaniel Braman

Robert Gilkeson

Philip Linden

Mehdi Alilou

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 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 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, a probability logic that computes a probability that the nodule is benign or that the nodule will respond to a treatment, 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.

Decision support for disease characterization and treatment response with disease and peri-disease radiomics

Methods and apparatus associated with producing a quantification of differences associated with biochemical recurrence (BcR) in a region of tissue demonstrating prostate cancer (PCa) are described. One example apparatus includes a set of logics, and a data store that stores a set of magnetic resonance (MR) images acquired from a population of subjects. The set of logics includes an image acquisition logic that acquires a diagnostic image of a region of tissue in a patient demonstrating PCa, a morphology logic that extracts a shape feature, a volume feature, or an intensity feature from the diagnostic image or from a member of the set of MR images, a differential atlas construction logic that constructs a statistical shape differential atlas from the set of MR images, and a quantification logic that produces a quantification of differences based on the shape feature, the volume feature, or the intensity feature, and the differential atlas.

Differential atlas for cancer assessment

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 and apparatus distinguish invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS). One example apparatus includes a set of circuits, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of circuits includes a classification circuit that generates an invasiveness classification for a diagnostic 3D radiological image, a training circuit that trains the classification circuit to identify a texture feature associated with IA, an image acquisition circuit that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification circuit, and a prediction circuit that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training circuit trains the classification circuit using a set of 3D histological reconstructions combined with the set of 3D radiological images.

Disease characterization from fused pathology and radiology data

Methods and apparatus associated with distinguishing invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS) are described. One example apparatus includes a set of logics, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of logics includes a classification logic that generates an invasiveness classification for a diagnostic 3D radiological image, a training logic that trains the classification logic to identify a texture feature associated with IA, an image acquisition logic that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification logic, and a prediction logic that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training logic trains the classification logic using a set of 3D histological reconstructions combined with the set of 3D radiological images.

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

Mirabela Rusu