Biomedical engineering’s Anant Madabhushi and team issued patents

Illustrated hand signing patent certificate

Radiomics has been shown to be important for prognostic and diagnostic applications, but what about predicting response to therapy? In fall 2019, seven patents were awarded to inventors in the Center for Computational Imaging and Personalized Diagnostics (CCIPD) and their collaborators for new radiomics to predict response and benefit of chemotherapy, radiation and immunotherapy. The Case Western Reserve University inventors are: Anant Madabhushi, the F. Alex Nason Professor II of Biomedical Engineering and director of the CCIPD; Research Assistant Professors Cheng Lu and Mehdi Alilou; and PhD candidates Nate Braman, Niha Beig and Mohammadhadi Khorrami.

The patents issued are listed below.

“Predicting cancer progression using cell run length features”

United States Serial Number (USSN) 10,503,959; Dec. 10
Inventors: Anant Madabhushi, Cheng Lu

Abstract: Embodiments include an image acquisition circuit configured to access an image of a region of tissue demonstrating cancerous pathology, a nuclei detection and graphing circuit configured to detect cellular nuclei represented in the image; and construct a nuclear sub-graph based on the detected cellular nuclei, where a node of the sub-graph is a nuclear centroid of a cellular nucleus; a cell run length (CRF) circuit configured to compute a CRF vector based on the sub-graph; compute a set of CRF features based on the CRF vector and the sub-graph; and generate a CRF signature based, at least in part, on the set of CRF features; and a classification circuit configured to compute a probability that the region of tissue will experience cancer progression, based, at least in part, on the CRF signature; and generate a classification of the region of tissue as a progressor or non-progressor.

Get more information about “Predicting cancer progression using cell run length features.”

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

USSN 10,492,723; Dec. 3
Inventors: Anant Madabhushi, Mehdi Alilou, Vamsidhar Velcheti

Abstract: 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.

Learn more about “Predicting immunotherapy response in non-small cell lung cancer patients with quantitative vessel tortuosity.”

“Characterizing lung nodule risk with quantitative nodule and perinodular radiomics”

USSN: 10,470,734; Nov. 12 
Inventors: Anant Madabhushi, Mahdi Orooji, Mirabela Rusu, Philip Linden, Robert Gilkeson, Nathaniel Braman, Mehdi Alilou

Abstract: 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.  

Find out more about “Characterizing lung nodule risk with quantitative nodule and perinodular radiomics.”

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

USSN: 10,458,895; Oct. 29
Inventors: Anant Madabhushi, Vamsidhar Velcheti, Mahdi Orooji, Sagar Rakshit, Mehdi Alilou, Niha Beig

Abstract: 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.

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

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

USSN: 10,441,215; Oct. 15
Inventors: Anant Madabhushi, Yuanqi Xie, Vamsidhar Velcheti

Abstract: One embodiment includes an image acquisition circuit that accesses a pre-treatment and a post-treatment image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), a segmentation and registration circuit that annotates the tumor represented in the images, and that registers the pre-treatment image with the post-treatment image; a feature extraction circuit that selects a set of pre-treatment and a set of post-treatment quantitative vessel tortuosity (QVT) features from the registered image; a delta-QVT circuit that generates a set of delta-QVT features by computing a difference between the set of post-treatment QVT features and the set of pre-treatment QVT features; and a classification circuit that generates a probability that the region of tissue will respond to immunotherapy based on the difference, and that classifies the region of tissue as a responder or non-responder. Embodiments may generate an immunotherapy treatment plan based on the classification.

Read about “Predicting immunotherapy response in non-small cell lung cancer patients with quantitative vessel tortuosity.”

“Predicting disease recurrence following trimodality therapy in non-small cell lung cancer using computed tomography derived radiomic features and clinico-pathologic features”

USSN: 10,441,225; Oct. 15
Inventors: Anant Madabhushi, Mohammadhadi Khorrami, Vamsidhar Velcheti 

Abstract: Embodiments include operations, apparatus, methods and other embodiments that access a baseline CT image of a region of tissue (ROT) demonstrating non-small cell lung cancer (NSCLC), segment a tumoral region represented in the baseline CT image; define a peritumoral region by dilating the tumoral boundary; extract a set of tumoral radiomic features from the tumoral region, a set of peritumoral radiomic features from the peritumoral region, and a set of clinico-pathologic features from the baseline CT image; provide the set of tumoral radiomic features, peritumoral radiomic features, and clinico-pathologic features to a machine learning classifier; receive, from the machine learning classifier, a time-to-recurrence post trimodality therapy (TMT) prediction, based on the set of tumoral radiomic features, peritumoral radiomic features, and clinico-pathologic features; generate a classification of the ROT as an MPR responder or MPR non-responder based, at least in part, on the time-to-recurrence post-TMT prediction; and display the classification.

Learn about “Predicting disease recurrence following trimodality therapy in non-small cell lung cancer using computed tomography derived radiomic features and clinico-pathologic features.”

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

USSN 10,398,399; Sep. 8
Inventors: Anant Madabhushi, Mahdi Orooji, Mirabela Rusu, Philip Linden, Robert Gilkeson, Nathaniel Mason Braman 

Abstract: 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.

Find out more about “Decision support for disease characterization and treatment response with disease and peri-disease radiomics.”

(From The Daily, 1/3/2020)