Abstract: Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.
Type:
Grant
Filed:
March 10, 2023
Date of Patent:
January 9, 2024
Assignee:
Johnson & Johnson Enterprise Innovation Inc.
Inventors:
George R. Washko, Jr., Christopher Scott Stevenson, Samuel Yoffe Ash, Raul San Jose Estepar, Matthew David Mailman
Abstract: Provided herein, in certain aspects, are antibodies that bind to IL-1? and compositions comprising the antibodies. Methods of making and using the antibodies are also provided.
Type:
Application
Filed:
January 6, 2023
Publication date:
July 20, 2023
Applicant:
JOHNSON & JOHNSON ENTERPRISE INNOVATION INC.
Inventors:
Gabriel PASCUAL, Sagit HINDI, Suresh Kumar SWAMINATHAN
Abstract: Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.
Type:
Grant
Filed:
January 15, 2021
Date of Patent:
May 2, 2023
Assignee:
Johnson & Johnson Enterprise Innovation Inc.
Inventors:
George R. Washko, Jr., Christopher Scott Stevenson, Samuel Yoffe Ash, Raul San Jose Estepar, Matthew David Mailman