System and method for predicting the risk of future lung cancer
AssigneeJohnson & Johnson Enterprise Innovation Inc.
InventorsGeorge R. Washko, JR., Christopher Scott Stevenson, Samuel Yoffe Ash, Raul San Jose Estepar, Matthew David Mailman
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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.
Claims33
A method for predicting one or more future risks of lung cancer for a subject, the method comprising:
- obtaining one or more images captured from the subject at a single timepoint;
- extracting features from the one or more obtained images, the extracted features comprising at least non-nodule specific features, wherein the non-nodule specific features comprise one or both of lung parenchyma features or body composition features;
- predicting one or more future risks of lung cancer for the subject by applying one or more trained risk prediction models to analyze the extracted features from the one or more obtained images.
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A non-transitory computer readable medium for predicting one or more future risks of lung cancer for a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to:
- obtain one or more images captured from the subject at a single timepoint;
- extract features from the one or more obtained images, the extracted features comprising at least non-nodule specific features, wherein the non-nodule specific features comprise one or both of lung parenchyma features or body composition features;
- predict one or more future risks of lung cancer for the subject by applying one or more trained risk prediction models to analyze the extracted features from the one or more obtained images.
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Dependencies are inferred from the claim text.