3d radiomic platform for imaging biomarker development
AssigneeTempus Labs, Inc.
InventorStephen Yip
Life of this patent
Estimating the term…
Abstract
A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.
Claims32
A computer-implemented method to analyze medical image data, the method comprising:
- obtaining, using one or more processors, the medical image data comprising a plurality of two-dimensional (2D) medical images;
- performing, using the one or more processors, target tissue detection and target tissue segmentation for each 2D medical image to produce a set of segmented target tissue images;
- generating, using the one or more processors, a three-dimensional (3D) model of detected and segmented target tissue;
- identifying, using the one or more processors, a master set of radiomic features for the 3D model of detected and segmented target tissue;
- comparing, using the one or more processors, at least some of the radiomic features in the master set to identify redundant radiomic features for the 3D model of detected and segmented target tissue;
- excluding, using the one or more processors, the redundant radiomic features from the 3D model of detected and segmented target tissue;
- and
- extracting, using the one or more processors, a selected set of radiomic features from the 3D model of detected and segmented target tissue.
A computer-implemented method for generating radiomic features for use in a 3D model of a tumor, the method comprising:
- performing, using the one or more processors, using a convolution neural network, model target tissue detection and target tissue segmentation for a plurality of 2D medical images to produce a set of segmented target tissue images;
- generating, using the one or more processors, a 3D model of detected and segmented target tissue from a plurality of 2D medical images;
- identifying, using the one or more processors, using a convolution neural network, a master set of radiomic features for the 3D model of detected and segmented target tissue;
- identifying, using the one or more processors, using a statistical model, radiomic features that are significantly associated with clinical outcomes and/or genomic data;
- and
- storing or displaying the significantly associated radiomic features in an enhanced 3D model or in a digital report.
A computing device comprising:
- one or more processors;
- a user interface;
- and
- a computer-readable memory coupled to the one or more processors, the memory storing instructions that cause the one or more processors to:
- perform target tissue detection and target tissue segmentation for each of a plurality of 2D medical image and produce a set of segmented target tissue images;
- generate a 3D model of detected and segmented target tissue;
- identify a master set of radiomic features for the 3D model of detected and segmented target tissue;
- compare at least some of the radiomic features in the master set to identify redundant radiomic features for the 3D model of detected and segmented target tissue;
- exclude the redundant radiomic features from the 3D model of detected and segmented target tissue;
- extract a selected set of radiomic features from the 3D model of detected and segmented target tissue;
- and
- store or display the extracted set of radiomic features in an enhanced 3D model or in a digital report.
A computer-implemented method to analyze medical image data, the method comprising:
- obtaining, using one or more processors, a first plurality of 2D medical images captured at a first time period, performing, using the one or more processors, target tissue detection and target tissue segmentation on each of the first plurality of 2D medical images to produce a set of segmented target tissue images, and generating, using the one or more processors, a first 3D model of detected and segmented target tissue;
- identifying, using the one or more processors, set of radiomic features in the first 3D model;
- obtaining, using one or more processors, a second plurality of 2D medical images captured at a second time period different from the first time period, performing, using the one or more processors, target tissue detection and target tissue segmentation for each the second plurality of 2D medical images to produce a set of segmented target tissue images, and generating, using the one or more processors, a second 3D model of detected and segmented target tissue;
- identifying, using the one or more processors, the set of radiomic features in the second 3D model;
- and
- comparing, using the one or more processors, the set of radiomic features in the first 3D model and the second 3D model and determining a change in the set of radiomic features.
Dependencies are inferred from the claim text.