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US 12,711,620 B2Granted
Cancer mapping using machine learning
AssigneeAvenda Health, Inc.
InventorsShyam Natarajan, Alan Martin Priester, Joshua Dylan Shubert, Jeremy Tet Kong Bong, Brittany Nan Pusey
Life of this patent
Estimating the term…
Abstract
A method for mapping cancer includes inputting data elements from medical images, biopsy, and biopsy pathology labels into a machine learning model estimating the likelihood of clinically significant cancer in a patient and outputting, via the machine learning model, an estimate of the clinically significant cancer likelihood at each voxel of a three-dimensional image.
Claims19
A device for mapping cancer, comprising:
- a processor electrically coupled to a memory component storing electronic instructions that, when executed by the processor, cause the device to execute a machine learning algorithm configured to receive inputs and produce an output based on the inputs, wherein:
- the inputs comprise data elements from a medical image of a patient;
- and
- the output comprises:
- an estimate of clinically significant cancer likelihood in the patient;
- at each voxel of a three-dimensional image;
- a lesion contour representing a lesion size of a cancer lesion in the patient;
- and an encapsulation confidence score representing an estimated likelihood that the lesion contour encompasses all clinically significant prostate cancer in the patient.
5 more elements
A method for mapping cancer, comprising:
- inputting data elements from medical images of a patient into a machine learning model estimating a likelihood of clinically significant cancer in a patient;
- and
- generating an output, via the machine learning model, including an estimate of the clinically significant cancer likelihood at each voxel of a three-dimensional image, the output further comprising:
- an adjustable lesion contour representing at least one of a size of a cancer lesion or a margin determination for the cancer lesion in the patient;
- and
- an encapsulation confidence score representing a probability of all clinically significant prostate cancer in the patient being within a specified lesion contour.
3 more elements
A method for mapping cancer, comprising:
- inputting data elements from medical images of a patient into a machine learning model estimating a likelihood of clinically significant cancer in the patient;
- and
- displaying a visual representation of the likelihood at each voxel of a three-dimensional image, the visual representation comprising:
- a cancer estimation map (CEM) illustrating a color-coded heat map representing the likelihood of clinically significant cancer overlying the image, the CEM including a lesion contour representing a size of a cancer lesion;
- and
- a curve representing an encapsulation confidence score versus the lesion size, the curve including a point representing the lesion size and the encapsulation confidence score;
- wherein:
- the point is configured to be visually manipulated along the curve to change the lesion size and the encapsulation confidence score represented by the point;
- and
- manipulating the point alters the lesion contour.
7 more elements
Dependencies are inferred from the claim text.