Predicting immunotherapy response in non-small cell lung cancer with serial quantitative vessel tortuosity

A non-transitory computer-readable storage device storing computer-executable instructions that when executed by a computer control the computer to perform a method for predicting non-small cell lung cancer (NSCLC) patient response to immunotherapy, the method comprising:

• accessing a pre-treatment image of a region of tissue in a patient demonstrating NSCLC, where the pre-treatment image includes a plurality of voxels;
• annotating a tumor represented in the pre-treatment image;
• generating a pre-treatment three dimensional (3D) segmented vasculature by segmenting a vessel associated with the tumor represented in the pre-treatment image from the tumor represented in the pre-treatment image;
• identifying a center line of the pre-treatment 3D segmented vasculature;
• accessing at least one post-treatment image of the region of tissue demonstrating NSCLC, where the at least one post-treatment image includes the tumor, and where the post-treatment image includes a plurality of voxels;
• annotating the tumor represented in the at least one post-treatment image;
• generating a post-treatment 3D segmented vasculature by segmenting a vessel associated with the tumor represented in the at least one post-treatment image from the tumor represented in the at least one post-treatment image;
• identifying a center line of the post-treatment 3D segmented vasculature;
• generating a registered image by registering the pre-treatment image with the at least one post-treatment image;
• extracting a set of pre-treatment quantitative vessel tortuosity (QVT) features from the registered image based, at least in part on the center line of the pre-treatment 3D segmented vasculature;
• extracting a set of post-treatment QVT features from the registered image based, at least in part on the center line of the post-treatment 3D segmented vasculature;
• generating a set of delta-QVT features by computing, on a per-voxel basis, a difference between the set post-treatment QVT features and the set of pre-treatment QVT features;
• providing the set of delta-QVT features to a machine learning classifier;
• receiving, from the machine learning classifier, a probability that the region of tissue will respond to immunotherapy based, at least in part, on the set of delta-QVT features;
• classifying the region of tissue as a responder or non-responder based, at least in part, on the probability;
• and
• generating an NSCLC immunotherapy treatment plan based, at least in part, on the classification and at least one of the probability, the set of delta-QVT features, the pre-treatment image, the at least one post-treatment image, or the registered image.

A computer-readable storage device storing computer-executable instructions that, in response to execution, cause an immunotherapy prediction response system to perform operations comprising:

• accessing a pre-treatment radiological image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), where the pre-treatment radiological image includes a tumor and a vasculature associated with the tumor, where the pre-treatment radiological image includes a plurality of voxels;
• accessing at least one post-treatment radiological image of the region of tissue demonstrating NSCLC, where the at least one post-treatment radiological image includes the tumor and the vasculature, where the post-treatment radiological image includes a plurality of voxels;
• generating a registered image by registering the pre-treatment radiological image with the at least one post-treatment radiological image;
• extracting a set of pre-treatment quantitative vessel tortuosity (QVT) features from the vasculature represented in the registered image;
• extracting a set of post-treatment QVT features from the vasculature represented in the registered image;
• generating 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, where a member of the set of delta-QVT features has a threshold level of stability;
• providing the set of delta-QVT features to a machine learning classifier;
• receiving, from the machine learning classifier, a probability that the region of tissue will respond to immunotherapy based, at least in part, on the set of delta-QVT features;
• classifying the region of tissue as a responder or non-responder based, at least in part, on the probability;
• and
• generating an NSCLC immunotherapy treatment plan based, at least in part, on the classification and at least one of the probability, the set of delta-QVT features, the pre-treatment radiological image, or the at least one post-treatment radiological image.

A computer-readable storage device storing computer-executable instructions that, in response to execution, cause an immunotherapy prediction response system to perform operations comprising:

• accessing a registered radiological image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), where the registered radiological image includes a pre-treatment radiological image of the region of tissue registered with a post-immunotherapy radiological image of the region of tissue, where the registered radiological image includes a tumor and a vasculature associated with the tumor, where the registered radiological image includes a plurality of voxels;
• extracting a set of pre-treatment quantitative vessel tortuosity (QVT) features from the vasculature represented in the registered radiological image;
• extracting a set of post-treatment QVT features from the vasculature represented in the registered radiological image;
• generating 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, where a member of the set of delta-QVT features has a threshold level of stability;
• computing a probability that the region of tissue will respond to immunotherapy based, at least in part, on the set of delta-QVT features;
• classifying the region of tissue as a responder or non-responder based, at least in part, on the probability;
• and
• generating an NSCLC immunotherapy treatment plan based, at least in part, on the classification and at least one of the probability, the set of delta-QVT features, or the registered radiological image.