Predicting prostate cancer risk of progression with multiparametric magnetic resonance imaging using machine learning and peritumoral radiomics
AssigneeCase Western Reserve University
InventorsAnant Madabhushi, Ahmad Algohary, Rakesh Shiradkar
Life of this patent
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Abstract
Embodiments facilitate stratification of a patient according to prostate cancer (PCa) risk. A first set of embodiments relates to training of a machine learning classifier to compute a probability that a patient has a low-risk of PCa progression based on intratumoral radiomic features and peritumoral radiomic features extracted from multi-parametric magnetic resonance imaging (mpMRI) images. A second set of embodiments relates to classifying a patient as low-risk of PCa progression, or high-risk of PCa progression, based on radiomic features extracted from mpMRI imagery of the patient.
Claims24
A non-transitory computer-readable storage device storing computer-executable instructions that when executed cause a processor to perform operations, the operations comprising:
- accessing a multi-parametric magnetic resonance imaging (MRI) image associated with a patient, where the image includes a region of interest (ROI) demonstrating prostate cancer (PCa) pathology, the image having a plurality of pixels, a pixel having an intensity;
- segmenting a tumoral region represented in the image, where segmenting the tumoral region includes defining a tumoral boundary;
- defining a peritumoral region based on the tumoral boundary;
- extracting a set of radiomic features from the image, where the set of radiomic features includes at least one radiomic feature extracted from the peritumoral region, and at least one, different radiomic feature extracted from the tumoral region;
- providing the set of radiomic features to a machine learning classifier trained to distinguish a first class from a second, different class based on the set of radiomic features;
- receiving, from the machine learning classifier, a probability that the ROI is a member of the first class, where the machine learning classifier computes the probability based on the set of radiomic features;
- classifying the ROI as a member of the first class or the second, different class based, at least in part, on the probability;
- and
- displaying the classification.
An apparatus comprising:
- a processor;
- a memory configured to store a bi-parametric magnetic resonance imaging (bpMRI) image associated with a patient, where the image includes a region of interest (ROI) demonstrating prostate cancer (PCa) pathology, the bpMRI image having a plurality of pixels, a pixel having an intensity, the bpMRI image comprising a T2W MRI image and an apparent diffusion coefficient (ADC) map;
- an input/output (I/O) interface;
- a set of circuits;
- and
- an interface that connects the processor, the memory, the I/O interface, and the set of circuits, the set of circuits comprising:
- an image acquisition circuit configured to:
- access the bpMRI image;
- a region definition circuit configured to:
- segment a tumoral region represented in the bpMRI image, where segmenting the tumoral region includes defining a tumoral boundary;
- and
- define a peritumoral region based on the tumoral boundary, where the peritumoral region includes a plurality of annular rings;
- a radiomic feature circuit configured to:
- extract a first set of radiomic features from the bpMRI image, where the first set of radiomic features includes at least one radiomic feature extracted from the peritumoral region, and at least one, different radiomic feature extracted from the tumoral region;
- a PCa classification circuit configured to:
- compute a first probability that the patient associated with the ROI has a low-risk of PCa progression, or a high-risk of PCa progression based on the first set of radiomic features;
- generate a first classification of the patient as having low-risk of PCa progression, or a high-risk of PCa progression based, at least in part, on the probability;
- and
- a display circuit configured to display the first classification.
A non-transitory computer-readable storage device storing computer-executable instructions that when executed cause a processor to perform operations, the operations comprising:
- accessing a training dataset of a plurality of bi-parametric magnetic resonance imaging (bpMRI) images of tissue demonstrating prostate cancer (PCa), where each bpMRI image of the plurality of bpMRI images comprises an associated plurality of pixels, each pixel of the associated plurality of pixels having an associated intensity, where each bpMRI image is associated with a patient, and where a PCa progression risk for each patient is known;
- determining, for each radiomic feature of N radiomic features extracted from each bpMRI image, N being an integer, an associated value for that radiomic feature for each bpMRI image of the training dataset, where the N radiomic features includes at least one tumoral radiomic feature and at least one peritumoral radiomic feature;
- and
- training a quadratic discriminant analysis (QDA) classifier based on the training dataset, the associated values for each radiomic feature of the N radiographic features for each bpMRI image of the training dataset, and the known risk of PCa progression associated with each bpMRI image of the training dataset.
Dependencies are inferred from the claim text.