Predicting prostate cancer recurrence in pre-treatment prostate magnetic resonance imaging (MRI) with combined tumor induced organ distension and tumor radiomics

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 prostate cancer (PCa) recurrence, the method comprising:

• accessing a first pre-treatment radiological image of a region of tissue demonstrating PCa;
• accessing a second pre-treatment radiological image of the region of tissue;
• generating a first segmented prostate by automatically segmenting a prostate capsule represented in the first pre-treatment radiological image;
• generating a second segmented prostate by automatically segmenting the prostate capsule represented in the first pre-treatment radiological image;
• generating a registered segmented prostate by registering the first segmented prostate with a surface of interest (SOI) mask;
• generating a patient-specific SOI mesh from registered segmented prostate;
• extracting a set of distension features from the patient-specific SOI mesh;
• providing a first machine learning classifier the set of distension features;
• extracting a set of radiomic features from the second segmented prostate;
• providing a second machine learning classifier the set of radiomic features;
• receiving, from the first machine learning classifier, a first probability that the region of tissue will experience PCa recurrence based, at least in part, on the set of distension features;
• receiving, from the second machine learning classifier, a second probability that the region of tissue will experience PCa recurrence based, at least in part, on the set of radiomic features;
• computing a combined probability that the region of tissue will experience PCa recurrence based on the first probability and the second probability;
• upon detecting that the combined probability is greater than a threshold probability:
• classifying the region of tissue as likely to experience PCa recurrence;
• upon detecting that the combined probability is less than or equal to the threshold probability:
• classifying the region of tissue as unlikely to experience PCa recurrence;
• displaying the classification and at least one of the combined probability, the first probability, the second probability, the set of radiomics features, the set of distension features, the first pre-treatment radiological image, or the second pre-treatment radiological image.

A non-transitory computer-readable storage device storing instructions that when executed by a processor control the processor to perform operations, the operations including:

• accessing a first pre-treatment image of a region of tissue demonstrating prostate cancer (PCa), where the first pre-treatment image includes a segmented prostate capsule;
• accessing a second pre-treatment image of the region of tissue, where the second pre-treatment image includes the segmented prostate capsule;
• generating a registered segmented prostate capsule by registering the segmented prostate capsule represented in the first pre-treatment image to a recurrence negative (C−) median template;
• registering a surface of interest (SOI) mask to the registered segmented prostate capsule;
• generating a patient-specific SOI mask from the registered SOI mask;
• generating a patient-specific SOI mesh from the patient-specific SOI mask;
• extracting a set of organ distension features from the patient-specific mesh, where the set of organ distension features a Gaussian curvature (θ) feature, a surface normal orientation (Φ) feature represented in a spherical coordinate system, and a θ kurtosis feature, a Φ skewness feature, a Φ standard deviation feature, and a Φ mean feature computed from the θ feature and the Φ feature;
• computing a first probability that the region of tissue will experience PCa recurrence based, at least in part, on the set of organ distension features;
• extracting a set of radiomic features from the segmented prostate capsule represented in the second pre-treatment image, where the set of radiomic features includes a subset of first order statistical features, a subset of Haralick features, and a subset of Gabor features;
• computing a second probability that the region of tissue will experience PCa recurrence based, at least in part, on the set of radiomics features;
• computing a joint-probability that the region of tissue will experience PCa recurrence based on the first probability and the second probability;
• classifying the region of tissue as likely to experience biochemical recurrence (BCR) or unlikely to experience BCR based, at least in part, on the joint probability;
• and
• displaying the classification, the joint probability, the set of organ distension features, the set of radiomic features, the patient-specific SOI mesh, the patient-specific SOI mask, the first pre-treatment image, or the second pre-treatment image.