Predicting prostate cancer biochemical recurrence using combined nuclear NF-KB/P65 localization and gland morphology

An apparatus for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients, the apparatus comprising:

• a processor;
• a memory configured to store at least one digitized pathology slide of a region of tissue demonstrating PCa;
• 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 a first digitized pathology slide of a region of tissue demonstrating PCa, the first digitized pathology slide having a first stain channel, and
• access a second, different digitized pathology slide of the region of tissue, the second digitized pathology slide having a second, different stain channel;
• a morphology feature extraction circuit configured to:
• extract a set of morphology features from the first stain channel of the first digitized pathology slide, where the set of morphology features includes at least one gland lumen feature and at least one Haralick feature, and
• compute a first probability that the region of tissue will experience BCR based, at least in part, on the set of morphology features;
• a molecular biomarker feature extraction circuit configured to:
• extract a set of stain intensity features from the second stain channel of the second digitized pathology slide, where a stain intensity feature quantifies an amount of a molecular biomarker present in a cellular nucleus represented in the second, different digitized pathology slide, where the molecular biomarker is NF-KB/p65, and
• compute a second probability that the region of tissue will experience BCR based, at least in part, on the set of stain intensity features;
• a BCR recurrence classification circuit configured to compute an aggregate probability that the region of tissue will experience BCR as a function of the first probability and the second probability;
• and
• a display circuit configured to display the aggregate probability and at least one of the first digitized pathology slide, the second digitized pathology slide, the first probability, the second probability, the set of morphology features, or the set of stain intensity features.

A method for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients, the method comprising:

• accessing a first digitized pathology slide of a region of tissue demonstrating PCa, the first digitized pathology slide having a first stain channel;
• accessing a second, different digitized pathology slide of the region of tissue, the second digitized pathology slide having a second, different stain channel;
• extracting a set of morphology features from the first stain channel of the first digitized pathology slide;
• extracting a set of optical density features from the second, different stain channel of the second, different digitized pathology slide, where an optical density feature quantifies an amount of a BCR-predictive molecular biomarker present in a cellular nucleus represented in the second, different digitized pathology slide;
• controlling a first machine learning classifier to generate a first probability of BCR based on the set of morphology features;
• controlling a second, different machine learning classifier to generate a second, different probability of BCR based on the set of optical density features;
• and
• computing an aggregate probability of BCR as a function of the first probability and the second, different probability.