Assessing risk of breast cancer recurrence

A system comprising:

• one or more processors;
• and
• a memory coupled to the processor, the memory storing computer readable instructions that, when executed by the processor, cause the one or more processors to perform operations comprising:
• accessing a first image depicting at least part of a training tissue sample stained with a first type of stain;
• accessing a second image depicting at least part of the training tissue sample stained with a second type of stain;
• identifying, for the first image, a first plurality of marker-specific features;
• identifying, for the second image, a second plurality of marker-specific features, wherein each marker-specific feature of the first or second plurality of marker-specific features includes a value that identifies one or more characteristics of a biological feature of the training tissue sample;
• applying a machine-learning model to one or more marker-specific features of the first plurality of marker-specific features and one or more marker-specific features of the second plurality of marker-specific features to determine a set of prognostic marker-specific features of the training tissue sample;
• computing a plurality of inter-marker features of the first and second images, wherein each inter-marker feature of the plurality of inter-marker features is computed based on a first marker-specific feature of the first plurality of marker-specific features and a second marker-specific feature of the second plurality of marker-specific features;
• applying the machine-learning model to one or more inter-marker features of the plurality of inter-marker features to determine a set of prognostic inter-marker features of the training tissue sample;
• training a breast-cancer recurrence model using the set of prognostic marker-specific features and the set of prognostic inter-marker features, wherein the breast-cancer recurrence model is trained to process another image to predict a breast-cancer recurrence risk level for a particular subject;
• and
• outputting the trained breast-cancer recurrence model.

A computer program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform operations comprising:

• accessing a first image depicting at least part of a training tissue sample stained with a first type of stain;
• accessing a second image depicting at least part of the training tissue sample stained with a second type of stain;
• identifying, for the first image, a first plurality of marker-specific features;
• identifying, for the second image, a second plurality of marker-specific features, wherein each marker-specific feature of the first or second plurality of marker-specific features includes a value that identifies one or more characteristics of a biological feature of the training tissue sample;
• applying a machine-learning model to one or more marker-specific features of the first plurality of marker-specific features and one or more marker-specific features of the second plurality of marker-specific features to determine a set of prognostic marker-specific features of the training tissue sample;
• computing a plurality of inter-marker features of the first and second images, wherein each inter-marker feature of the plurality of inter-marker features is computed based on a first marker-specific feature of the first plurality of marker-specific features and a second marker-specific feature of the second plurality of marker-specific features;
• applying the machine-learning model to one or more inter-marker features of the plurality of inter-marker features to determine a set of prognostic inter-marker features of the training tissue sample;
• training a breast-cancer recurrence model using the set of prognostic marker-specific features and the set of prognostic inter-marker features, wherein the breast-cancer recurrence model is trained to process another image to predict a breast-cancer recurrence risk level for a particular subject;
• and
• outputting the trained breast-cancer recurrence model.