Distinguishing colon cancer stages based on computationally derived morphological features of cancer nuclei

A non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising:

• accessing a training set comprising a plurality of digital whole slide images (WSIs), wherein each digital WSI of the plurality of digital WSIs comprises an associated tumor and a known associated cancer stage;
• for each digital WSI of the training set:
• segmenting the associated tumor on that digital WSI;
• segmenting associated cancer nuclei in the associated segmented tumor of that digital WSI;
• and
• extracting an associated value for each of a plurality of features from the associated segmented cancer nuclei of that digital WSI;
• determining a set of best features from the plurality of features, based at least in part on the known associated cancer stage for each digital WSI of the training set and on the associated values for each of the plurality of features for each digital WSI of the training set;
• and
• constructing a machine learning model configured to determine an additional cancer stage for an additional digital WSI based at least in part on the set of best features.

An apparatus, comprising:

• memory configured to store at least a portion of a digital whole slide image (WSI) comprising a tumor;
• one or more processors configured to perform operations comprising:
• segmenting the tumor on the at least the portion of the digital WSI;
• segmenting cancer nuclei in the segmented tumor;
• extracting one or more features from the segmented cancer nuclei;
• providing the one or more features extracted from the segmented cancer nuclei to a trained machine learning model;
• and
• receiving, from the machine learning model, an indication of a cancer stage of the tumor.