Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation images

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 post-transplant bone marrow aspirate from an associated patient that has acute myeloid leukemia (AML), wherein each digital WSI of the plurality of digital WSIs has a known associated outcome for the associated patient of that digital WSI, wherein the known associated outcome is one of a relapse or a non-relapse;
• for each digital WSI of the training set:
• segmenting one or more associated myeloblasts on 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 outcome 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 associated outcome 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 post-transplant bone marrow aspirate from a patient that has acute myeloid leukemia (AML);
• one or more processors configured to perform operations comprising:
• segmenting one or more myeloblasts on the digital WSI;
• extracting one or more features from the segmented one or more myeloblasts;
• providing the one or more features extracted from the segmented one or more myeloblasts to a trained machine learning model;
• and
• receiving, from the trained machine learning model, an indication of a risk of relapse of the AML.