Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.

ABSTRACT:

Background and objectives

Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon cancer that is, captured by these features, whereas microsatellite instability (MSI) is an established biomarker traditionally assessed by immunohistochemistry or polymerase chain reaction.

Methods

H&E-stained slides from The Cancer Genome Atlas (TCGA) colon cohort are passed through the CNN. Resulting imaging features are used to cluster morphologically similar slide regions. Tile-level pairwise similarities are calculated and used to generate a tumor heterogeneity score (THS). Patient-level THS is then correlated with TCGA-report

SUBMITTER: Rubinstein JC 

PROVIDER: S-EPMC11446314 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature