Exploring the interplay between colorectal cancer subtypes genomic variants and cellular morphology: A deep-learning approach.

ABSTRACT: Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications

SUBMITTER: Hezi H 

PROVIDER: S-EPMC11386451 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature