Deep learning-based histological predictions of chromosomal instability in colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) is a lethal malignancy and a leading cause of cancer-related mortality worldwide. Chromosomal instability (CIN) is a key driver of genomic instability in CRC and is characterized by aneuploidy and somatic copy-number alterations. This study aimed to predict CIN in CRC using histological data from whole slide images (WSIs). CRC samples from TCGA were analyzed, with tumor regions segmented into tiles and nuclei for feature extraction using convolutional neural network (CNN) and morphologic analysis. Binary classification models were developed to distinguish high and low aneuploidy scores (AS) based on slide-level features. The analysis included 313 patients with 315 WSIs, resulting in over 350,000 tumor tiles and nearly 2.7 million tumor cell nuclei. The ResNet18-SSL

SUBMITTER: Hyeon D 

PROVIDER: S-EPMC11477831 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature