Clinical actionability of triaging DNA mismatch repair deficient colorectal cancer from biopsy samples using deep learning.

ABSTRACT:

Background

We aimed to develop a deep learning (DL) model to predict DNA mismatch repair (MMR) status in colorectal cancers (CRC) based on hematoxylin and eosin-stained whole-slide images (WSIs) and assess its clinical applicability.

Methods

The DL model was developed and validated through three-fold cross validation using 441 WSIs from the Cancer Genome Atlas (TCGA) and externally validated using 78 WSIs from the Pathology AI Platform (PAIP), and 355 WSIs from surgical specimens and 341 WSIs from biopsy specimens of the Sun Yet-sun University Cancer Center (SYSUCC). Domain adaption and multiple instance learning (MIL) techniques were adopted for model development. The performance of the models was evaluated using the area under the receiver operating characteristic curve (A

SUBMITTER: Jiang W 

PROVIDER: S-EPMC9240789 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature