Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning.

ABSTRACT: Breast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutations related to targeted therapies are identified by genomic analysis like high-throughput molecular profiling. In this study, we develop a deep-learning model to predict the genetic mutations and biological pathway activities directly from WSIs. Our study offers unique insights into WSI visual interactions between mutation and its related pathway, enabling a head-to-head comparison to reinforce our major findings. Using the histopathology images from the Genomic Data Commons Database, our model can pred

SUBMITTER: Qu H 

PROVIDER: S-EPMC8460699 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature