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Research Area: Machine Learning
Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning.
Keywords:
Consensus Molecular Subtype
Colorectal Cancer
Deep Learning
CRC
CMS
Author(s) Name: Korsuk Sirinukunwattana,Enric Domingo, Susan D Richman, Keara L Redmond, Andrew Blake, Clare Verrill
Journal name: machine learning
Conferrence name:
Publisher name: BMJ Journals
DOI: 10.1136/gutjnl-2019-319866
Volume Information: Volume 70
Paper Link: https://gut.bmj.com/content/70/3/544.abstract
