Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2017, Article ID 3926498, 7 pages
https://doi.org/10.1155/2017/3926498
Research Article

Identification of “BRAF-Positive” Cases Based on Whole-Slide Image Analysis

1Institute of Biostatistics and Analyses, Faculty of Medicine and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masarykova Univerzita, Kamenice 5, 625 00 Brno, Czech Republic
2Institute of Computer Science, Masarykova Univerzita, Šumavská 15, 602 00 Brno, Czech Republic
3Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masarykova Univerzita, Kamenice 5, 625 00 Brno, Czech Republic

Correspondence should be addressed to Vlad Popovici; zc.inum.abi@icivopop

Received 11 November 2016; Accepted 20 March 2017; Published 24 April 2017

Academic Editor: Xudong Huang

Copyright © 2017 Vlad Popovici et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A key requirement for precision medicine is the accurate identification of patients that would respond to a specific treatment or those that represent a high-risk group, and a plethora of molecular biomarkers have been proposed for this purpose during the last decade. Their application in clinical settings, however, is not always straightforward due to relatively high costs of some tests, limited availability of the biological material and time, and procedural constraints. Hence, there is an increasing interest in constructing tissue-based surrogate biomarkers that could be applied with minimal overhead directly to histopathology images and which could be used for guiding the selection of eventual further molecular tests. In the context of colorectal cancer, we present a method for constructing a surrogate biomarker that is able to predict with high accuracy whether a sample belongs to the “BRAF-positive” group, a high-risk group comprising V600E BRAF mutants and BRAF-mutant-like tumors. Our model is trained to mimic the predictions of a 64-gene signature, the current definition of BRAF-positive group, thus effectively identifying histopathology image features that can be linked to a molecular score. Since the only required input is the routine histopathology image, the model can easily be integrated in the diagnostic workflow.