Image-based surrogate biomarkers for molecular subtypes of colorectal cancer

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F17%3A00097868" target="_blank" >RIV/00216224:14310/17:00097868 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1093/bioinformatics/btx027" target="_blank" >http://dx.doi.org/10.1093/bioinformatics/btx027</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/bioinformatics/btx027" target="_blank" >10.1093/bioinformatics/btx027</a>

Result language

angličtina

Original language name

Image-based surrogate biomarkers for molecular subtypes of colorectal cancer

Original language description

Motivation: Whole genome expression profiling of large cohorts of different types of cancer led to the identification of distinct molecular subcategories (subtypes) that may partially explain the observed inter-tumoral heterogeneity. This is also the case of colorectal cancer (CRC) where several such categorizations have been proposed. Despite recent developments, the problem of subtype definition and recognition remains open, one of the causes being the intrinsic heterogeneity of each tumor, which is difficult to estimate from gene expression profiles. However, one of the observations of these studies indicates that there may be links between the dominant tumor morphology characteristics and the molecular subtypes. Benefiting from a large collection of CRC samples, comprising both gene expression and histopathology images, we investigated the possibility of building image-based classifiers able to predict the molecular subtypes. We employed deep convolutional neural networks for extracting local descriptors which were then used for constructing a dictionary-based representation of each tumor sample. A set of support vector machine classifiers were trained to solve different binary decision problems, their combined outputs being used to predict one of the five molecular subtypes. Results: A hierarchical decomposition of the multi-class problem was obtained with an overall accuracy of 0.84 (95% CI = 0.79-0.88). The predictions from the image-based classifier showed significant prognostic value similar to their molecular counterparts.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Bioinformatics

ISSN

1367-4803

e-ISSN

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Volume of the periodical

33

Issue of the periodical within the volume

13

Country of publishing house

GB - UNITED KINGDOM

Number of pages

8

Pages from-to

2002-2009

UT code for WoS article

000404054700013

EID of the result in the Scopus database

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