Image-based surrogate biomarkers for molecular subtypes of
colorectal cancer

J 2017

Image-based surrogate biomarkers for molecular subtypes of colorectal cancer

POPOVICI, Vlad, Eva BUDINSKÁ, Ladislav DUŠEK, Michal KOZUBEK, Fred BOSMAN et. al.

Základní údaje

Originální název

Image-based surrogate biomarkers for molecular subtypes of colorectal cancer

Autoři

POPOVICI, Vlad (642 Rumunsko, garant, domácí), Eva BUDINSKÁ (703 Slovensko, domácí), Ladislav DUŠEK (203 Česká republika, domácí), Michal KOZUBEK (203 Česká republika, domácí) a Fred BOSMAN (528 Nizozemské království)

Vydání

Bioinformatics, Oxford, Oxford University Press, 2017, 1367-4803

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 5.481

Kód RIV

RIV/00216224:14310/17:00097868

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1093/bioinformatics/btx027

UT WoS

000404054700013

Klíčová slova anglicky

digital pathology; bioinformatics; molecular subtypes

Štítky

cbia-web, NZ, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 5. 4. 2018 11:32, Ing. Nicole Zrilić

Anotace

V originále

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.

Návaznosti

4SGA8736, interní kód MU

Název: Computational framework for joint analysis of histopathology images and gene expression data (Akronym: HIGEX)

Investor: Jihomoravský kraj, Computational framework for joint analysis of histopathology images and gene expression data, Granty pro zahraniční vědce

Citovat

POPOVICI, Vlad, Eva BUDINSKÁ, Ladislav DUŠEK, Michal KOZUBEK a Fred BOSMAN. Image-based surrogate biomarkers for molecular subtypes of colorectal cancer. Bioinformatics. Oxford: Oxford University Press, 2017, roč. 33, č. 13, s. 2002-2009. ISSN 1367-4803. Dostupné z: https://dx.doi.org/10.1093/bioinformatics/btx027.

@article{1392199,
   author = {Popovici, Vlad and Budinská, Eva and Dušek, Ladislav and Kozubek, Michal and Bosman, Fred},
   article_location = {Oxford},
   article_number = {13},
   doi = {http://dx.doi.org/10.1093/bioinformatics/btx027},
   keywords = {digital pathology; bioinformatics; molecular subtypes},
   language = {eng},
   issn = {1367-4803},
   journal = {Bioinformatics},
   title = {Image-based surrogate biomarkers for molecular subtypes of colorectal cancer},
   volume = {33},
   year = {2017}
}

TY  - JOUR
ID  - 1392199
AU  - Popovici, Vlad - Budinská, Eva - Dušek, Ladislav - Kozubek, Michal - Bosman, Fred
PY  - 2017
TI  - Image-based surrogate biomarkers for molecular subtypes of colorectal cancer
JF  - Bioinformatics
VL  - 33
IS  - 13
SP  - 2002-2009
EP  - 2002-2009
PB  - Oxford University Press
SN  - 13674803
KW  - digital pathology
KW  - bioinformatics
KW  - molecular subtypes
N2  - 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.
ER  -

POPOVICI, Vlad, Eva BUDINSKÁ, Ladislav DUŠEK, Michal KOZUBEK a Fred BOSMAN. Image-based surrogate biomarkers for molecular subtypes of colorectal cancer. \textit{Bioinformatics}. Oxford: Oxford University Press, 2017, roč.~33, č.~13, s.~2002-2009. ISSN~1367-4803. Dostupné z: https://dx.doi.org/10.1093/bioinformatics/btx027.

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