Identification of

J 2017

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

POPOVICI, Vlad, Aleš KŘENEK and Eva BUDINSKÁ

Basic information

Original name

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

Authors

POPOVICI, Vlad (642 Romania, guarantor, belonging to the institution), Aleš KŘENEK (203 Czech Republic, belonging to the institution) and Eva BUDINSKÁ (703 Slovakia, belonging to the institution)

Edition

Biomed Research International, New York, HINDAWI LTD, 2017, 2314-6133

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Impact factor

Impact factor: 2.583

RIV identification code

RIV/00216224:14310/17:00097871

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1155/2017/3926498

UT WoS

000399970000001

Keywords in English

digital pathology; bioinformatics; BRAF signature

Abstract

V originále

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.

Links

LM2015085, research and development project

Name: CERIT Scientific Cloud (Acronym: CERIT-SC)

Investor: Ministry of Education, Youth and Sports of the CR, CERIT Scientific Cloud

4SGA8736, interní kód MU

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

Investor: South-Moravian Region, Incoming grants

Citovat

POPOVICI, Vlad, Aleš KŘENEK and Eva BUDINSKÁ. Identification of "BRAF-Positive" Cases Based on Whole-Slide Image Analysis. Biomed Research International. New York: HINDAWI LTD, 2017, Neuveden, April, p. nestránkováno, 7 pp. ISSN 2314-6133. Available from: https://dx.doi.org/10.1155/2017/3926498.

@article{1392207,
   author = {Popovici, Vlad and Křenek, Aleš and Budinská, Eva},
   article_location = {New York},
   article_number = {April},
   doi = {http://dx.doi.org/10.1155/2017/3926498},
   keywords = {digital pathology; bioinformatics; BRAF signature},
   language = {eng},
   issn = {2314-6133},
   journal = {Biomed Research International},
   title = {Identification of "BRAF-Positive" Cases Based on Whole-Slide Image Analysis},
   volume = {Neuveden},
   year = {2017}
}

TY  - JOUR
ID  - 1392207
AU  - Popovici, Vlad - Křenek, Aleš - Budinská, Eva
PY  - 2017
TI  - Identification of "BRAF-Positive" Cases Based on Whole-Slide Image Analysis
JF  - Biomed Research International
VL  - Neuveden
IS  - April
SP  - nestránkováno
EP  - nestránkováno
PB  - HINDAWI LTD
SN  - 23146133
KW  - digital pathology
KW  - bioinformatics
KW  - BRAF signature
N2  - 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.
ER  -

POPOVICI, Vlad, Aleš KŘENEK and Eva BUDINSKÁ. Identification of ''BRAF-Positive'' Cases Based on Whole-Slide Image Analysis. \textit{Biomed Research International}. New York: HINDAWI LTD, 2017, Neuveden, April, p.~nestránkováno, 7 pp. ISSN~2314-6133. Available from: https://dx.doi.org/10.1155/2017/3926498.

Detailed Information on Publication Record