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

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.

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

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• 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
• Tags: NZ, rivok
• Tags: International impact, Reviewed

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.

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