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

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

Základní údaje

• Originální název: Identification of "BRAF-Positive" Cases Based on Whole-Slide Image Analysis
• Autoři: POPOVICI, Vlad (642 Rumunsko, garant, domácí), Aleš KŘENEK (203 Česká republika, domácí) a Eva BUDINSKÁ (703 Slovensko, domácí).
• Vydání: Biomed Research International, New York, HINDAWI LTD, 2017, 2314-6133.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Článek v odborném periodiku
• Obor: 10201 Computer sciences, information science, bioinformatics
• Stát vydavatele: Spojené státy
• Utajení: není předmětem státního či obchodního tajemství
• Impakt faktor: Impact factor: 2.583
• Kód RIV: RIV/00216224:14310/17:00097871
• Organizační jednotka: Přírodovědecká fakulta
• Doi: http://dx.doi.org/10.1155/2017/3926498
• UT WoS: 000399970000001
• Klíčová slova anglicky: digital pathology; bioinformatics; BRAF signature
• Štítky: NZ, rivok
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.

Návaznosti

• LM2015085, projekt VaV: Název: CERIT Scientific Cloud (Akronym: CERIT-SC)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CERIT Scientific Cloud

• 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