Other formats:
BibTeX
LaTeX
RIS
@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.
|