| POPOVICI, Vlad, Eva BUDINSKÁ, Lenka ČÁPKOVÁ, Daniel SCHWARZ, Ladislav DUŠEK, Josef FEIT and Rolf JAGGI. Joint analysis of histopathology image features and gene expression in breast cancer. BMC Bioinformatics. London: BioMed Central, 2016, vol. 17, No 209, p. 1-9. ISSN 1471-2105. Available from: https://dx.doi.org/10.1186/s12859-016-1072-z. |
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@article{1365508,
author = {Popovici, Vlad and Budinská, Eva and Čápková, Lenka and Schwarz, Daniel and Dušek, Ladislav and Feit, Josef and Jaggi, Rolf},
article_location = {London},
article_number = {209},
doi = {http://dx.doi.org/10.1186/s12859-016-1072-z},
keywords = {Histopathology images; Image analysis; Biomarker discovery; Gene expression; Multimodal data mining},
language = {eng},
issn = {1471-2105},
journal = {BMC Bioinformatics},
title = {Joint analysis of histopathology image features and gene expression in breast cancer},
volume = {17},
year = {2016}
}
TY - JOUR
ID - 1365508
AU - Popovici, Vlad - Budinská, Eva - Čápková, Lenka - Schwarz, Daniel - Dušek, Ladislav - Feit, Josef - Jaggi, Rolf
PY - 2016
TI - Joint analysis of histopathology image features and gene expression in breast cancer
JF - BMC Bioinformatics
VL - 17
IS - 209
SP - 1-9
EP - 1-9
PB - BioMed Central
SN - 14712105
KW - Histopathology images
KW - Image analysis
KW - Biomarker discovery
KW - Gene expression
KW - Multimodal data mining
N2 - Background: Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images. Results: We developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach - a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available. Conclusions: The framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.
ER -
POPOVICI, Vlad, Eva BUDINSKÁ, Lenka ČÁPKOVÁ, Daniel SCHWARZ, Ladislav DUŠEK, Josef FEIT and Rolf JAGGI. Joint analysis of histopathology image features and gene expression in breast cancer. \textit{BMC Bioinformatics}. London: BioMed Central, 2016, vol.~17, No~209, p.~1-9. ISSN~1471-2105. Available from: https://dx.doi.org/10.1186/s12859-016-1072-z.
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