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. doi:10.1186/s12859-016-1072-z.
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Basic information
Original name Joint analysis of histopathology image features and gene expression in breast cancer
Authors POPOVICI, Vlad (642 Romania, guarantor, belonging to the institution), Eva BUDINSKÁ (703 Slovakia, belonging to the institution), Lenka ČÁPKOVÁ (203 Czech Republic, belonging to the institution), Daniel SCHWARZ (203 Czech Republic, belonging to the institution), Ladislav DUŠEK (203 Czech Republic, belonging to the institution), Josef FEIT (203 Czech Republic, belonging to the institution) and Rolf JAGGI (756 Switzerland).
Edition BMC Bioinformatics, London, BioMed Central, 2016, 1471-2105.
Other information
Original language English
Type of outcome article in a journal
Field of Study 30200 3.2 Clinical medicine
Country of publisher United Kingdom
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 2.448
RIV identification code RIV/00216224:14110/16:00088933
Organization unit Faculty of Medicine
Doi http://dx.doi.org/10.1186/s12859-016-1072-z
UT WoS 000375849000001
Keywords in English Histopathology images; Image analysis; Biomarker discovery; Gene expression; Multimodal data mining
Tags EL OK, podil
Tags International impact, Reviewed
Changed by Changed by: Ing. Mgr. Věra Pospíšilíková, učo 9005. Changed: 19/12/2016 17:58.
Abstract
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.
Links
NT14134, research and development projectName: Integrativní vývoj multimodálního rizikového skóre pro odhad relapsu u pacientek s karcinomem prsu.
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