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Joint analysis of histopathology image features and gene expression in breast cancer
V. Popovici, E. Budinská, L. Čápková, D. Schwarz, L. Dušek, J. Feit, R. Jaggi,
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články
Grantová podpora
NT14134
MZ0
CEP - Centrální evidence projektů
Digitální knihovna NLK
Plný text - Článek
Zdroj
NLK
BioMedCentral
od 2000-01-12
BioMedCentral Open Access
od 2000
Directory of Open Access Journals
od 2000
Free Medical Journals
od 2000
PubMed Central
od 2000
Europe PubMed Central
od 2000
ProQuest Central
od 2009-01-01
Open Access Digital Library
od 2000-01-01
Open Access Digital Library
od 2000-01-01
Open Access Digital Library
od 2000-07-01
Medline Complete (EBSCOhost)
od 2000-01-01
Health & Medicine (ProQuest)
od 2009-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2000
Springer Nature OA/Free Journals
od 2000-12-01
- MeSH
- genomika metody MeSH
- Kaplanův-Meierův odhad MeSH
- lidé MeSH
- nádory prsu genetika patologie MeSH
- počítačové zpracování obrazu * MeSH
- regulace genové exprese u nádorů * MeSH
- shluková analýza MeSH
- stanovení celkové genové exprese MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
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.
Citace poskytuje Crossref.org
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