High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma | Zendy

Michael Lehrer | Zendy; Anindya Bhadra | Zendy; Sathvik Panambur Aithala | Zendy; Visweswaran Ravikumar | Zendy; Youyun Zheng | Zendy; Başak E. Doğan | Zendy; Emerlinda Bonaccio | Zendy; Elizabeth S. Burnside | Zendy; Elizabeth A. Morris | Zendy; Elizabeth J. Sutton | Zendy; Gary J. Whitman | Zendy; Jose Net | Zendy; Kathy R. Brandt | Zendy; Marie A. Ganott | Zendy; Margarita L. Zuley | Zendy; Arvind Rao | Zendy

Open Access

High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma

Author(s) -

Michael Lehrer,

Anindya Bhadra,

Sathvik Panambur Aithala,

Visweswaran Ravikumar,

Youyun Zheng,

Başak E. Doğan,

Emerlinda Bonaccio,

Elizabeth S. Burnside,

Elizabeth A. Morris,

Elizabeth J. Sutton,

Gary J. Whitman,

Jose Net,

Kathy R. Brandt,

Marie A. Ganott,

Margarita L. Zuley,

Arvind Rao

Publication year - 2018

Publication title -

oncoscience

Language(s) - English

Resource type - Journals

ISSN - 2331-4737

DOI - 10.18632/oncoscience.397

Subject(s) - magnetic resonance imaging , computational biology , breast cancer , gene expression profiling , radiogenomics , hierarchical clustering , pathway analysis , gene expression , breast carcinoma , biology , biological pathway , bioinformatics , pathology , medicine , cluster analysis , cancer , gene , artificial intelligence , computer science , genetics , radiology , radiomics

Imaging features derived from MRI scans can be used for not only breast cancer detection and measuring disease extent, but can also determine gene expression and patient outcomes. The relationships between imaging features, gene/protein expression, and response to therapy hold potential to guide personalized medicine. We aim to characterize the relationship between radiologist-annotated tumor phenotypic features (based on MRI) and the underlying biological processes (based on proteomic profiling) in the tumor.

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