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Proceedings Paper

Identifying key radiogenomic associations between DCE-MRI and micro-RNA expressions for breast cancer
Author(s): Ravi K. Samala; Heang-Ping Chan; Lubomir Hadjiiski; Mark A. Helvie; Renaid Kim
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Paper Abstract

Understanding the key radiogenomic associations for breast cancer between DCE-MRI and micro-RNA expressions is the foundation for the discovery of radiomic features as biomarkers for assessing tumor progression and prognosis. We conducted a study to analyze the radiogenomic associations for breast cancer using the TCGA-TCIA data set. The core idea that tumor etiology is a function of the behavior of miRNAs is used to build the regression models. The associations based on regression are analyzed for three study outcomes: diagnosis, prognosis, and treatment. The diagnosis group consists of miRNAs associated with clinicopathologic features of breast cancer and significant aberration of expression in breast cancer patients. The prognosis group consists of miRNAs which are closely associated with tumor suppression and regulation of cell proliferation and differentiation. The treatment group consists of miRNAs that contribute significantly to the regulation of metastasis thereby having the potential to be part of therapeutic mechanisms. As a first step, important miRNA expressions were identified and their ability to classify the clinical phenotypes based on the study outcomes was evaluated using the area under the ROC curve (AUC) as a figure-of-merit. The key mapping between the selected miRNAs and radiomic features were determined using least absolute shrinkage and selection operator (LASSO) regression analysis within a two-loop leave-one-out cross-validation strategy. These key associations indicated a number of radiomic features from DCE-MRI to be potential biomarkers for the three study outcomes.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340T (3 March 2017); doi: 10.1117/12.2255512
Show Author Affiliations
Ravi K. Samala, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)
Renaid Kim, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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