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Proceedings Paper

Radiomics-based quantitative biomarker discovery: development of a robust image processing infrastructure
Author(s): Darryl H. Hwang; Bino A. Varghese; Michael Chang; Christopher Deng; Chidubem Ugweze; Steven Y. Cen; Vinay A. Duddalwar
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Paper Abstract

Radiomics workflows are high-throughput disease descriptive or predictive tools that extract mineable quantitative data of pathological phenotypes from standard-of-care grayscale images using advanced image processing algorithms. The success of these workflows rely on establishing large image datasets from which diverse disease descriptors can be extracted, with the expectation that large numbers may be able to overcome some of the inherent heterogeneities inherent in standard-of-care medical imaging workflows. Here, we present such a radiomics platform which relies on a combination of existing standard-of-care imaging clinical and research software as well as custom written code. The key components of the workflow include a file organization schema for centralized data storage, deployment of image registration strategies, and frontend GUI design for ease of use by the clinical researcher, all of which increase the transparency, flexibility, and portability of our radiomics platform. Widespread establishment of such radiomics platform can greatly revolutionize radiomics research and aid in successful translation into clinical decision support systems.

Presented are three preliminary studies completed using our proposed radiomics research workflow to investigate various diseases. The radiomics research workflow is modality and disease independent which allow it to serve as a general platform for medical image post-processing experimentation.

Paper Details

Date Published: 26 January 2017
PDF: 6 pages
Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 1016017 (26 January 2017); doi: 10.1117/12.2256829
Show Author Affiliations
Darryl H. Hwang, The Univ. of Southern California (United States)
Bino A. Varghese, The Univ. of Southern California (United States)
Michael Chang, The Univ. of Southern California (United States)
Christopher Deng, The Univ. of Southern California (United States)
Chidubem Ugweze, The Univ. of Southern California (United States)
Steven Y. Cen, The Univ. of Southern California (United States)
Vinay A. Duddalwar, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 10160:
12th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Jorge Brieva; Ignacio Larrabide; , Editor(s)

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