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Proceedings Paper

General purpose radiomics for multi-modal clinical research
Author(s): Michael G. Wels; Félix Lades; Alexander Muehlberg; Michael Suehling
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Paper Abstract

In this paper we present an integrated software solution targeting clinical researchers for discovering relevant radiomic biomarkers covering the entire value chain of clinical radiomics research. Its intention is to make this kind of research possible even for less experienced scientists. The solution provides means to create, collect, manage, and statistically analyze patient cohorts consisting of potentially multimodal 3D medical imaging data, associated volume of interest annotations, and radiomic features. Volumes of interest can be created by an extensive set of semi-automatic segmentation tools. Radiomic feature computation relies on the de facto standard library PyRadiomics and ensures comparability and reproducibility of carried out studies. Tabular cohort studies containing the radiomics of the volumes of interest can be managed directly within the software solution. The integrated statistical analysis capabilities introduce an additional layer of abstraction allowing non-experts to benefit from radiomics research as well. There are ready-to-use methods for clustering, uni- and multivariate statistics, and machine learning to be applied to the collected cohorts. They are validated in two case studies: for one thing, on a subset of the publicly available NSCLC-Radiomics data collection containing pretreatment CT scans of 317 non-small cell lung cancer (NSCLC) patients and for another, on the Lung Image Database Consortium imaging study with diagnostic and lung cancer screening CT scans including 2,753 distinct lesions from 870 patients. Integrated software solutions with optimized workflows like the one presented and further developments thereof may play an important role in making precision medicine come to life in clinical environments.

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095046 (13 March 2019); doi: 10.1117/12.2511856
Show Author Affiliations
Michael G. Wels, Siemens Healthineers (Germany)
Félix Lades, Siemens Healthineers (Germany)
Alexander Muehlberg, Siemens Healthineers (Germany)
Michael Suehling, Siemens Healthineers (Germany)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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