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Proceedings Paper

Automated assessment of imaging biomarkers for the PanCan lung cancer risk prediction model with validation on NLST data
Author(s): Rafael Wiemker; Merlijn Sevenster; Heber MacMahon; Feng Li; Sandeep Dalal; Amir Tahmasebi; Tobias Klinder
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Paper Abstract

The imaging biomarkers EmphysemaPresence and NoduleSpiculation are crucial inputs for most models aiming to predict the risk of indeterminate pulmonary nodules detected at CT screening. To increase reproducibility and to accelerate screening workflow it is desirable to assess these biomarkers automatically. Validation on NLST images indicates that standard histogram measures are not sufficient to assess EmphysemaPresence in screenees. However, automatic scoring of bulla-resembling low attenuation areas can achieve agreement with experts with close to 80% sensitivity and specificity. NoduleSpiculation can be automatically assessed with similar accuracy. We find a dedicated spiculi tracing score to slightly outperform generic combinations of texture features with classifiers.

Paper Details

Date Published: 3 March 2017
PDF: 10 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013421 (3 March 2017); doi: 10.1117/12.2253905
Show Author Affiliations
Rafael Wiemker, Philips Research Labs. (Germany)
Merlijn Sevenster, Philips Research Labs. (United States)
Heber MacMahon, The Univ. of Chicago (United States)
Feng Li, The Univ. of Chicago (United States)
Sandeep Dalal, Philips Research Labs. (United States)
Amir Tahmasebi, Philips Research Labs. (United States)
Tobias Klinder, Philips Research Labs. (Germany)


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

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