Automated assessment of imaging biomarkers for the PanCan lung cancer risk prediction model with validation on NLST data

Rafael Wiemker; Merlijn Sevenster; Heber MacMahon; Feng Li; Sandeep Dalal; Amir Tahmasebi; Tobias Klinder

doi:10.1117/12.2253905

3 March 2017 Automated assessment of imaging biomarkers for the PanCan lung cancer risk prediction model with validation on NLST data

Rafael Wiemker, Merlijn Sevenster, Heber MacMahon, Feng Li, Sandeep Dalal, Amir Tahmasebi, Tobias Klinder

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013421 (2017) https://doi.org/10.1117/12.2253905
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

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Rafael Wiemker, Merlijn Sevenster, Heber MacMahon, Feng Li, Sandeep Dalal, Amir Tahmasebi, and Tobias Klinder "Automated assessment of imaging biomarkers for the PanCan lung cancer risk prediction model with validation on NLST data", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013421 (3 March 2017); https://doi.org/10.1117/12.2253905

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