A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT

Lifeng Yu; Qiyuan Hu; Chi Wan Koo; Edwin A. Takahashi; David L. Levin; Tucker F. Johnson; Megan J. Hora; Shane Dirks; Baiyu Chen; Kyle McMillan; Shuai Leng; J. G. Fletcher; Cynthia H. McCollough

doi:10.1117/12.2255593

9 March 2017 A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT

Lifeng Yu, Qiyuan Hu, Chi Wan Koo, Edwin A. Takahashi, David L. Levin, Tucker F. Johnson, Megan J. Hora, Shane Dirks, Baiyu Chen, Kyle McMillan, Shuai Leng, J. G. Fletcher, Cynthia H. McCollough

Author Affiliations +

Proceedings Volume 10132, Medical Imaging 2017: Physics of Medical Imaging; 101321R (2017) https://doi.org/10.1117/12.2255593
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Task-based image quality assessment using model observers is promising to provide an efficient, quantitative, and objective approach to CT dose optimization. Before this approach can be reliably used in practice, its correlation with radiologist performance for the same clinical task needs to be established. Determining human observer performance for a well-defined clinical task, however, has always been a challenge due to the tremendous amount of efforts needed to collect a large number of positive cases. To overcome this challenge, we developed an accurate projection-based insertion technique. In this study, we present a virtual clinical trial using this tool and a low-dose simulation tool to determine radiologist performance on lung-nodule detection as a function of radiation dose, nodule type, nodule size, and reconstruction methods. The lesion insertion and low-dose simulation tools together were demonstrated to provide flexibility to generate realistically-appearing clinical cases under well-defined conditions. The reader performance data obtained in this virtual clinical trial can be used as the basis to develop model observers for lung nodule detection, as well as for dose and protocol optimization in lung cancer screening CT.

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Lifeng Yu, Qiyuan Hu, Chi Wan Koo, Edwin A. Takahashi, David L. Levin, Tucker F. Johnson, Megan J. Hora, Shane Dirks, Baiyu Chen, Kyle McMillan, Shuai Leng, J. G. Fletcher, and Cynthia H. McCollough "A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101321R (9 March 2017); https://doi.org/10.1117/12.2255593

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