Abstract #4349

Markov Model of Lung Cancer Screening Demonstrates Equivalent Lung Cancer Detection using either Lung MRI or Low-Dose CT Screening Strategies

Bradley D Allen¹, Mark L Schiebler², Hans-Ulrich Kauczor^3,4, Jürgen Biederer^3,4,5, Timothy J Kruser⁶, Nisha A Mohindra⁷, David D Odell⁸, James C Carr¹, and Gorden B Hazen⁹

¹Radiology, Northwestern University, Chicago, IL, United States, ²Radiology, University of Wisconsin-Madison, Madison, WI, United States, ³Diagnostic and Interventional Radiology, University of Heidelberg, Heidelberg, Germany, ⁴Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung ResearchCenter (DZL), Heidelburg, Germany, ⁵Radiologie Darmstadt, Darmstadt, Germany, ⁶Radiation Oncology, Northwestern University, Chicago, IL, United States, ⁷Medicine - Hematology and Oncology, Northwestern University, Chicago, IL, United States, ⁸Surgery - Thoracic Surgery, Northwestern University, Chicago, IL, United States, ⁹Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States

Lung cancer screening with low dose CT (LDCT) has been shown to result in a 20% mortality reduction, but has relatively low specificity for lung cancer diagnosis, as well as concerns related to radiation dose and overdiagnosis. Lung MRI has similar sensitivity and improved specificity for lung cancer detection. In this study, we developed a Markov model of lung cancer screening to compare performance of LDCT and MRI. Based on our analysis, lung cancer screening with MRI could provide an equivalent number of lung cancer diagnoses, while dramatically reducing the number of false positive findings relative to LDCT.

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