Kristin J Lastwika, Wei Wu, Yuzheng Zhang, Ningxin Ma, Mladen Zečević, Sudhakar N J Pipavath, Timothy W Randolph, A McGarry Houghton, Viswam S Nair, Paul D Lampe, Paul E Kinahan
Author Information
Kristin J Lastwika: Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Wei Wu: Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA.
Yuzheng Zhang: Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Ningxin Ma: Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Mladen Zečević: Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA. ORCID
Sudhakar N J Pipavath: Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA.
Timothy W Randolph: Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA. ORCID
A McGarry Houghton: Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Viswam S Nair: Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Paul D Lampe: Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA. ORCID
Paul E Kinahan: Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA.
The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.
