• Lung Cancer screening by Low Dose Computed Tomography (LDCT) for patients that fall into high-risk categories results in high sensitivity for finding pulmonary nodules, but poor specificity since typically > 95 % are benign. Approximately-two thirds of identified nodules are Indeterminate pulmonary nodules (IPNs) with unclear risk for malignancy. • Biomarkers may improve the ability to assess IPNs for risk of lung cancer malignancy. Biomarkers are less invasive and costly versus surgical biopsies for lung cancer detection. • A combination of imaging and biomarkers yields improved specificity/sensitivity for detecting malignant IPNs over their use as single methods of lung cancer detection. This combined approach could improve the ability to predict likelihood of malignancy of IPNs and could aid clinicians and patients plan appropriate nodule assessment follow-up. • Clinical lung cancer risk prediction model improvements were robust across multiple model building methods when using a combination of biomarkers and imaging to assess lung cancer risk in IPNs. Multivariable analyses yielded an algorithm consisting of CA-125, total IgG, IgA, IgM, IgE, LFLC, nodule size, and smoking pack years with the following performance attributes (AUC 0.82, 95 %CI 0.74–0.90). Early detection of lung cancer allows for earlier stage treatment initiation and improved patient prognosis. This report focuses on utilization of combining patient demographic information with non-invasive biomarkers and their potential ability to predict risk of malignancy of nodules. A pilot study cohort of 141 subjects with IPNs (105 stage I cancer and 36 benign nodules) were collected by RUMC. The demographic variables of gender, age, sex, race, ethnicity, nodule size (mm), and smoking pack years, as well as the plasma levels of CA-125, SCC, CEA, HE4, ProGRP, NSE, Cyfra 21-1, hs-CRP, Ferritin, IgG, IgG1, IgG2, IgG3, IgG4, IgE, IgM, IgA, KFLC, and LFLC, were assessed for this cohort. Multivariable analyses of the previously aforementioned biomarkers and demographic variables yielded a reduced algorithm consisting of CA-125, total IgG, IgA, IgM, IgE, LFLC, nodule size, and smoking pack years with improved performance (AUC 0.82, 95 %CI 0.74–0.90) over the same analysis of the demographic variables (age, nodule size, and smoking pack years) alone (AUC 0.70, 95 %CI 0.61–0.78). This reduced algorithm of biomarkers and demographic variables may aid in assessing the risk of IPN malignancy which could be a useful stratification tool in early detection of lung cancer in high-risk subjects. [ABSTRACT FROM AUTHOR]