Simple SummaryIndeterminate pulmonary nodules detected by computer tomography are a common clinical finding, but the path to determine malignancy can cause harm to patients. We aimed to comprehensively assess other types of noninvasive information that could help clinicians diagnose lung cancer, including semantic imaging features, quantitative radiomic imaging features and proteomic, glycomic, and autoantibody–antigen complex blood-based biomarkers. Utilizing these data, we generated a malignancy risk prediction model called PSR (plasma, semantic, radiomic) comprising nine imaging and molecular biomarkers. The PSR model performed well in two cohorts and against a clinical risk prediction model. Adding known clinical risk factors for lung cancer further improved the PSR model, indicating that our discovered markers held independent clinical utility. Our study revealed novel biomarkers and a risk prediction model to help assess cancer risk in patients with indeterminate pulmonary nodules.AbstractThe 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.