Blood-Based Biomarkers for Pulmonary Nodule Risk Stratification

Background: Indeterminate pulmonary nodules are frequently detected on chest imaging, particularly in lung cancer screening and incidental imaging cohorts. Existing clinical and radiographic risk models often classify a substantial proportion of nodules as intermediate risk, leading to prolonged surveillance or invasive diagnostic procedures. Blood-based biomarkers have emerged as adjunctive tools to improve risk stratification and guide clinical decision-making. Methods: We conducted a systematic review and meta-analysis of studies evaluating blood-based biomarkers for distinguishing malignant from benign pulmonary nodules. Thirteen studies comprising 2883 patients met inclusion criteria. Study-level sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were extracted and pooled using random-effects bivariate and hierarchical summary ROC models. Positive and negative predictive values (PPV and NPV) were calculated assuming a malignancy prevalence of 15%. Subgroup analyses were performed by biomarker modality, including proteomic assays, cfDNA-based assays, autoantibody tests, and integrated classifiers. Risk of bias and publication bias were assessed using standard methods. Results: Across all studies, pooled diagnostic performance demonstrated a sensitivity of 0.83 (95% CI: 0.78-0.87) and specificity of 0.68 (95% CI: 0.61-0.74), with a summary AUC of 0.81 (95% CI: 0.77-0.85). At a modeled prevalence of 15%, the pooled NPV was 0.96 (95% CI: 0.94-0.97) and PPV was 0.32 (95% CI: 0.27-0.37). In subgroup analyses, cfDNA-based assays demonstrated the highest overall diagnostic accuracy (AUC: 0.86), while integrated classifiers achieved high sensitivity (0.87) with excellent negative predictive value. Proteomic assays showed high sensitivity but lower specificity, whereas autoantibody assays demonstrated low sensitivity with high specificity. No significant publication bias was detected. Conclusion: Blood-based biomarker assays demonstrate clinically meaningful diagnostic performance for pulmonary nodule risk stratification, particularly as rule-out tools in intermediate-risk populations. cfDNA-based and integrated approaches showed the most consistent performance. These tests may help reduce unnecessary invasive procedures when used alongside established clinical and radiologic models.