A Prediction Model Based on Biomarkers and Clinical Characteristics for Detection of Lung Cancer in Pulmonary Nodules.

Lung cancer early detection by low-dose computed tomography (LDCT) can reduce the mortality. However, LDCT increases the number of indeterminate pulmonary nodules (PNs), whereas 95% of the PNs are ultimately false positives. Modalities for specifically distinguishing between malignant and benign PNs are urgently needed. We previously identified a panel of peripheral blood mononucleated cell (PBMC)-miRNA (miRs-19b-3p and -29b-3p) biomarkers for lung cancer. This study aimed to evaluate efficacy of integrating biomarkers and clinical and radiological characteristics of smokers for differentiating malignant from benign PNs. We analyzed expression of 2 miRNAs (miRs-19b-3p and -29b-3p) in PBMCs of a training set of 137 individuals with PNs. We used multivariate logistic regression analysis to develop a prediction model based on the biomarkers, radiographic features of PNs, and clinical characteristics of smokers for identifying malignant PNs. The performance of the prediction model was validated in a testing set of 111 subjects with PNs. A prediction model comprising the two biomarkers, spiculation of PNs and smoking pack-year, was developed that had 0.91 area under the curve of the receiver operating characteristic for distinguishing malignant from benign PNs. The prediction model yielded higher sensitivity (80.3% vs 72.6%) and specificity (89.4% vs 81.9%) compared with the biomarkers used alone (all P<.05). The performance of the prediction model for malignant PNs was confirmed in the validation set. We have for the first time demonstrated that the integration of biomarkers and clinical and radiological characteristics could efficiently identify lung cancer among indeterminate PNs.