A combined diagnostic model based on circulating tumor cell in patients with solitary pulmonary nodules

Background Although many prediction models in diagnosis of solitary pulmonary nodules (SPNs) have been developed, few are widely used in clinical practice. It is therefore imperative to identify novel biomarkers and prediction models supporting early diagnosis of SPNs. This study combined folate receptor‐positive circulating tumor cells (FR+CTC) with serum tumor biomarkers, patient demographics and clinical characteristics to develop a prediction model. Methods A total of 898 patients with a solitary pulmonary nodule who received FR+CTC detection were randomly assigned to a training set and a validation set in a 2:1 ratio. Multivariate logistic regression was used to establish a diagnostic model to differentiate malignant and benign nodules. The receiver operating curve (ROC) and the area under the curve (AUC) were calculated to assess the diagnostic efficiency of the model. Results The positive rate of FR+CTC between patients with non‐small cell lung cancer (NSCLC) and benign lung disease was significantly different in both the training and the validation dataset (p