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

Abstract

AbstractBackgroundAlthough 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.MethodsA 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.ResultsThe 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 < 0.001). The FR+CTC level was significantly higher in the NSCLC group compared with that of the benign group (p < 0.001). FR+CTC (odds ratio, OR, 95% confidence interval, CI: 1.13, 1.07–1.19, p < 0.0001), age (OR, 95% CI: 1.06, 1.01–1.12, p = 0.03) and sex (OR, 95% CI: 1.07, 1.01–1.13, p = 0.01) were independent risk factors of NSCLC in patients with a solitary pulmonary nodule. The area under the curve (AUC) of FR+CTC in diagnosing NSCLC was 0.650 (95% CI, 0.587–0.713) in the training set and 0.700 (95% CI, 0.603–0.796) in the validation set, respectively. The AUC of the combined model was 0.725 (95% CI, 0.659–0.791) in the training set and 0.828 (95% CI, 0.754–0.902) in the validation set, respectively.ConclusionsWe confirmed the value of FR+CTC in diagnosing SPNs and developed a prediction model based on FR+CTC, demographic characteristics, and serum biomarkers for differential diagnosis of solitary pulmonary nodules.