The development and validation of a circulating tumor cells-based integrated model for improving the indeterminate lung solid nodules diagnosis.

Background: There is a risk of over investigation and delayed treatment in the work up of solid nodules. Thus, the aim of our study was to develop and validate an integrated model that estimates the malignant risk for indeterminate pulmonary solid nodules (IPSNs). Methods: Patients included in this study with IPSNs who was diagnosed malignant or benign by histopathology. Univariate and multivariate logistic regression were used to build integrated model based on clinical, circulating tumor cells (CTCs) and radiomics features. The performance of the integrated model was estimated by applying receiver operating characteristic (ROC) analysis, and tested in different nodules size and intermediate risk IPSNs. Net reclassification index (NRI) was applied to quantify the additional benefit derived from the integrated model. Results: The integrated model yielded areas under the ROC curves (AUCs) of 0.83 and 0.76 in internal and external set, respectively, outperforming CTCs (0.70, P=0.001; 0.68, P=0.128), the Mayo clinical model (0.68, P<0.001; 0.55, P=0.007), the and radiomics model (0.72, P=0.002; 0.67, P=0.050) in both validation sets. Robust performance with high sensitivity up to 98% was also maintained in IPSNs with different solid size and intermediate risk probability. The performance of the integrated model was comparable with positron emission tomography/computed tomography (PET-CT) examination (P=0.308) among the participants with established PET-CT records. NRI demonstrated that the integrated model provided net reclassification of at least 10% on the external validation set compared with single CTCs test. Conclusions: The integrated model could complement conventional risk models to improve the diagnosis of IPSNs, which is not inferior to PET-CT and could help to guide clinician's decision-making on clinically specific population.