Deep learning-based computed tomography quantification integrated with circulating tumor cells for prognostic evaluation in stage I lung adenocarcinoma.

Background: Prognosis varies among patients with early-stage lung adenocarcinoma (LUAD) even within the same clinical stage. This study aimed to evaluate the prognostic value of measurements of computed tomography (CT), circulating tumor cells (CTC), and their combinations in clinical stage I LUAD. Methods: This retrospective analysis included 183 patients at stage I peripheral LUAD who underwent preoperative thin-section chest CT and CTC detection. The maximal solid size to tumor ratio on axial image was measured by radiologists (R-MSSA%). The three-dimensional volume (3D-SV) and mass (3D-SM) of solid components were measured by deep learning, followed by calculation of the consolidation-to-tumor ratios of 3D-SV (3D-SV%) and 3D-SM (3D-SM%). The differences in recurrence rates or overall survival of CTC, CT measurements, and their combinations were compared. Multivariate Cox proportional hazards models were used to identify independent risk factors. Results: Our study results indicated that 3D-SM, 3D-SV, 3D-SM%, and CTC can stratify the recurrence risk in clinical stage I patients. For clinical stage IA patients with high values of 3D-SM, 3D-SV, R-MSSA%, or 3D-SM%, the recurrence risk showed no significant difference compared with stage IB patients. Furthermore, for stage IA patients stratified as high-risk by the integration of CTC with 3D-SM or 3D-SV, the prognosis was worse than that of stage IB patients. A model combining 3D-SM and CTC had a higher C-index [0.75; 95% confidence interval (CI): 0.70, 0.81] compared to that of 3D-SM or CTC alone. Conclusions: The integration of CTC and CT measurements can enhance the stratification ability for early-stage lung cancer patients and improve the prognosis prediction, especially for those at stage IA.