A nomogram model for diagnosing bone metastasis in category T1 Lung Adenocarcinoma

Rationale and Objectives: Bone metastasis (BM) significantly affects the prognosis of lung adenocarcinoma (LUAD) patients. Currently, no effective clinical model exists for predicting early BM in category T1 LUAD. This study aims to develop a model for timely BM detection by analyzing relevant influencing factors. Materials and Methods This retrospective study analyzed data from 478 patients with category T1 LUAD from August 2017 to August 2023. Of these, 334 patients were assigned to a training cohort and 144 to an internal validation cohort. Univariate and multivariate analyses identified BM risk factors, leading to a nomogram model. Model performance was evaluated using area under the curve (AUC), calibration curves, and decision curve analysis (DCA). An online calculator was also created to assess BM risk. Results Multivariate analysis revealed that alkaline phosphatase (ALP), carcinoembryonic antigen (CEA), nodule type, CT-reported N staging, and pleural effusion are independent BM risk factors. The nomogram showed strong accuracy, with AUC values of 0.929 in the training cohort and 0.954 in the validation cohort. Calibration analyses confirmed reliability, with DCA indicating high clinical benefit for both cohorts. Conclusion This nomogram effectively identifies high-risk patients for BM in category T1 LUAD, aiding personalized clinical decision-making.