Abstract
Purpose: To develop an effective diagnostic model for bone metastasis of gastric cancer by combining (18)F-FDG PET/CT and clinical data. Materials and Methods: A total of 212 gastric cancer patients with abnormal bone imaging scans based on (18)F-FDG PET/CT were retrospectively enrolled between September 2009 and March 2020. Risk factors for bone metastasis of gastric cancer were identified by multivariate logistic regression analysis and used to create a nomogram. The performance of the nomogram was evaluated by using receiver operating characteristic curves and calibration plots. Results: The diagnostic power of the binary logistic regression model incorporating skeleton-related symptoms, anemia, the SUVmax of bone lesions, bone changes, the location of bone lesions, ALP, LDH, CEA, and CA19-9 was significantly higher than that of the model using only clinical factors (p = 0.008). The diagnostic model for bone metastasis of gastric cancer using a combination of clinical and imaging data showed an appropriate goodness of fit according to a calibration test (p = 0.294) and good discriminating ability (AUC = 0.925). Conclusions: The diagnostic model combined with the (18)F-FDG PET/CT findings and clinical data showed a better diagnosis performance for bone metastasis of gastric cancer than the other studied models. Compared with the model using clinical factors alone, the additional (18)F-FDG PET/CT findings could improve the diagnostic efficacy of identifying bone metastases in gastric cancer.