Clinical model to estimate the pretest probability of malignancy in patients with pulmonary focal Ground-glass Opacity

Abstract

Background: Effective strategies for managing patients with pulmonary focal Ground-glass Opacity (fGGO) depend on the pretest probability of malignancy. Estimating a clinical probability of malignancy in patients with fGGOs can facilitate the selection and interpretation of subsequent diagnostic tests. Methods: Data from patients with pulmonary fGGO lesions, who were diagnosed at Sun Yat-sen University Cancer Center, was retrospectively collected. Multiple logistic regression analysis was used to identify independent clinical predictors for malignancy and to develop a clinical predictive model to estimate the pretest probability of malignancy in patients with fGGOs. Results: One hundred and sixty-five pulmonary fGGO nodules were detected in 128 patients. Independent predictors for malignant fGGOs included a history of other cancers (odds ratio [OR], 0.264; 95% confidence interval [CI], 0.072 to 0.970), pleural indentation (OR, 8.766; 95% CI, 3.033-25.390), vessel-convergence sign (OR, 23.626; 95% CI, 6.200 to 90.027) and air bronchogram (OR, 7.41; 95% CI, 2.037 to 26.961). Model accuracy was satisfactory (area under the curve of the receiver operating characteristic, 0.934; 95% CI, 0.894 to 0.975), and there was excellent agreement between the predicted probability and the observed frequency of malignant fGGOs. Conclusions: We have developed a predictive model, which could be used to generate pretest probabilities of malignant fGGOs, and the equation could be incorporated into a formal decision analysis. © 2013 Tianjin Lung Cancer Institute and Wiley Publishing Asia Pty Ltd.