Background::Computed tomography images are easy to misjudge because of their complexity, especially images of solitary pulmonary nodules, of which diagnosis as benign or malignant is extremely important in lung cancer treatment. Therefore, there is an urgent need for a more effective strategy in lung cancer diagnosis. In our study, we aimed to externally validate and revise the Mayo model, and a new model was established.Methods::A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training, internal validation, and external validation sets ( n = 849, 365, and 236, respectively). External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training set. Overall performance of each model was evaluated using area under receiver operating characteristic curve (AUC). Finally, the model validation was completed on the validation data set. Res

作者：Liu Hai-Yang；Zhao Xing-Ru；Chi Meng；Cheng Xiang-Song；Wang Zi-Qi；Xu Zhi-Wei；Li Yong-Li；Yang Rui；Wu Yong-Jun；Zhang Xiao-Ju

来源：中华医学杂志英文版 2021 年 134卷 14期