Objective To improve accuracy of clinical diagnosis through analyzing the CT characteristics and clinical manifestations of patients with benign lung diseases whose CT manifestations initially led to a suspicion of lung cancer. Methods This study collected 2 239 patients of benign lung disease verified by postoperative pathology in the Department of Thoracic Surgery, Beijing Chao-yang Hospital from June 2006 to December 2016. Lesions of 173 patients (101 males and 72 females with a mean age of 56.0 years) were considered very likely to be malignant on preoperative contrast CT scan, which were sorted to 20 types of lung diseases, and the 20 types of diseases contained 907 patients diagnosed or misdiagnosed. Statistical analyses were performed using the CT and clinical characteristics of the 173 patients. Results Among the 907 patients with benign lung disease, the benign pathologies that were most commonly misdiagnosed by preoperative enhanced CT were pulmonary leiomyoma (100.0%), pulmonary actinomycosis (75.0%), pulmonary cryptococcosis (71.4%), sclerosing hemangioma (50.0%) and organizing pneumonia (44.2%). Among the 173 patients with benign diseases, the most common diseases were tuberculosis (29.5%), organizing pneumonia (28.9%), pulmonary hamartoma (6.4%) and pulmonary abscess (6.4%). In the 173 patients, 17.3% had fever, 56.6% coughing, 8.7% yellow sputum, 28.9% hemoptysis, 16.2% chest pain, 18.5% elevated leukocyte counts and 4.6% elevated carcinoembryonic antigen levels. Most of the CT manifestations consisted of nodular or mass shadows, 70.5% of which had foci≤3 cm and manifestations were similar to those of lung cancer, such as a spiculated margin (49.1%), lobulation (33.5%), pleural indentation (27.2%) and significant enhancement (39.3%). Furthermore, some patients had uncommon tumor signs, such as calcification (12.7%), central liquefactive necrosis (18.5%), satellite foci (9.8%) and multiple pulmonary nodules (42.2%). Moreover, 24.3% of the patients had enlarged lymph nodes of the mediastinum or hilum. Conclusion As the CT manifestations of some benign lung conditions are similar to those of lung cancer, careful differential diagnosis is necessary to identify the basic characteristics of the disease when the imaging results are ambiguous, and the diagnosis of a lung disease need incorporate the patients' clinical characteristics and a comprehensive analysis.
Objective To explore the independent risk factors for tumor invasiveness of ground-glass nodules and establish a tumor invasiveness prediction model. Methods A retrospective analysis was performed in 389 patients with ground-glass nodules admitted to the Department of Thoracic Surgery in the First Hospital of Lanzhou University from June 2018 to May 2021 with definite pathological findings, including clinical data, imaging features and tumor markers. A total of 242 patients were included in the study according to inclusion criteria, including 107 males and 135 females, with an average age of 57.98±9.57 years. CT data of included patients were imported into the artificial intelligence system in DICOM format. The artificial intelligence system recognized, automatically calculated and output the characteristics of pulmonary nodules, such as standard diameter, solid component size, volume, average CT value, maximum CT value, minimum CT value, central CT value, and whether there were lobulation, burr sign, pleural depression and blood vessel passing. The patients were divided into two groups: a preinvasive lesions group (atypical adenomatoid hyperplasia/adenocarcinoma in situ) and an invasive lesions group (minimally invasive adenocarcinoma/invasive adenocarcinoma). Univariate and multivariate analyses were used to screen the independent risk factors for tumor invasiveness of ground-glass nodules and then a prediction model was established. The receiver operating characteristic (ROC) curve was drawn, and the critical value was calculated. The sensitivity and specificity were obtained according to the Yorden index. Results Univariate and multivariate analyses showed that central CT value, Cyfra21-1, solid component size, nodular nature and burr of the nodules were independent risk factors for the diagnosis of tumor invasiveness of ground-glass nodules. The optimum critical value of the above indicators between preinvasive lesions and invasive lesions were –309.00 Hu, 3.23 ng/mL, 8.65 mm, respectively. The prediction model formula for tumor invasiveness probability was logit (P)=0.982–(3.369×nodular nature)+(0.921×solid component size)+(0.002×central CT value)+(0.526×Cyfra21-1)–(0.0953×burr). The areas under the curve obtained by plotting the ROC curve using the regression probabilities of regression model was 0.908. The accuracy rate was 91.3%. Conclusion The logistic regression model established in this study can well predict the tumor invasiveness of ground-glass nodules by CT and tumor markers with high predictive value.