ObjectiveTo investigate the clinical value of artificial intelligence (AI)-assisted chest computed tomography (CT) in the diagnosis of peripheral lung shadow. MethodsThe CT image data of 810 patients with peripheral pulmonary shadow treated by thoracic surgery in Tianjin Chest Hospital Affiliated to Tianjin University from January 2018 to July 2019 were retrospectively analyzed using AI-assisted chest CT imaging diagnosis system. There were 339 males and 471 females with a median age of 63 years. The malignant probability of preoperative AI-assisted diagnosis of peripheral pulmonary shadow was compared with the results of postoperative pathology. ResultsThe pathological diagnosis of 810 patients with peripheral pulmonary shadow was lung cancer in 627 (77.4%) patients, precancerous lesion in 30 (3.7%) patients and benign lesion in 153 (18.9%) patients. The median probability of malignant AI diagnosis before operation was 86.0% (lung cancer), 90.0% (precancerous lesion) and 37.0% (benign lesion), respectively. According to the analysis of receiver operating characteristic (ROC) curve of AI malignant probability distribution in this group of patients, the area under the ROC curve was 0.882. The critical value of malignant probability for diagnosis of lung cancer was 75.0% with a sensitivity of 0.856 and specificity of 0.814. A total of 571 patients were diagnosed with AI malignancy probability≥75.0%, among whom 537 patients were pathologically diagnosed as lung cancer with a positive predictive value of 94.0% (537/571). ConclusionThe AI-assisted chest CT diagnosis system has a high accuracy in the diagnosis of peripheral lung cancer with malignant probability≥75.0% as the diagnostic threshold.
ObjectiveTo explore the key points and difficulties of intraoperative frozen section diagnosis of pulmonary diseases. MethodsThe intraoperative frozen section and postoperative paraffin section results of pulmonary nodule patients in Beijing Chaoyang Hospital, Capital Medical University from January 2021 to January 2022 were collected. The main causes of misdiagnosis in frozen section diagnosis were analyzed, and the main points of diagnosis and differential diagnosis were summarized. ResultsAccording to the inclusion criteria, a total of 1 263 frozen section diagnosis results of 1 178 patients were included in the study, including 475 males and 703 females, with an average age of 58.7 (23-86) years. In 1 263 frozen section diagnosis results, the correct diagnosis rate was 95.65%, and the misdiagnosis rate was 4.35%. There were 55 misdiagnoses, including 18 (3.44%) invasive adenocarcinoma, 17 (5.82%) adenocarcinoma in situ, 7 (35.00%) mucinous adenocarcinoma, 4 (2.09%) minimally invasive adenocarcinoma, 3 (100.00%) IgG4 related diseases, 2 (66.67%) mucinous adenocarcinoma in situ, 1 (16.67%) atypical adenomatous hyperplasia, 1 (14.29%) sclerosing pulmonary cell tumor, 1 (33.33%) bronchiolar adenoma, and 1 (100.00%) papillary adenoma. ConclusionIntraoperative frozen section diagnosis still has its limitations. Clinicians need to make a comprehensive judgment based on imaging examination and clinical experience.