Objective To evaluate the validity of video-assisted thoracoscopic surgery (VATS) pneumonectomy in thoracic diseases treatment. Methods We retrospectively analyzed the clinical data of 34 consecutive patients who underwent VATS pneumonectomy in Xiangya Hospital Central South University between January 2013 and October 2015. There were 26 males and 8 females at age of 35–69 (53.8±7.7) years. Results VATS pneumonectomy was completed successfully in 32 patients (5.8% conversion rate). The average operation time was 182.5±52.4 min. The average blood loss was 217.1±1 834.8 ml. Chest tube drainage flow was 3–11 (6.0±1.7) days and postoperative hospital stay was 5–12 (7.6±1.8) days. Eleven patients got postoperative complications (34.3%), mainly pulmonary infections. The 32 patients were followed up for 10 (1–21) months. Two patients died of lung metastasis 16 or 17 months after the operation. One patient died of sudden cardiac arrest 3 months after operation. Bronchopleural fistula (BPF) happened in one patient after hospital discharge in 2 months. Conclusion VATS is feasible for pneumonectomy. However, further studies and follow-up are needed to verify the benefits of VATS pneumonectomy for lung cancer.
ObjectiveTo analyze the risk factors for complications after robotic segmentectomy.MethodsClinical data of 207 patients undergoing robot-assisted anatomical segmentectomy in our hospital from June 2015 to July 2019 were retrospectively analyzed, including 69 males and 138 females with a median age of 54.0 years. The relationship between clinicopathological factors and prolonged air leakage, pleural effusion, and pulmonary infection after surgery was analyzed.ResultsAfter robot-assisted segmentectomy, 20 (9.7%) patients developed prolonged air leakage (>5 d), 17 (8.2%) patients developed pleural effusion, and 4 (1.9%) patients developed pulmonary infection. Univariate logistic regression showed that body mass index (BMI, P=0.018), FEV1% (P=0.024), number of N1 lymph nodes resection (P=0.008) were related to prolonged air leakage after robot-assisted segmentectomy. Benign lesion was a risk factor for pleural effusion (P=0.013). The number of lymph node sampling stations was significantly related to the incidence of pulmonary infection (P=0.035). Multivariate logistic analysis showed that the BMI (OR=0.73, P=0.012) and N1 lymph node sampling (OR=1.38, P=0.001) had a negative and positive relationship with prolonged air leakage after robot-assisted segmentectomy, respectively.ConclusionThe incidence of pulmonary complications after robot-assisted segmentectomy is low. The lower BMI and more N1 lymph node sampling is, the greater probability of prolonged air leakage is. Benign lesions and more lymph node sampling stations are risk factors for pleural effusion and lung infection, respectively. Attention should be paid to the prevention and treatment of perioperative complications for patients with such risk factors.
Objective To evaluate the security and clinical value of the combination of three-dimensional computed tomography-bronchography and angiography (3D-CTBA) and indocyanine green (ICG) staining in video-assisted thoracic surgery (VATS) segmentectomy. Methods The clinical data of 125 patients who received VATS segmentectomy from January 2020 to January 2021 in our hospital were retrospectively analyzed. There were 40 (32.0%) males and 85 (68.0%) females with an average age of 54.8±11.1 years. Results The procedure was almost identical to the preoperative simulation. All intersegment planes were displayed successfully by ICG reverse staining method. There was no allergic patient. A total of 130 pathological specimens were obtained from the 125 patients. The mean operation time was 126.8±41.9 min, the time of first appearance of fluorescence was 22.7±4.9 s, the mean mark time was 65.6±20.3 s, the median blood loss was 20.0 (10.0-400.0) mL, the postoperative hospital stay was 5.6 (4.0-28.0) d, and the postoperative retention of chest tube time was 3.2 (2.0-25.0) d. Pathological results showed that microinvasive adenocarcinoma was the most common type (38.5%, 50/130), followed by invasive adenocarcinoma (36.9%, 48/130); there were 3 metastatic tumors (3/130, 2.3%).Conclusion The combination of 3D-CTBA and ICG reverse staining is proved to be a safe, necessary and feasible method. It solves the difficult work encountered in the procedure of segmentectomy, and it is worth popularizing and applying in clinic.
ObjectiveTo evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological typing of pulmonary nodules, and to summarize clinical application experience. MethodsA retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological typing of pulmonary nodules. ResultsIn the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate analysis showed that age, nodule type (subsolid pulmonary nodule), average density, burr sign, and vascular convergence sign were independent risk factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), burr sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The AUC value of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological typing were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians' prediction of lung nodule pathological typing indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50%, respectively. ConclusionThis AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological typing of lung nodules, especially in predicting lung nodule pathological typing, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.