ObjectiveTo investigate the clinical efficacy of preoperative location of pulmonary nodules guided by electromagnetic navigation bronchoscopy (ENB). MethodsPatients who received preoperative ENB localization and then underwent surgery from March 2021 to November 2022 in the Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine were collected. The clinical efficacy and safety of ENB localization and the related factors that may affect the success of ENB localization were analyzed. ResultsInitially 200 patients were included, among whom 17 undergoing preoperative localization and biopsy were excluded and a total of 183 patients and 230 nodules were finally included. There were 62 males and 121 females with a mean age of 49.16±12.50 years. The success rate of navigation was 88.7%, and the success rate of ENB localization was 67.4%. The rate of complications related to ENB localization were 2.7%, and the median localization time was 10 (7, 15) min. Multi-variable analysis showed that factors related to successful localization included distance from localization site (OR=0.27, 95%CI 0.13-0.59, P=0.001), staining material (OR=0.40, 95%CI 0.17-0.95, P=0.038), and staining dose (OR=60.39, 95%CI 2.31-1 578.47, P=0.014). Conclusion ENB-guided preoperative localization of pulmonary nodules is safe and effective, and the incidence of complications is low, which can be used to effectively assist the diagnosis and treatment of early lung cancer.
ObjectiveTo evaluate the safety and application value of three-dimensional reconstruction for localization of pulmonary nodules in thoracoscopic lung wedge resection.MethodsThe clinical data of 96 patients undergoing thoracoscopic lung wedge resection in our hospital from January 2019 to August 2020 were retrospectively reviewed and analyzed, including 30 males and 66 females with an average age of 57.62±12.13 years. The patients were divided into two groups, including a three-dimensional reconstruction guided group (n=45) and a CT guided Hook-wire group (n=51). The perioperative data of the two groups were compared.ResultsAll operations were performed successfully. There was no statistically significant difference between the two groups in the failure rate of localization (4.44% vs. 5.88%, P=0.633), operation time [15 (12, 19) min vs. 15 (13, 17) min, P=0.956], blood loss [16 (10, 20) mL vs. 15 (10, 19) mL, P=0.348], chest tube placement time [2 (2, 2) d vs. 2 (2, 2) d, P=0.841], resection margin width [2 (2, 2) cm vs. 2 (2, 2) cm, P=0.272] or TNM stage (P=0.158). The complications of CT guided Hook-wire group included pneumothorax in 2 patients, hemothorax in 2 patients and dislodgement in 4 patients. There was no complication related to puncture localization in the three-dimensional reconstruction guided group.ConclusionBased on three-dimensional reconstruction, the pulmonary nodule is accurately located. The complication rate is low, and it has good clinical application value.
The robotic bronchoscopy system is a new technology for lung lesion location, biopsy and interventional therapy. Its safety and effectiveness have been clinically proven. Based on many advanced technologies carried by the robotic bronchoscopy system, it is more intelligent, convenient and stable when clinicians perform bronchoscopy operations. It has higher accuracy and diagnostic rates, and less complications than bronchoscopy with the assistance of magnetic navigation and ordinary bronchoscopy. This article gave a review of the progress of robotic bronchoscopy systems, and a prospect of the combination with artificial intelligence.
ObjectiveTo compare the clinical application of empirical thoracoscopic segmentectomy and precise segmentectomy planned by artificial intelligence software, and to provide some reference for clinical segmentectomy. MethodsA retrospective analysis was performed on the patients who underwent thoracoscopic segmentectomy in our department from 2019 to 2022. The patients receiving empirical thoracoscopic segmentectomy from January 2019 to September 2021 were selected as a group A, and the patients receiving precise segmentectomy from October 2021 to December 2022 were selected as a group B. The number of preoperative Hookwire positioning needle, proportion of patients meeting oncology criteria, surgical time, intraoperative blood loss, postoperative chest drainage time, postoperative hospital stay, and number of patients converted to thoracotomy between the two groups were compared. Results A total of 322 patients were collected. There were 158 patients in the group A, including 56 males and 102 females with a mean age of 56.86±8.82 years, and 164 patients in the group B, including 55 males and 109 females with a mean age of 56.69±9.05 years. All patients successfully underwent thoracoscopic segmentectomy, and patients whose resection margin did not meet the oncology criteria were further treated with extended resection or even lobectomy. There was no perioperative death. The number of positioning needles used for segmentectomy in the group A was more than that in the group B [47 (29.7%) vs. 9 (5.5%), P<0.001]. There was no statistical difference in the number of positioning needles used for wedge resection between the two groups during the same period (P=0.572). In the group A, the nodule could not be found in the resection target segment in 3 patients, and the resection margin was insufficient in 10 patients. While in the group B, the nodule could not be found in 1 patient, and the resection margin was insufficient in 3 patients. There was a statistical difference between the two groups [13 (8.2%) vs. 4 (2.4%), P=0.020]. There was no statistical difference between the two groups in terms of surgical time, intraoperative blood loss, duration of postoperative thoracic drainage, postoperative hospital stay, or conversion to open chest surgery (P＞0.05). Conclusion Preoperative surgical planning performed with the help of artificial intelligence software can effectively guide the completion of thoracoscopic anatomical segmentectomy. It can effectively ensure the resection margin of pulmonary nodules meeting the oncological requirements and significantly reduce the number of positioning needles of pulmonary nodules.
ObjectiveTo establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. MethodsPatients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. ResultsA total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. ConclusionFor patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.
Objective To analyze the benign-malignant outcomes of pulmonary nodules in surgical patients and their influencing factors, and provide evidence and ideas for optimizing and improving the integrated management model of pulmonary nodules. Methods From October to December 2023, a convenience sampling method was used to select patients who underwent lung surgery at West China Hospital, Sichuan University between July 2022 and June 2023 for this study. The malignancy rate of postoperative pathological results of pulmonary nodules and its influencing factors were analyzed using univariate analysis and multiple logistic regression. Results A total of 4600 surgical patients with pulmonary nodules were included, with a malignancy rate of 88.65% (4078/4600) and a benign rate of 11.35% (522/4600). Univariate analysis showed significant differences in malignancy rates among different genders, ages, methods of pulmonary nodule detection, and smoking histories (P<0.05); however, no significant difference was found regarding place of birth or family history of lung cancer (P>0.05). Multiple logistic regression analysis indicated that females [odds ratio (OR)=1.533, 95% confidence interval (CI) (1.271, 1.850)], older age groups [61-75 vs. ≤30 years: OR=1.640, 95%CI (1.021, 2.634); >75 vs. ≤30 years: OR=2.690, 95%CI (1.062, 6.814)], and pulmonary nodules detected during physical examinations [OR=1.286, 95%CI (1.064, 1.554)] were high-risk factors for malignancy, with statistical significance (P<0.05). Conclusion In the integrated management of pulmonary nodules, it is crucial not to overlook females or older patients, as they may be more significant influencing factors than smoking; furthermore, lung examinations are effective means of early detection of malignant lung tumors and are worth promoting and popularizing.
Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. ResultsA total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.
Increasing peripheral pulmonary nodules are detected given the growing adoption of chest CT screening for lung cancer. The invention of electromagnetic navigation bronchoscope provides a new diagnosis and treatment method for pulmonary nodules, which has been demonstrated to be feasible and safe, and the technique of microwave ablation through bronchus is gradually maturing. The one-stop diagnosis and treatment of pulmonary nodules can be completed by the combination of electromagnetic navigation bronchoscopy and microwave ablation, which will help achieve local treatment through the natural cavity without trace.
ObjectiveTo systematically evaluate the application effect of CT-guided Hook-wire localization and CT-guided microcoil localization in pulmonary nodules surgery. MethodsThe literatures on the comparison between CT-guided Hook-wire localization and CT-guided microcoil localization for pulmonary nodules were searched in PubMed, EMbase, The Cochrane Library, Web of Science, Wanfang, VIP and CNKI databases from the inception to October 2021. Review Manager (version 5.4) software was used for meta-analysis. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of studies.ResultsA total of 10 retrospective cohort studies were included, with 1 117 patients including 473 patients in the CT-guided Hook-wire localization group and 644 patients in the CT-guided microcoil localization group. The quality of the studies was high with NOS scores>6 points. The result of meta-analysis showed that the difference in the localization operation time (MD=0.14, 95%CI −3.43 to 3.71, P=0.940) between the two groups was not statistically significant. However, the localization success rate of the Hook-wire group was superior to the microcoil group (OR=0.35, 95%CI 0.17 to 0.72, P=0.005). In addition, in comparison with Hook-wire localization, the microcoil localization could reduce the dislocation rate (OR=4.33, 95%CI 2.07 to 9.08, P<0.001), the incidence of pneumothorax (OR=1.62, 95%CI 1.12 to 2.33, P=0.010) and pulmonary hemorrhage (OR=1.64, 95%CI 1.07 to 2.51, P=0.020). ConclusionAlthough Hook-wire localization is slightly better than microcoil localization in the aspect of the success rate of pulmonary nodule localization, microcoil localization has an obvious advantage compared with Hook-wire localization in terms of controlling the incidence of dislocation, pneumothorax and pulmonary hemorrhage. Therefore, from a comprehensive perspective, this study believes that CT-guided microcoil localization is a preoperative localization method worthy of further promotion.
ObjectiveTo explore the application of artificial intelligence (AI) in the standardized training of thoracic surgery residents, specifically in enhancing clinical skills and anatomical understanding through AI-assisted lung nodule identification and lung segment anatomy teaching. MethodsThoracic surgery residents undergoing standardized training at Peking Union Medical College Hospital from September 2023 to September 2024 were selected. They were randomly assigned to a trial group and a control group using a random number table. The trial group used AI-assisted three-dimensional reconstruction technology for lung nodule identification, while the control group used conventional chest CT images. After basic teaching and self-practice, the ability to identify lung nodules on the same patient CT images was evaluated, and feedback was collected through questionnaires. ResultsA total of 72 residents participated in the study, including 30 (41.7%) males and 42 (58.3%) females, with an average age of (24.0±3.0) years. The trial group showed significantly better overall diagnostic accuracy for lung nodules (91.9% vs. 73.3%) and lung segment identification (100.0% vs. 83.70%) compared to the control group, and the reading time was significantly shorter [ (118.5±10.5) s vs. (332.1±20.2) s, P<0.01]. Questionnaire results indicated that 94.4% of the residents had a positive attitude toward AI technology, and 91.7% believed that it improved diagnostic accuracy. ConclusionAI-assisted teaching significantly improves thoracic surgery residents’ ability to read images and clinical thinking, providing a new direction for the reform of standardized training.