ObjectiveTo explore the safety and effectiveness of a precise marking method based on body surface mesh and three-dimensional (3D) image reconstruction.MethodsWe retrospectively analyzed the clinical data of 22 patients in our hospital from October 2018 to October 2019. There were 13 males and 9 females aged 58.5 (37-72) years. All patients underwent a precise marking of pulmonary nodules based on body surface mesh and 3D image reconstruction. Then, video-assisted thoracoscopic surgery (VATS) was performed to resect the nodules. The clinical data, including positioning success rate and operation time were analyzed.ResultsA total of 22 small pulmonary nodules were removed. The average diameter of small nodules was 12±3 mm, and the average distance from the visceral pleura was 17±6 mm. The localization success rate was 86.4%. The operation time was 110±43 min, and there was no surgery-related complication.ConclusionThe method of marking pulmonary nodules based on body surface mesh and 3D image reconstruction is a safe and reliable technology, which reduces the risk of hemopneumothorax caused by CT-guided lung puncture.
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 analyze the value of structured electronic medical records for pulmonary nodules in increasing the ability of outpatient service and hospital management by resident physicians.MethodsWe included 40 trainees [94 males and 26 females aged 22-31 (26.45±2.81) years] who were trained in the standardized training base for surgical residents in our hospital from January 2018 to January 2021. The trainees were randomly divided into two groups including a structured group using the structured electronic medical record for pulmonary nodule and an unstructured group using unstructured electronic medical record designed by our department. The time of completing hospitalization records and first-time course records, the quality of course records, the accuracy of issuing admission orders, the quality of teaching rounds, and patient’s satisfaction between the two groups were analyzed and compared.Results(1) The average time in the structured group to complete inpatient medical records was significantly shorter than that of the unstructured group (53.61±8.12 min vs. 84.25±16.09 min, P<0.010); the average time in the structured group to complete the first-time course record was shorter than that of the unstructured group (13.20±5.43 min vs. 27.51±8.62 min, P<0.010), and there was a significant statistical difference between the two groups. (2) The overall teaching round quality score of the students in the structured group was significantly higher than that in the unstructured group (84.21±15.61 vs. 70.91±12.28, P<0.010). (3) The score of the medical record writing quality of the structured group was significantly higher than that of the unstructured group (80.25±9.22 vs. 74.22±5.40, P<0.010).ConclusionThe structured electronic medical record specific for pulmonary nodules can effectively improve the training efficiency in the standardized training of surgical residents, improve the clinical ability to deal with pulmonary nodules, improve the integrity and accuracy of key clinical data collected by students, and improve doctor-patient relationship.
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.
ObjectiveTo evaluate the diagnostic value of endobronchial ultrasound technology in combination with LungPoint virtual navigation system for pulmonary peripheral nodules. MethodsRetrospective analysis of 317 patients with peripheral pulmonary nodules who underwent endobronchial ultrasound at the endoscopy center of Shanghai Pulmonary Hospital from January 2021 to March 2022 was used as the study population. They were divided into the endobronchial ultrasound group (EBUS-GS group) and the virtual navigation combined with endobronchial ultrasound group (VBN+EBUS-GS group) according to whether the path was planned with the LungPoint virtual navigation system preoperatively or not. The diagnostic rate, bronchoscopic arrival rate, arrival time, operation time and complications were compared between the EBUS-GS group and the VBN+EBUS-GS group, and the factors associated with the diagnostic rate of endobronchial ultrasound were analyzed. ResultsThere were 101 malignant nodules and 216 benign nodules. The mean size of lung nodules was (1.9±0.7) cm and (1.8±0.6) cm in the EBUS-GS and VBN+EUBS-GS groups, respectively (P>0.05); The time to reach the lesions was 7 (5 - 9) and 4 (3 - 5) min, and the total operation time was 18 (16 - 20) and 16 (14 - 18) min, respectively (P<0.05). The arrival rates of endobronchial ultrasound in the two groups was 82.6% and 98.1% (P<0.05), respectively. The overall diagnostic rate, malignant nodule diagnostic rate and benign nodule diagnostic rate of the two groups were 61.3% vs. 64.8%, 67.9% vs. 68.6% and 57.6% vs. 63.1% respectively (P>0.05). There was one pneumothorax in the EBUS-GS group after examination (0.6%, 1/155). No complications such as hemoptysis or infection occurred in all patients. ConclusionsLungPoint virtual navigation can significantly improve the arrival rate of lesions under endobronchial ultrasound, significantly reduce the arrival time of endobronchial ultrasound to the lesions and the total operation time, which is beneficial to improve the efficiency of clinical examination.
ObjectiveTo explore the safety and feasibility of 3D precise localization based on anatomical markers in the treatment of pulmonary nodules during video-assisted thoracoscopic surgery (VATS).MethodsFrom June 2019 to April 2015, 27 patients with pulmonary nodules underwent VATS in our Hospital were collected in the study, including 3 males and 24 females aged 51.8±13.7 years. The surgical data were retrospectively reviewed and analyzed, such as localization time, localization accuracy rate, pathological results, complication rate and postoperative hospital stay.ResultsA total of 28 pulmonary nodules were localized via this method. All patients received surgery successfully. No mortality or major morbidity occurred. The general mean localization time was 17.6±5.8 min, with an accuracy of 96.4%. The mean diameter of pulmonary nodules was 14.0±8.0 mm with a mean distance from visceral pleura of 6.5±5.4 mm. There was no localization related complication. The mean postoperative hospital stay was 6.7±4.3 d. The routine pathological result showed that 78.6% of the pulmonary nodules were adenocarcinoma.Conclusion3D precise localization based on anatomical markers in the treatment of pulmonary nodules during thoracoscopic surgery is accurate, safe, effective, economical and practical, and it is easy to master with a short learning curve.
Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. MethodsA total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. ResultsThere were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.
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 investigate the preoperative psychological state of patients with pulmonary nodules in order to make the content of the education more "individualized and humanized".MethodsWe conducted a consecutive questionnaire study for 107 patients who were planning to undergo pulmonary resection surgery from May 2018 to July 2018 in our department. There were 54 males and 53 females with an average age of 56.8±11.2 years. The questionnaire content included two parts: personal basic information and 20 questions about surgery, complications, follow-up and hospitalization expense.ResultsThere were 60.7% of the patients diagnosed with pulmonary nodules by CT scan during physical examination, and 52.3% of the patients had strong will to undergo pulmonary surgery to resect nodules; 64.5% of patients wanted doctors to tell them the extent of the disease and whether the tumor could be cured by surgery, and 30.0% of patients concerned whether chief surgeon would complete the whole surgery. The surgery risk and postoperative complications were ignored by patients easily (5.6% and 14.9% respectively). The hospital expenses were not the primary concern of patients. Only 1.9% of patients believed that doctors used nonessentials which deliberately led to increased costs. Network follow-up was accepted by most patients (94.4%).ConclusionIt will contribute to improve preoperative education rationality and effectiveness by understanding true psychological state of patients.
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.