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 investigate the feasibility of using magnetic beads to locate small pulmonary nodules.MethodsTwelve rabbits were randomly divided into two groups, 6 in each group. One group underwent thoracotomy after anesthesia and the other group underwent percutaneous puncture under the guidance of X-ray. One and two cylindrical tracer magnets (magnetic beads) with a diameter of 1 mm and a height of 3 mm were injected adjacent to the imaginary pulmonary nodules in left lung in each group. The magnetic beads beside the imaginary nodules were attracted by a pursuit magnet with a diameter of 9 mm and a height of 19 mm. The effectiveness of localization by magnetic beads were determined by attraction between tracer and pursuit magnets.ResultsAll processes were uneven in 12 rabbits. There was micro hemorrhage and no hematoma in the lung tissue at the injection site of the magnetic beads. When tracked with the pursuit magnets, there was one bead divorce in cases that one bead was injected, but no migration or divorce of the magnetic beads in cases that two magnetic beads were simultaneously injected to localize the small pulmonary nodules.ConclusionThe feasibility of using magnetic beads to locate small pulmonary nodules has been preliminarily verified.
The widespread use of low-dose computed tomography (LDCT) in lung cancer screening has enabled more and more lung nodules to get identified of which more than 20% are multiple pulmonary nodules. At present, there is no guideline or consensus for multiple pulmonary nodules whose management is based primarily on the pulmonary imaging characteristics and associated risk factors. Herein, this review covers the imaging methods, CT appearances and management of multiple pulmonary nodules.
The possibility of solitary pulmonary nodules tending to lung cancer is very high in the middle and late stage. In order to detect the middle and late solitary pulmonary nodules, we present a new computer-aided diagnosis method based on the geometric features. The new algorithm can overcome the disadvantage of the traditional algorithm which can't eliminate the interference of vascular cross section. The proposed algorithm was implemented by multiple clustering of the extracted geometric features of region of interest (ROI) through K-means algorithm, including degree of slenderness, similar degree of circle, degree of compactness and discrete degree. The 232 lung CT images were selected from Lung Image Database Consortium (LIDC) database to do contrast experiment. Compared with the traditional algorithm, the detection rate of the new algorithm was 92.3%, and the error rate was 14.8%. At the same time, the detection rate of the traditional algorithm was only 83.9%, and the error rate was 78.2%. The results show that the proposed algorithm can mark the solitary pulmonary nodules more accurately and reduce the error rate due to precluding the disturbance of vessel section.
In thoracoscopic pulmonary nodule resection surgery, precise preoperative planning is crucial. Artificial intelligence (AI)-assisted three-dimensional (3D) reconstruction technologies have shown great potential in this area. AI-assisted 3D reconstruction technologies can provide accurate, personalized models of the pulmonary vasculature and bronchial anatomy, assisting surgeons in detailed surgical planning and thus enhancing the precision and safety of surgeries. This article reviews the application progress of AI-assisted 3D reconstruction technologies in pulmonary nodule surgery, including their applications in preoperative diagnosis, surgical planning, and intraoperative navigation, as well as the advancements in AI-assisted 3D reconstruction technologies. It analyzes the technical features of all kinds of 3D reconstruction methods, their clinical applications, and the challenges they face.
ObjectiveTo compare the imaging characteristics and surgical methods of pulmonary nodules in the external 1/3 group and internal 2/3 group. MethodsA retrospective analysis of clinical data from patients who underwent thoracoscopic preoperative CT-guided lung nodule localization at the Department of Radiology, the First Affiliated Hospital of Xiamen University from September 2020 to April 2022 was conducted. ResultsA total of 215 patients were enrolled (247 pulmonary nodules), including 70 males and 145 females, with a median age of 48 years. Based on the location of the nodules under CT guidance, those located in the external 1/3 area of the lung were classified into an external 1/3 group, while those located in the middle 1/3 and inner 1/3 areas were classified into an internal 2/3 group. There was no statistical difference between the two groups in terms of general clinical data, nature of pulmonary nodules, distribution of pulmonary nodules in lobes, localization time, or localization complications (P>0.05). However, there were statistical differences in the distance of pulmonary nodules from the pleura [0.6 (0.0-1.9) cm vs. 1.8 (0.0-4.5) cm, P<0.001], size of pulmonary nodules [0.7 (0.2-1.8) cm vs. 1.0 (0.2-2.0) cm, P<0.001], and surgical methods (P=0.002). In the external 1/3 group, 92.1% of nodules underwent thoracoscopic wedge resection, while fewer patients underwent other procedures; in the internal 2/3 group, 77.1% of nodules underwent thoracoscopic wedge resection, and 19.3% underwent segmentectomy. ConclusionThe diameter of pulmonary nodules, the distance of pulmonary nodules from the pleura, and surgical methods differ between the external 1/3 group and internal 2/3 group. Thoracic surgeons can develop more precise surgical plans based on the location and size of pulmonary nodules.
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.
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 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 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.