Objective The management of pulmonary nodules is a common clinical problem, and this study constructed a nomogram model based on FUT7 methylation combined with CT imaging features to predict the risk of adenocarcinoma in patients with pulmonary nodules. Methods The clinical data of 219 patients with pulmonary nodules diagnosed by histopathology at the First Affiliated Hospital of Zhengzhou University from 2021 to 2022 were retrospectively analyzed. The FUT7 methylation level in peripheral blood were detected, and the patients were randomly divided into training set (n=154) and validation set (n=65) according to proportion of 7:3. They were divided into a lung adenocarcinoma group and a benign nodule group according to pathological results. Single-factor analysis and multi-factor logistic regression analysis were used to construct a prediction model in the training set and verified in the validation set. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model, the calibration curve was used to evaluate the consistency of the model, and the clinical decision curve analysis (DCA) was used to evaluate the clinical application value of the model. The applicability of the model was further evaluated in the subgroup of high-risk CT signs (located in the upper lobe, vascular sign, and pleural sign). Results Multivariate logistic regression analysis showed that female, age, FUT7_CpG_4, FUT7_CpG_6, sub-solid nodules, lobular sign and burr sign were independent risk factors for lung adenocarcinoma (P<0.05). A column-line graph prediction model was constructed based on the results of the multifactorial analysis, and the area under the ROC curve was 0.925 (95%CI 0.877 - 0.972 ), and the maximum approximate entry index corresponded to a critical value of 0.562, at which time the sensitivity was 89.25%, the specificity was 86.89%, the positive predictive value was 91.21%, and the negative predictive value was 84.13%. The calibration plot predicted the risk of adenocarcinoma of pulmonary nodules was highly consistent with the risk of actual occurrence. The DCA curve showed a good clinical net benefit value when the threshold probability of the model was 0.02 - 0.80, which showed a good clinical net benefit value. In the upper lobe, vascular sign and pleural sign groups, the area under the ROC curve was 0.903 (95%CI 0.847 - 0.959), 0.897 (95%CI 0.848 - 0.945), and 0.894 (95%CI 0.831 - 0.956). Conclusions This study developed a nomogram model to predict the risk of lung adenocarcinoma in patients with pulmonary nodules. The nomogram has high predictive performance and clinical application value, and can provide a theoretical basis for the diagnosis and subsequent clinical management of pulmonary nodules.
Objective To explore the diagnosis and treatment strategies for elderly patients with ground-glass opacity (GGO). MethodsThe imaging features and postoperative pathological findings of the elderly patients with pulmonary GGO receiving surgery in our hospital from 2017 to 2019 were retrospectively analyzed. The patients were divided into an elderly patient group and a non-elderly patient group based on their age. Results Finally 575 patients were included in the study. There were 281 elderly patients, including 83 males and 198 females, with an average age of (67.0±5.3) years. There were 294 non-elderly patients, including 88 males and 206 females, with an average age of (49.1±7.3) years. Compared with the non-elderly patients, elderly GGO patients showed the following distinct clinical features: long observation time for lesions (P=0.001), high proportion of rough edges of GGO (P<0.001), significant pleural signs (P＜0.001) and bronchial signs (P＜0.001), and high proportion of type Ⅱ-Ⅳ GGO (P<0.001), lobectomy type (P=0.013), and invasive lesions reported in postoperative pathology (P<0.001). There was no statistical difference in the average hospital stay between the two groups (P=0.106). Multivariate logistic regression analysis showed that GGO diameter and GGO type were the main factors affecting the operation. Observation time, GGO diameter, GGO type and pleural signs were the main influencing factors for postoperative pathological infiltrative lesions. The cut-off value of GGO diameter in predicting infiltrating lesions was 10.5 mm in the elderly patients group. Conclusion The size and type of GGO are important factors in predicting invasive lesions and selecting surgical methods. Elderly patients with radiographic manifestations of type Ⅱ-Ⅳ GGO lesions with a diameter greater than 10.5 mm should be closely followed up.
ObjectiveTo introduce the application of mixed reality technique to the preoperative and intraoperative pulmonary nodules surgery.MethodsOne 49-year female patient with multiple nodules in both lobes of the lung who finally underwent uniportal thoracoscopic resection of superior segment of left lower lobe and wedge resection of left upper lobe was taken as an example. The Mimics medical image post-processing software was used to reconstruct the patient's lung image based on the DICOM data of the patient's chest CT image before the surgery. The three-dimensional reconstructed image data was imported into the HoloLens glasses, and the preoperative discussions were conducted with the assistance of mixed reality technology to formulate the surgical methods, and the preoperative conversation with the patients was also conducted. At the same time, mixed reality technology was used to guide the surgery in real time.ResultsMixed reality technology can clearly pre-show the important anatomical structures of blood vessels, trachea, lesions and their positional relationship. With the help of mixed reality technology, the operation went smoothly. The total operation time was 49 min, the precise dorsal resection time was 27 min, and the intraoperative blood loss was about 39 mL. The patient recovered well and was discharged from hospital smoothly after surgery.ConclusionMixed reality technology has certain application value before and during the surgery for pulmonary nodules. The continuous maturity of this technology and its further application in clinics will not only bring a new direction to the development of thoracic surgery, but also provide a wide prospect.
The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.
ObjectiveTo explore the clinical issues associated with video-assisted pulmonary segmentectomy and to provide reference for better implementation of thoracoscopic pulmonary segmentectomy and reduction of perioperative complications through analyzing the clinical results of thoracoscopic segmentectomy.MethodsThe clinical data of 90 patients who planned to undergo thoracoscopic segmentectomy in our department from October 2017 to December 2019 were retrospectively analyzed, including 35 males with an average age of 60.34±9.40 years and 55 females with an average age of 56.09±12.11 years. The data including lung nodule number, benign or malignant, preoperative location by Hookwire, preoperative planning and actual implementation, operation time, intraoperative blood loss, postoperative drainage volume and time of drainage tube removal, postoperative hospital stay and complications were collected and analyzed.ResultsAmong the 90 patients, 38 were preoperatively positioned by Hook-wire, 52 were directly operated on; 87 were completed under thoracoscopic surgery among whom 3 underwent passive lobectomy after segmentectomy under thoracoscopic surgery, and 3 were converted to thoracotomy among whom 1 underwent lobectomy. Operation time was 198.58±56.42 min, intraoperative blood loss was 129.78±67.51 mL, lymph node samples were 6.43±1.41, drainage time was 2.98±1.25 d, the amount of postoperation drainage was 480.00±262.00 mL, hospital stay was 7.60±2.38 d. In all patients, 73 had single nodules and 17 had multiple nodules. Totally 113 pulmonary nodules were resected, 14 (12.39%) were benign nodules and 99 (87.61%) were malignant nodules. There was no perioperative death or serious complications.ConclusionFor those pulmonary parenchymal nodules which meet the indications, it is feasible to perform thoracoscopic anatomic pulmonary segmentectomy according to preoperative thin-slice CT and three-dimensional computed tomography-bronchography and angiography (3D-CTBA) reconstruction results. Preoperative Hookwire localization can ensure effective edge resection and reduce unplanned lobotomy for intersegmental nodules and non-palpable peripheral pure ground-glass nodules.
ObjectiveTo explore the clinical value of three-dimensional computed tomography bronchography and angiography (3D-CTBA) in robotic lung segmentectomy.MethodsA non-randomized control study was performed and continuously enrolled 122 patients who underwent robotic lung segmentectomy in our hospital from January 2019 to January 2020. 3D-CTBA was performed before operations in 53 patients [a 3D-CTBA group, including 18 males, 35 females, with a median age of 52 (26-69) years] and not performed in the other 69 patients [a traditional group, including 23 males, 46 females, with a median age of 48 (30-76) years]. The clinical data of the patients were compared between the two groups.ResultsAll the patients were successfully completed the surgery and recovered from hospital, with no perioperative death. The baseline characteristics of the patients were not significantly different between the two groups (P>0.05). No significant difference was found in the operative time [120 (70-185) min vs. 120 (45-225) min, P=0.801], blood loss [50 (20-300) mL vs. 30 (20-400) mL, P=0.778], complications rate (17.0% vs. 11.6%, P=0.162), postoperative hospital stay [7 (4-19) d vs. 7 (3-20) d, P=0.388] between the two groups. In the 3D-CTBA group, 5 (9.4%) patients did not find nodules after segmentectomy, and only 1 (1.9%) of them needed lobectomy, but in the traditional group, 8 (11.6%) patients did not find nodules and had to carry out lobectomy, the difference was statistically significant (P<0.05). The follow-up time was 10 (1-26) months, and during this period, there was no recurrence, metastasis or death in the two groups.Conclusion3D-CTBA is helpful for accurate localization of nodules and reasonable surgical planning before operations, and reducing wrong resections in segmentectomy, without increasing the operation time, blood loss and complications. It is safe and effective in anatomical lung segmentectomy.
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.
Solitary pulmonary nodule (SPN) is defined as a rounded opacity≤3 cm in diameter surrounded by lung parenchyma. The majority of smokers who undergo thin-section CT have SPNs, most of which are smaller than 7 mm. In the past, multiple follow-up examinations over a two-year period, including CT follow-up at 3, 6, 12, 18, and 24 months, were recommended when such nodules are detected incidentally. This policy increases radiation burden for the affected population. Nodule features such as shape, edge characteristics, cavitation, and location have not yet been found to be accurate for distinguishing benign from malignant nodules. When SPN is considered to be indeterminate in the initial exam, the risk factor of the patients should be evaluated, which includes patients' age and smoking history. The 2005 Fleischner Society guideline stated that at least 99% of all nodules 4 mm or smaller are benign; when nodule is 5-9 mm in diameter, the best strategy is surveillance. The timing of these control examinations varies according to the nodule size (4-6, or 6-8 mm) and the type of patients, specifically at low or high risk of malignancy concerned. Noncalcified nodules larger than 8 mm diameter bear a substantial risk of malignancy, additional options such as contrast material-enhanced CT, positron emission tomography (PET), percutaneous needle biopsy, and thoracoscopic resection or videoassisted thoracoscopic resection should be considered.
Objective To identify the needs of patients undergoing pulmonary nodule day surgery during the perioperative medical interaction experience under the enhanced recovery after surgery mode based on the patient journey map, and to provide references for optimizing the diagnosis and treatment experience of these patients and the patient-centered medical support. Methods Using the descriptive qualitative research method, 15 patients who underwent thoracoscopic pulmonary nodule resection or pulmonary wedge resection in the day surgery ward of Huadong Hospital, Fudan University between January and June 2024 were selected for semi-structured interviews. The data were analyzed using the content analysis method, and the interactive map of the patient’s medical seeking experience was drawn. Results According to the medical seeking process and perioperative stages, the interactive journey map of the patient’s medical seeking experience was formed. The medical seeking experience and health support needs of patients undergoing pulmonary nodule day surgery were classified and summarized into themes such as medical consultation cues, screening of medical information, multidisciplinary assessment, medical-patient cognitive differences, rehabilitation exercises and sports, nutritional management, doctor-patient communication, social adaptation, and emotional expression from four links: interaction touchpoints, information flow, communication methods, and feedback mechanisms. Conclusions Patients undergoing pulmonary nodule day surgery experience the perioperative medical seeking process under the enhanced recovery after surgery mode, with a shortened hospitalization period but complex postoperative health management needs. The interaction touchpoints generated during the experience are intertwined and changeable, and the health support needs required vary at different stages of intervention points. Optimizing the touchpoints can ensure that patients receive efficient medical support when needed, thereby enhancing the patient experience, improving the medical seeking feeling, and ensuring that high-quality nursing services are implemented throughout all stages.
ObjectiveTo reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. MethodsPublications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. ResultsA marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. ConclusionOver the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.