Lung cancer, as one of the malignant tumors with the fastest increasing morbidity and mortality in the world, has a serious impact on people's health. With the continuous advancement of medical technology, more and more medical methods are applied to lung cancer screening, which has gradually increased the detection rate of early lung cancer. At present, the standard operation for the treatment of early non-small cell lung cancer (NSCLC) is still lobectomy and mediastinal lymph node dissection. There is a growing trend to use segmentectomy for the treatment of early stage lung cancer. Anatomical segmentectomy not only removes the lesions to the maximum extent, but also preserves the lung function to the greatest extent, and its advantages are also obvious. This article reviews the progress of anatomical segmentectomy in the treatment of early NSCLC.
Objective To investigate the accuracy of 18F-FDG positron emission tomography/computed tomography (PET/CT) combined with CT three-dimensional reconstruction (CT-3D) in the differential diagnosis of benign and malignant pulmonary nodules. Methods The clinical data of patients who underwent pulmonary nodule surgery in the Department of Thoracic Surgery, Northern Jiangsu People's Hospital from July 2020 to August 2021 were retrospectively analyzed. The preoperative 18F-FDG PET/CT and chest enhanced CT-3D and other imaging data were extracted. The parameters with diagnostic significance were screened by the area under the receiver operating characteristic (ROC) curve (AUC). Three prediction models, including PET/CT prediction model (MOD PET), CT-3D prediction model (MOD CT-3D), and PET/CT combined CT-3D prediction model (MOD combination), were established through binary logistic regression, and the diagnostic performance of the models were validated by ROC curve. Results A total of 125 patients were enrolled, including 57 males and 68 females, with an average age of 61.16±8.57 years. There were 46 patients with benign nodules, and 79 patients with malignant nodules. A total of 2 PET/CT parameters and 5 CT-3D parameters were extracted. Two PET/CT parameters, SUVmax≥1.5 (AUC=0.688) and abnormal uptake of hilar/mediastinal lymph node metabolism (AUC=0.671), were included in the regression model. Among the CT-3D parameters, CT value histogram peaks (AUC=0.694) and CT-3D morphology (AUC=0.652) were included in the regression model. Finally, the AUC of the MOD PET was verified to be 0.738 [95%CI (0.651, 0.824)], the sensitivity was 74.7%, and the specificity was 60.9%; the AUC of the MOD CT-3D was 0.762 [95%CI (0.677, 0.848)], the sensitivity was 51.9%, and the specificity was 87.0%; the AUC of the MOD combination was 0.857 [95%CI (0.789, 0.925)], the sensitivity was 77.2%, the specificity was 82.6%, and the differences were statistically significant (P<0.001). Conclusion 18F-FDG PET/CT combined with CT-3D can improve the diagnostic performance of pulmonary nodules, and its specificity and sensitivity are better than those of single imaging diagnosis method. The combined prediction model is of great significance for the selection of surgical timing and surgical methods for pulmonary nodules, and provides a theoretical basis for the application of artificial intelligence in the pulmonary nodule diagnosis.
ObjectiveBy applying the mutual corroboration in the diagnosis, we aimed to improve the accuracy of preoperative imaging diagnosis, select the appropriate timing of operation and guide the follow-up time for patients with pulmonary nodules.MethodsClinical data of 1 368 patients with pulmonary nodules undergoing surgical treatment in our department from July 2016 to October 2019 were summarized. There were 531 males and 837 females at age of 44 (21-67) years. The intraoperative findings, images and pathology were classified and analyzed. The imaging pathology and pathological changes of pulmonary nodules were shown as a dynamic process through mutual collaboration and interaction.ResultsOf 1 368 patients with pulmonary nodules, 376 (27.5%) were pure ground-glass nodules, 729 (53.3%) were mixed ground-glass nodules and 263 (19.2%) were solid nodules. Among the pure ground-glass nodules, adenocarcinoma in situ (AIS) accounted for the highest proportion (156 patients), followed by microinvasive adenocarcinoma (MIA, 90 patients), atypical adenomatous hyperplasia (AAH, 85 patients), and benign tumors (20 patients). Among mixed ground-glass nodules, 495 patients were invasive adenocarcinoma (IA) and 207 patients of MIA. In solid nodules, patients were characterized by pathology of either IA (213 patients) or benign tumors (50 patients), and no patient was featured by AAH, AIS or MIA.ConclusionThe mutual collaboration and interaction can improve the accuracy of preoperative diagnosis of pulmonary nodules, and it supports the choice of operation timing and the judgment of follow-up time.
Lung cancer has brought tough challenges to human health due to its high incidence and mortality rate in the current practice. Nowadays, computed tomography (CT) imaging is still the most preferred diagnostic tool for early screening of lung cancer. However, a great challenge brought from accumulative CT imaging data can not meet the demand of the current clinical practice. As a novel kind of artificial intelligence technique aimed to deal with medical images, a computer-aided diagnosis has been found to provide useful auxiliary information, attenuate the workload of doctors, and significantly improve the efficiency and accuracy for clinical diagnosis of lung cancer. Therefore, an effective combination of computer-aided techniques and CT imaging has increasingly become an active area of investigation in early diagnosis of lung cancer. This review aims to summarize the latest progress on the diagnostic value of computer-aided technology with regard to early stage lung cancer from the perspectives of machine learning and deep learning.
ObjectiveTo analyze the independent risk factors affecting complications of preoperative CT-guided Hookwire localization of pulmonary nodules, and establish and validate a nomogram risk prediction model. MethodsClinical data of patients who underwent thoracoscopic lung surgery with preoperative CT-guided Hookwire localization at the Department of Thoracic Surgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University from January 2023 to October 2023 were collected. Patients were divided into a complication group and a non-complication group according to whether they had complications. The clinical data of the two groups were compared by univariate analysis and multivariate binary logistic regression analysis to determine the independent risk factors causing complications during localization, and a nomogram prediction model was established. The discrimination of the model was evaluated by receiver operating characteristic (ROC) curve, and the consistency between predicted events and actual results was evaluated by calibration curve. ResultsA total of 300 patients were included, including 143 males and 157 females, aged 24-68 (46.00±22.81) years. Univariate analysis showed that there were statistically significant differences in age, number and location of nodules, preoperative anxiety score, history of chronic obstructive pulmonary disease (COPD), number of needle adjustments, pain score, and distance between the tip of the localization needle and the visceral pleura between the two groups (P<0.05). Multivariate binary logistic regression analysis suggested that pain score [OR=1.253, 95%CI (1.094, 1.434), P=0.001], age [OR=1.020, 95%CI (1.000, 1.042), P=0.049], history of COPD [OR=3.281, 95%CI (1.751, 6.146), P<0.001], number of nodules [OR=1.667, 95%CI (1.221, 2.274), P=0.001], preoperative anxiety score [OR=1.061, 95%CI (1.031, 1.092), P<0.001], number of needle adjustments [OR=1.832, 95%CI (1.263, 2.658), P=0.001], and distance between the needle tip and the visceral pleura [OR=1.759, 95%CI (1.373, 2.254), P<0.001] were associated with localization complications. The area under the ROC curve for the modeling group was 0.825, and that for the validation group was 0.845. Hosmer-Lemeshow test showed that there was no statistically significant difference between the ideal curve of the model fitting curve and that of the modeling group and internal validation group, indicating good goodness of fit (χ2=6.488, P=0.593). ConclusionAdvanced age, multiple nodules, preoperative anxiety, history of COPD, multiple needle adjustments, severe pain during localization, and long distance between the tip of the localization needle and the visceral pleura are independent risk factors for complications of lung nodule localization, and the prediction model based on these factors has good predictive performance.
ObjectivesTo investigate the influence of scanning parameters (tube voltages and tube currents) on image quality and corresponding radiation doses with simulated lung nodules in chest CT.MethodsThe anthropomorphic chest phantoms with 12 simulated, randomly placed nodules of different diameters and densities in the chest were scanned by different scanning parameters. The detection rate, degree of nodular deformation, image quality (with both subjective and objective evaluation) and the corresponding radiation doses were recorded and evaluated, and the correlation between nodule detection rate, degree of nodular deformation, radiation dose and image quality using different scanning parameters was analyzed.ResultsThe image quality improved with the increase of tube voltage and tube current (P<0.05). When the tube current was constant, the CT values of the vertebral decreased gradually with the increase of tube voltages (P<0.05); however, significant difference was not detected in CT values of the lung field (P>0.05). When the tube current was 100 mAs, the lung nodules with CT values of +100 HU and −630 HU showed statistical difference when using different tube voltage (P<0.05); but there was no significant difference in nodules of −800 HU (P=0.57). When tube voltage was 100 kV and 120 kV each, it was possible to detect all lung nodules with a detection rate of 100%. The detection rate was 33% and 66% in 3 mm diameter when the tube current was 80 kV/15 mA and 80 kV/20 mA, respectively. The nodules deformation in nodules with a CT value of −630 HU and diameter less than 5 mm was the most prominent (P<0.05). After analyzing the relationship between image quality and radiation doses using different tube voltages, we established a system of correlation equations: 80 kV: Y=2.625X+0.038; 100 kV: Y=14.66X+0.158; 120 kV: Y=18.59X+0.093.ConclusionsThe image quality improves with the increase of tube current and tube voltage, as well as the corresponding radiation doses. By reducing the tube voltage and increasing the tube current appropriately, the radiation doses can be reduced. Follow-up CT examination of pulmonary ground glass nodules should apply the same tube voltage imaging parameters, so as to effectively reduce the measurement error of nodule density and evaluate the change of nodules more accurately.
Lung cancer is the malignant tumor with the highest incidence and mortality rate in China. Early diagnosis and treatment are key to improving the survival rate and reducing the mortality rate for lung cancer patients. This article introduces the integrated management model for patients with pulmonary nodules/lung cancer developed by West China Hospital of Sichuan University based on “internet plus” and health service team of treatment, nursing, and care. The Integrated Care Management Center has established a multidisciplinary team, using internet platforms and artificial intelligence tools to develop a whole life cycle health service system for patients with pulmonary nodules/lung cancer, which is from the screening of high-risk population for lung cancer, the intelligent risk stratification and follow-up management of pulmonary nodules, the subsequent standardized diagnosis and treatment of lung cancer and comorbidity management, until the patient’s demise. After the implementation of this model, the malignancy rate in surgically treated patients with pulmonary nodules reached 85.08%, and the patient satisfaction score was 95.76. This model provides a new idea and reference for the innovation of chronic disease service model and the management of pulmonary nodules and lung cancer.
Surgical resection is the only radical method for the treatment of early-stage non-small cell lung cancer. Intraoperative frozen section (FS) has the advantages of high accuracy, wide applicability, few complications and real-time diagnosis of pulmonary nodules. It is one of the main means to guide surgical strategies for pulmonary nodules. Therefore, we searched PubMed, Web of Science, CNKI, Wanfang and other databases for nearly 30 years of relevant literature and research data, held 3 conferences, and formulated this consensus by using the Delphi method. A total of 6 consensus contents were proposed: (1) Rapid intraoperative FS diagnosis of benign and malignant diseases; (2) Diagnosis of lung cancer types including adenocarcinoma, squamous cell carcinoma, others, etc; (3) Diagnosis of lung adenocarcinoma infiltration degree; (4) Histological subtype diagnosis of invasive adenocarcinoma; (5) The treatment strategy of lung adenocarcinoma with inconsistent diagnosis on degree of invasion between intraoperative FS and postoperative paraffin diagnosis; (6) Intraoperative FS diagnosis of tumor spread through air space, visceral pleural invasion and lymphovascular invasion. Finally, we gave 11 recommendations in the above 6 consensus contents to provide a reference for diagnosis of pulmonary nodules and guiding surgical decision-making for peripheral non-small cell lung cancer using FS, and to further improve the level of individualized and precise diagnosis and treatment of early-stage lung cancer.
ObjectiveTo evaluate the feasibility and clinical value of robot-assisted lung segmentectomy through anterior approach.MethodsWe retrospectively analyzed the clinical data of 77 patients who underwent robotic lung segmentectomy through anterior approach in our hospital between June 2018 to October 2019. There were 22 males and 55 females, aged 53 (30-71) years. Patients' symptoms, general conditions, preoperative imaging data, distribution of resected lung segments, operation time, bleeding volume, number of lymph node dissected, postoperative duration of chest tube insertion, drainage volume, postoperative hospital stay, postoperative complications, perioperative death and other indicators were analyzed.ResultsAll operations were successfully completed. There was no conversion to thoracotomy, serious complications or perioperative death. The postoperative pathology revealed early lung cancer in 48 patients, and benign tumors in 29 patients. The mean clinical parameters were following: the robot Docking time 1-30 (M=4) min, the operation time 30-170 (M=76) min, the blood loss 20-400 (M=30) mL, the drainage tube time 2-15 (M=4) days, the drainage fluid volume 200-3 980 (M=780) mL and the postoperative hospital time 3-19 (M=7) days.ConclusionRobotic lung segmentectomy through anterior approach is a safe and convenient operation method for pulmonary nodules.
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.