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.
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.
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 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 assess the feasibility of 3D digital lung software used in preoperative planning of patients with multiple pulmonary nodules and poor pulmonary function. MethodsFive patients with multiple pulmonary nodules in the left lung, meanwhile with a history of single lung lobectomy in the right lung were included in our hospital between June and December 2015. There were 4 males and 1 female at an average age of 50.4±2.6 years. A 320-slice volumetric CT scanner was used to the CT angiography (CTA) of the pulmonary artery. The data of CT images were imported into the 3D digital lung software that was researched and developed by Xiamen QiangBen Science and Technology Company. The 3D reconstruction of digital virtual lung was completed by this software based on those data. At the same time the soft-ware completed the automatic segmentation of the lung based on the pulmonary artery system and the 3D reconstruction of the pulmonary nodules. The 3D digital lung software calculated the volume proportion of the intended removal (segm-ental lesions) to the whole lung, estimated the effect of surgery on forced expired volume in one second (FEV1), and the patient's tolerance ability to surgery. After the preoperative planning, the patients received multiple pulmonary segmental/subsegmental resection under the general anesthesia by video-assisted thoracoscopic surgery (VATS). ResultsThe 3d reconstruction of the pulmonary arteries reached 5 levels in 5 patients. And the software automatically identified out the lung segment/subsegment to show the lung nodules of lung segment/subsegment. The preselection lung volume of 5 patients accounted for 14.00%-27.00% of total lung volume. The software estimated FEV1 as 1.16-1.46 L which can tolerate the operation. The 5 patients were successfully performed surgery of multiple pulmonary segmental/subsegmental resection under the general anesthesia by VATS. The software located lung nodules from the resection of pulmonary segments during operation immediately. Then we sent them to the rapid pathological examination for diagnosis. After operation, the patients recovered well, and had no respiratory insufficiency. Hospitalization day was 4 days. ConclusionThe 3D digital pulmonary software can not only automatically identify the pulmonary segments, precisely position the pulmonary nodule, show the relationship among the target pulmonary segments artery, vein, bronchus and the surroun-ding artery, vein, and bronchus, but also calculate the volume of the pulmonary segments, estimate the impact of the pulmonary segmentectomy on the FEV1. It is useful for precise evaluation of the tolerant capacity of multiple pulmonary nodules in patients with unstaged multiple pulmonary segments.
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.
ObjectiveTo evaluate the clinical feasibility and safety of CT-guided percutaneous microwave ablation for peripheral solitary pulmonary nodules.MethodsThe imaging and clinical data of 33 patients with pulmonary nodule less than 3 cm in diameter treated by CT-guided microwave ablation treatment (PMAT) in our hospital from July 2018 to December 2019 were retrospectively analyzed. There were 21 males and 12 females aged 38-90 (67.6±13.4) years. Among them, 26 patients were confirmed with lung cancer by biopsy and 7 patients were clinically considered as partial malignant lesions. The average diameter of 33 nodules was 0.6-3.0 (1.8±0.6) cm. The 3- and 6-month follow-up CT was performed to evaluate the therapy method by comparing the diameter and enhancement degree of lesions with 1-month CT manifestation. Short-term treatment analysis including complete response (CR), partial response (PR), stable disease (SD) and progressive disease (PD) was calculated according to the WHO modified response evaluation criteria in solid tumor (mRECIST) for short-term efficacy evaluation. Eventually the result of response rate (RR) was calculated. Progression-free survival was obtained by Kaplan–Meier analysis.ResultsCT-guided percutaneous microwave ablation was successfully conducted in all patients. Three patients suffered slight pneumothorax. There were 18 (54.5%) patients who achieved CR, 9 (27.3%) patients PR, 4 (12.1%) patients SD and 2 (6.1%) patients PD. The short-term follow-up effective rate was 81.8%. Logistic analysis demonstrated that primary and metastatic pulmonary nodules had no difference in progression-free time (log-rank P=0.624).ConclusionPMAT is of high success rate for the treatment of solitary pulmonary nodules without severe complications, which can be used as an effective alternative treatment for nonsurgical candidates.
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.
ObjectiveTo explore and analyze the risk factors of pleural invasion in patients with small nodular type stage ⅠA pulmonary adenocarcinoma.MethodsFrom June 2016 to December 2017, 168 patients with small nodular type stage ⅠA pulmonary adenocarcinoma underwent surgical resection in the First Affiliated Hospital of Nanjing Medical University. There were 59 males and 109 females aged 58.7±11.5 years ranging from 28 to 83 years. The clinical data were analyzed retrospectively. Single factor Chi-square test and multivariate logistic regression were used to analyze the independent risk factors of pleural invasion.ResultsAmong 168 patients, 20 (11.9%) were pathologically confirmed with pleural invasion and 148 (88.1%) with no pleural invasion. Single factor analysis revealed significant differences (P<0.05) in nodule size, nodule status, pathological type, relation of lesion to pleura (RLP), distance of lesion to pleura (DLP), epidermal growth factor receptor (EGFR) mutation between patients with and without pleural invasion in stage ⅠA pulmonary adenocarcinoma. Logistic multivariate regression analysis showed that significant differences of nodule size, nodule status, RLP, DLP and EGFR mutation existed between the two groups (P<0.05), which were independent risk factors for pleural invasion.ConclusionImageological-pathological-biological characteristics of patients with small nodular type stage ⅠA pulmonary adenocarcinoma are closely related to pleural invasion. The possibility of pleural invasion should be evaluated by combining these parameters in clinical diagnosis and treatment.
Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. MethodsPatients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. ResultsA total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.