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.
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.
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.
As the popularity of thoracoscopic day surgery for pulmonary nodules increases, there is a growing demand among patients for information about the surgical approach, process, and recovery. To enhance patients’ understanding of the surgery, alleviate anxiety, facilitate postoperative recovery, and improve patient satisfaction, the Day Surgery Nursing Committee of Sichuan Tianfu New Area Medical Association has convened experts in the field to discuss the health education model and content for the perioperative period of thoracoscopic pulmonary nodule day surgery, reaching an expert consensus. The consensus underscores the importance of leveraging hospital intelligent information systems and integrating diverse educational methods to provide patients with comprehensive and individualized health education.
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.
Objective To identify the potential factors for psychological burdens and to better understand how the patients’ psychological status affect their treatment preferences. Methods A questionnaire survey was conducted among 996 patients with pulmonary nodules who visited the Thoracic Surgery Clinic of Guangdong Provincial People's Hospital from January to November 2021, including 381 males and 615 females, aged 47.26±11.53 years. A self-administrated questionnaire was used to investigate the sociodemographic and clinical characteristics of the patients, and the Hospital Anxiety and Depression Scale (HADS) was used to evaluate the psychological status of the patients, with a score>7 points of each subscale indicating potential anxiety or depression. Results Among the 996 patients with pulmonary nodules, the incidence of anxiety was 42.4% and the incidence of depression was 26.4%, while the incidence of both anxiety and depression was 24.7%. There was a significant correlation between anxiety and depression (ρ=0.834, P<0.05). Age, purpose of CT examination, number of pulmonary nodules and symptoms were independent factors for anxiety, while symptoms and number of pulmonary nodules were independent factors for depression (P<0.05). For treatment preferences, there was a statistical difference in educational level, symptoms, nodule size and anxiety level (P<0.05). Conclusion Anxiety and depression are common in patients with pulmonary nodules. Symptoms are associated with anxiety and depression, which also make an impact on treatment preferences.
The precise localization of pulmonary nodules has become an important technical key point in the treatment of pulmonary nodules by thoracoscopic surgery, which is a guarantee for safe margin and avoiding removal of too much normal lung parenchyma. With the development of medical technology and equipment, the methods of locating pulmonary nodules are also becoming less trauma and convenience. There are currently a number of methods applied to the preoperative or intraoperative localization of pulmonary nodules, including preoperative percutaneous puncture localization, preoperative transbronchial localization, intraoperative palpation localization, intraoperative ultrasound localization, and localization according to anatomy. The most appropriate localization method should be selected according to the location of the nodule, available equipment, and surgeon’s experience. According to the published literatures, we have sorted out a variety of different theories and methods of localization of pulmonary nodules in this article, summarizing their advantages and disadvantages for references.
ObjectiveTo reveal and demonstrate the hotspots and further research directions in screening technology for early lung cancer, and provide references for the future studies. MethodsResearches related to lung cancer screening from 2011 to 2021 in the Web of Science database were included. Biblioshiny, a bibliometrics program based on R language, was used to perform content analysis and visualization of the included literature information. ResultsResearches related to lung cancer screening were increasing year by year. Six major cooperation groups were formed between countries. The current research hotspots in the field of early lung cancer screening technology mainly focused on the multi-directional fusion of radiographic imaging, liquid biopsy and artificial intelligence. ConclusionLow-dose spiral CT screening is still the most important and mainstream method for the screening of early lung cancer at present. The combination and integration of artificial intelligence with various screening methods and the innovation of novel testing and diagnostic equipment are the current research hotspots and the future research trend in this field.
Objective To investigate the diagnostic value of tumor marker combining the probability of malignancy model in pulmonary nodules. Methods A total of 117 patients with pulmonary nodules diagnosed between January 2013 and January 2016 were retrospectively analyzed. Seventy-six cases of the patients diagnosed with cancer were selected as a lung cancer group. Forty-one cases of the patients diagnosed with benign lesions were selected as a benign group. Tumor markers were detected and the probability of malignancy were calculated. Results The positive rate of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), neuron-specific enolase (NSE), cytokeratin marker (CYFRA21-1), and the probability of malignancy in the lung caner group were significantly higher than those of the benign group. The sensitivity, specificity, and accuracy of CEA, CA125, NSE, CYFRA21-1 combined detection were 72.37%, 73.17%, and 72.65%, respectively. Using the probability of malignancy model to calculate each pulmonary nodules, the area under ROC curve was 0.743 which was higher than 0.7; and 28.5% was selected as cut-off value based on clinical practice and ROC curve. The sensitivity, specificity, and accuracy of the probability of malignancy model were 63.16%, 78.05%, and 68.68%, respectively. The sensitivity, specificity, and accuracy of tumor marker combining the probability of malignancy model were 93.42%, 68.29%, and 92.31%, respectively. The sensitivity and accuracy of tumor marker combining the probability of malignancy model were significantly improved compared with tumor markers or the probability of malignancy model single detection (P<0.01). Conclusion The tumor marker combining the probability of malignancy model can improve the sensitivity and accuracy in diagnosis of pulmonary nodules.
ObjectiveTo evaluate the learning curve of CT-guided medical glue localization for pulmonary nodule before video-assisted thoracic surgery (VATS). MethodsThe clinical data of the patients with pulmonary nodules who underwent CT-guided medical glue localization before VATS in our hospital from July 2018 to March 2021 were retrospectively analyzed. The patients were divided into 3 groups: a group A (from July 2018 to August 2019), a group B (from September 2019 to June 2020) and a group C (from July 2020 to March 2021). The localization time, morbidity, complete resection rate and other indexes were compared among the three groups. ResultsA total of 77 patients were enrolled, including 24 males and 53 females aged 57.4±10.1 years. There were 25 patients in the group A, 21 patients in the group B, and 31 patients in the group C. 77 pulmonary nodules were localized. There was no significant difference among the groups in the basic data (P＞0.05). The localization time in the group C was 10.6±2.0 min, which was statistically shorter than that in the group A (15.4±4.4 min) and group B (12.9±4.3 min) (P＜0.01). The incidence of complications in the group C was lower than that in the group A and group B (25.8% vs. 52.0% vs. 47.6%, P=0.04). The success rate of localization of the three groups was not statistically different (P=0.12). ConclusionThere is a learning curve in CT-guided medical glue localization for single pulmonary nodule before VATS. After the first 46 cases, the operation time can be shortened, and the incidence of complications can be decreased.