The incidence of lung cancer has increased significantly during the past decades. Pathology is the gold standard for diagnosis and the corresponding treatment measures selection of lung cancer. In recent years, with the development of artificial intelligence and digital pathology, the researches of pathological image analysis have achieved remarkable progresses in lung cancer. In this review, we will introduce the research progress on artificial intelligence in pathological classification, mutation genes and prognosis of lung cancer. Artificial intelligence is expected to further accelerate the pace of precision pathology.
ObjectiveTo summarize the changes and interaction of the cytokine in severe acute pancreatitis associated lung injury. MethodsThe published literatures at domestic and aboard in recent years about severe acute pancreatitis associated lung injury were collected and reviewed. ResultsThe cytokines had a chain effect, and influenced each other when severe acute pancreatitis with lung injury attacked. ConclusionsRelated cytokines play important roles in severe acute pancreatitis associated lung injury. Researching the related cytokines will contribute to the diagnosis and treatment for severe acute pancreatitis with lung injury.
Objective To establish a simple, valid rat orthotopic left lung transplantation model with the improved operation technique. Methods One hundred and thirty-six male SD rats were randomly divided into donor （n = 68） and recipient （n = 68）, transplantation were performed by using the improved cuff anastomosis technique. Results Time of donor lung perfusion-picking, donor lung vessel cuff anastomosis and recipient vessel anastomosis was 13±2 min, 9±1 min, 10±1 min respectively, the operative time was 60±3 min. In 68 rats of operations, successful rate was 88%（60/68）, anastomotic stoma leak in one rat, lung congestion 3 rats, lung atelectasis 4 rats. The shortest survival time was 1 day, there were 53 rats whose survival time was longer than 12 days. The chest computed tomography showed no atelectasis and blood gas analysis manifested good respiratory function. Conclusion The improved three cuff anastomosis technique offers a simple, valid, cheap and useful method,it can establish rat orthotopic left lung transplantation model successfully.
With the development and improved availability of low-dose computed tomography (LDCT), an increasing number of patients are clinically diagnosed with lung cancer manifesting as ground-glass nodules. Although radical surgery is currently the mainstay of treatment for patients with early-stage lung cancer, traditional anatomic lobectomy and mediastinal lymph node dissection (MLND) are not ideal for every patient. Clinically, it is critical to adopt an appropriate approach to pulmonary lobectomy, determine whether it is necessary to perform MLND, establish standard criteria to define the scope of lymph node dissection, and optimize the decision-making process. Thereby avoiding over- and under-treatment of lung cancer with surgical intervention and achieving optimal results from clinical diagnosis and treatment are important issues before us.
Objective To evaluate the complication rate of CT-guided percutaneous lung biopsy and determine the risk factors. Methods A retrospective investigation of patients with CT-guided percutaneous lung biopsy in Ruijin Hospital, Shanghai Jiao Tong University School of Medicine between2002 and 2009 was performed. The risk factors for complications were determined by multivariate analysis of variables related to patients’demographics, lung lesions, biopsy procedures, and individual radiological features. Results 281 biopsy procedures were enrolled. The total complication rate was 55. 9% with pneumothorax 32. 4% ( 91/281) , hemoptysis 34. 5% ( 97 /281) , and cutaneous emphysema2. 1% ( 6 /281) , and with no mortality.The pneumothorax rate was correlated with lesion location, lesion depth, and number of pleural passes. The bleeding risk was correlated with lesion size, lesion depth, and age. Prediction models for pneumothorax and bleeding were deduced by logistic regression. The pneumothorax model had a sensitivity of 80. 0% and a specificity of 62. 4% . And the bleeding model had a sensitivity of 67. 4% and a specificity of 88. 8% .Conclusions Lesion location, lesion depth, and number of pleural passes were independent risk factors for pneumothorax. Lesion size, lesion depth, and age were independent risk factors for bleeding. The prediction models for pneumothorax and bleeding will helpfully reduce the complication of CT-guided lung biopsy.
ObjectiveTo systematically review the association between inhaled corticosteroids (ICS) and the risk of lung cancer in patients with chronic obstructive pulmonary disease (COPD). MethodsPubMed, EMbase, Web of Science, Cochrane Library, CNKI, WanFang Data and VIP databases were electronically searched to collect cohort studies on the risk of lung cancer in COPD patients using ICS from inception to August 15, 2022. Two reviewers independently screened the literature, extracted data, and evaluated the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4 software. ResultsA total of 8 cohort studies involving 1 184 238 patients were included. The results of meta-analysis showed that ICS use decreased risk of lung cancer in COPD patients (HR=0.68, 95%CI 0.62 to 0.75, P<0.01). The dose of ICS was an influencing factor for the risk of lung cancer in COPD patients and a large dose of ICS could significantly reduce the risk. ConclusionCurrent evidence shows that the use of ICS can reduce the risk of lung cancer in patients with COPD, especially in high-dose patients. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
ObjectiveTo explore the clinical value of three early predictive scale of lung injury (ALI) in patients with high risk of acute lung injury (ALI) after lung cancer surgery.MethodsA convenient sampling method was used in this study. A retrospective analysis was performed on patients with lung cancer underwent lung surgery. The patients were divided into an ALI group and a non-ALI group according to ALI diagnostic criteria. Three kinds of lung injury predictive scoring methods were used, including lung injury prediction score (LIPS), surgical lung injury prediction (SLIP) and SLIP-2. The differences in the scores of the two groups were compared. The correlation between the three scoring methods was also analyzed. The diagnostic value was analyzed by drawing receiver operating characteristic (ROC) curves.ResultsA total of 400 patients underwent lung cancer surgery, and 38 patients (9.5%) developed ALI after operation. Among them, 2 cases progressed to acute respiratory distress syndrome and were treated in intensive care unit. There were no deaths. The predictive scores of the patients in the ALI group were higher than those in the non-ALI group, and the difference was statistically significant (all P<0.001). There was a good correlation between the three scoring methods (allP<0.001). The three scoring methods had better diagnostic value for early prediction of high risk ALI patients after lung cancer surgery and their area under ROC curve (AUC) were larger than 0.8. LIPS score performed better than others, with an AUC of 0.833, 95%CI (0.79, 0.87).ConclusionThree predictive scoring methods may be applied to early prediction of high risk ALI patients after lung cancer surgery, in which LIPS performs better than others.
Objective To examine the effect of endothelial progenitor cell (EPC) on lung ischemia-reperfusion injury (LIRI). Methods Twenty-four recipients were randomized into 3 groups including a sham group, a LIRI group, and an EPC group. Rats in the sham group only received anesthesia. Rats in the LIRI and EPC groups received left lung transplantation and received saline or EPC immediately after reperfusion. The partial pressure of oxygen to fraction of inspiratory oxygen (PaO2/FiO2) ratio, wet-to-dry weight ratio and protein levels in the transplanted lung and inflammation-related factors levels in serum were examined. Histological change of transplanted lung were analyzed. The nuclear factor (NF)-κB in the transplanted lung was detected. Results Compared with the LIRI group, the PaO2/FiO2 ratio dramaticly increased, and the wet-to-dry weight ratio and protein level significantly decreased by EPC after reperfusion. The lung histological injury was attenuated by EPC. The pro-inflammatory factors in serum were down-regulated, whereas IL-10 was up-regulated in the EPC group. The expression of NF-κB was decreased by EPC. Conclusion EPC ameliorated LIRI after lung transplantation. The protection of EPC partly associated with anti-inflammation.
ObjectiveTo investigate the risk factors for lymph node metastasis in resectable lung adenocarcinoma by combining spatial location, clinical, and imaging features, and to construct a lymph node metastasis prediction model. MethodsA retrospective study on patients who underwent chest computed tomography (CT) at the First Affiliated Hospital of Nanjing Medical University from June 2016 to June 2020 and were surgically confirmed to have invasive lung adenocarcinoma with or without lymph node metastasis was conducted. Patients were divided into a positive group and a negative group based on the presence or absence of lymph node metastasis. Clinical and imaging data of the patients were collected, and the independent risk factors for lymph node metastasis in resectable lung adenocarcinoma were analyzed using univariate and multivariate logistic regression. A combined spatial location-clinical-imaging feature prediction model for lymph node metastasis was established and compared with the traditional lymph node metastasis prediction model that does not include spatial location features. ResultsA total of 611 patients were included, with 333 in the positive group, including 172 males and 161 females, with an average age of (58.9±9.7) years; and 278 in the negative group, including 127 males and 151 females, with an average age of (60.1±11.4) years. Univariate and multivariate logistic regression analyses showed that the spatial relationship of the lesion to the lung hilum, nodule type, pleural changes, and serum carcinoembryonic antigen (CEA) levels were independent risk factors for lymph node metastasis. Based on this, the combined spatial location-clinical-imaging feature prediction model had a sensitivity of 91.67%, specificity of 74.05%, accuracy of 87.88%, and area under the curve (AUC) of 0.885. The traditional lymph node metastasis prediction model, which did not include spatial location features, had a sensitivity of 76.40%, specificity of 72.10%, accuracy of 53.86%, and AUC of 0.827. The difference in AUC between the two prediction methods was statistically significant (P=0.026). Compared with the traditional prediction model, the predictive performance of the combined spatial location-clinical-imaging feature prediction model was significantly improved. ConclusionIn patients with resectable lung adenocarcinoma, those with basal spatial location, solid density, pleural changes with wide base depression, and elevated serum CEA levels have a higher risk of lymph node metastasis.
Lung cancer is one of the tumors with the highest incidence rate and mortality rate in the world. It is also the malignant tumor with the fastest growing number of patients, which seriously threatens human life. How to improve the accuracy of diagnosis and treatment of lung cancer and the survival prognosis is particularly important. Machine learning is a multi-disciplinary interdisciplinary specialty, covering the knowledge of probability theory, statistics, approximate theory and complex algorithm. It uses computer as a tool and is committed to simulating human learning methods, and divides the existing content into knowledge structures to effectively improve learning efficiency and being able to integrate computer science and statistics into medical problems. Through the introduction of algorithm to absorb the input data, and the application of computer analysis to predict the output value within the acceptable accuracy range, identify the patterns and trends in the data, and finally learn from previous experience, the development of this technology brings a new direction for the diagnosis and treatment of lung cancer. This article will review the performance and application prospects of different types of machine learning algorithms in the clinical diagnosis and survival prognosis analysis of lung cancer.