With the development of thin section axial computed tomography scan, the detection rate of pulmonary ground-glass nodules (GGN) continues increasing. GGN has a special natural growth history: pure ground-glass nodules (PGGN) smaller than 10 mm can hold steady for a long term, surgery resection is unnecessary, patients need regular follow up. Larger part solid ground-glass nodules (PSN) with a solid component can be malignant early stage lung cancer, which requires early surgery intervention. Establishment of a standard definition of GGN growth, investments in the long term natural growth history of GGN, validation of the clinical, radiology and genetic risk factors would be beneficial for the management of GGN patients.
Persistent air leaks (PAL) is a common complications after pulmonary operation. Risk factors include operationrelated and general factors. At present， stapling device, staple line buttressing, pleural tent, and pneumoperitoneum are the main methods for management. This review described the definition, risk factors, qualitative and quantitative evaluation, and recent progress in air leak prevention and management.
With the wide utilization of high-resolution computed tomography (HRCT) in the lung cancer screening, patients detected with pulmonary ground-glass nodules (GGNs) have increased over time and account for a large proportion of all thoracic diseases. Because of its less invasiveness and fast recovery, video-assisted thoracoscopic surgery (VATS) is currently the first choice of surgical approach to lung nodule resection. However, GGNs are usually difficult to recognize during VATS, and failure of nodule localization would result in conversion to thoracotomy or extended lung resection. In order to cope with this problem, a series of approaches for pulmonary nodule localization have developed in the last few years. This article aims to summarize the reported methods of lung nodule localization and analyze its corresponding pros and cons, in order to help thoracic surgeons to choose appropriate localization method in different clinical conditions.
Histopathology is still the golden standard for the diagnosis of clinical diseases. Whole slide image (WSI) can make up for the shortcomings of traditional glass slices, such as easy damage, difficult retrieval and poor diagnostic repeatability, but it also brings huge workload. Artificial intelligence (AI) assisted pathologist's WSI analysis can solve the problem of low efficiency and improve the consistency of diagnosis. Among them, the convolution neural network (CNN) algorithm is the most widely used. This article aims to review the reported application of CNN in WSI image analysis, summarizes the development trend of CNN in the field of pathology and makes a prospect.
Systematic lymph nodes dissection has been a standard procedure in lung cancer surgery, while the manipulation of mediastinal lymph nodes for early stage lung cancer remains controversial since surgeons have been weighing the advantages and disadvantages of different methods of lymph node dissection. With an increasing in early stage non-small cell lung cancer patients in recent years, there are more and more intensive studies especially focusing on the mediastinal lymph nodes dissection of clinical stage ⅠA lung cancer. In this review, the lymph nodes management of clinical stage ⅠA non-small cell lung cancer, especially systematic lymph nodes dissection and sampling as well as lobe-specific lymph node dissection, are summarized.
Acute pulmonary embolism (PE) is a common disorder with significant morbidity and mortality in patients who underwent pulmonary ground-glass nodules (GGN) resection. We should make efforts to increase surgeons' awareness of risk factors of PE and their understanding of the effectiveness of prevention strategies. Using the optimal risk assessment model to identify high-risk patients and give them the individualized prophylaxis. Early diagnosis and accurate risk stratification is mandatory to reduce the rates of PE, to decrease health care costs and shorten the length of stay. This article summarizes the risk factors, diagnostic process, risk assessment models, prophylaxis and therapy for the PE patients who underwent GGN resection.
ObjectiveTo establish a machine learning model based on computed tomography (CT) radiomics for preoperatively predicting invasive degree of lung ground-glass nodules (GGNs). MethodsWe retrospectively analyzed the clinical data of GGNs patients whose solid component less than 3 cm in the Department of Thoracic Surgery of Shanghai Pulmonary Hospital from March 2021 to July 2021 and the First Hospital of Lanzhou University from January 2019 to May 2022. The lesions were divided into pre-invasiveness and invasiveness according to postoperative pathological results, and the patients were randomly divided into a training set and a test set in a ratio of 7∶3. Radiomic features (1 317) were extracted from CT images of each patient, the max-relevance and min-redundancy (mRMR) was used to screen the top 100 features with the most relevant categories, least absolute shrinkage and selection operator (LASSO) was used to select radiomic features, and the support vector machine (SVM) classifier was used to establish the prediction model. We calculated the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, positive predictive value to evaluate the performance of the model, drawing calibration and decision curves of the prediction model to evaluate the accuracy and clinical benefit of the model, analyzed the performance in the training set and subgroups with different nodule diameters, and compared the prediction performance of this model with Mayo and Brock models. Two primary thoracic surgeons were required to evaluate the invasiveness of GGNs to investigate the clinical utility of the model. ResultsA total of 400 patients were divided into the training set (n=280) and the test set (n=120) according to the admission criteria. There were 267 females and 133 males with an average age of 52.4±12.7 years. Finally, 8 radiomic features were screened out from the training set data to build SVM model. The AUC, sensitivity and specificity of the model in the training and test sets were 0.91, 0.89, 0.75 and 0.86, 0.92, 0.60, respectively. The model showed good prediction performance in the training set 0-10 mm, 10-20 mm and the test set 0-10 mm, 10-20 mm subgroups, with AUC values of 0.82, 0.88, 0.84, 0.72, respectively. The AUC of SVM model was significantly better than that of Mayo model (0.73) and Brock model (0.73). With the help of this model, the AUC value, sensitivity, specificity and accuracy of thoracic surgeons A and B in distinguishing invasive or non-invasive adenocarcinoma were significantly improved. ConclusionThe SVM model based on radiomics is helpful to distinguish non-invasive lesions from invasive lesions, and has stable predictive performance for GGNs of different sizes and has better prediction performance than Mayo and Brock models. It can help clinicians to more accurately judge the invasiveness of GGNs, to make more appropriate diagnosis and treatment decisions, and achieve accurate treatment.
In recent years, subxiphoid uniportal video-assisted thoracoscopic surgery is one of the most important innovations in the field of mini-invasive thoracic surgery. Because it avoids the injury of intercostal nerve, previous studies have shown that it can significantly reduce the perioperative and long-term incision pain. The operation is technically more difficult, so the selection of patients is more strict compared with the traditional intercostal surgery. Some special surgical techniques are needed during the operation, and special lengthening instruments should be used. We hope that the experience described in this paper will be continuously supplemented and improved with the further development of this technique, and will produce greater reference value.
Lung cancer is a malignant tumor with the highest mortality worldwide, and its early diagnosis and evaluation have a crucial impact on the comprehensive treatment of patients. Early preoperative diagnosis of lung cancer depends on a variety of imaging and tumor marker indicators, but it cannot be accurately assessed due to its high false positive rate. Liquid biopsy biomarkers can detect circulating tumor cells and DNA in peripheral blood by non-invasive methods and are gradually becoming a powerful diagnostic tool in the field of precision medicine for tumors. This article reviews the research progress of liquid biopsy biomarkers and their combination with clinical imaging features in the early diagnosis of lung cancer.