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.
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.
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.
ObjectiveTo explore the feasibility and clinical value of free-of-puncture positioning in three-dimension-guided anatomical segmentectomy for ground-glass nodule (GGN) compared with percutaneous positioning.MethodsClinical data of 268 enrolled patients undergoing anatomical pulmonary segmentectomy from October 2018 to June 2019 were retrospectively collected, including 75 males and 193 females with an average age of 56.55±12.10 years. The patients were divided into two groups, including a percutaneous positioning group (n=89) and a free-of-puncture positioning group (n=179). Perioperative data of the two groups were compared.ResultsThe average CT scan times of the percutaneous positioning group was 3.01±0.98 times, and the numerical rating scale (NRS) score of puncture pain was 3.98±1.61 points. Pulmonary compression pneumothorax (≥30%) occurred in 7 (7.87%) patients and intercostal vascular hemorrhage occurred in 8 (8.99%) patients after puncture. Lung nodules were successfully found and removed in both groups. There was no statistically significant difference between the two groups in the location of nodules (P=0.466), operation time (151.83±39.23 min vs. 154.35±33.19 min, P=0.585), margin width (2.07±0.35 cm vs. 1.98±0.28 cm, P=0.750), or the number of excised subsegments (2.83±1.13 vs. 2.73±1.16, P=0.530).ConclusionAnatomical segmentectomy with three-dimensional navigation avoids the adverse consequences of puncture, which has the same clinical efficacy and meets the requirements of oncology compared with percutaneous positioning. The free-of-puncture positioning method can be used for GGN located in the central region of pulmonary segment/subsegment or adjacent to intersegment veins instead of percutaneous positioning.
The majority of incidentally found and screen-detected lung cancer is manifested as ground-glass nodule (GGN), which is more likely to be detected in the young people, women and non-smokers. An appropriate management strategy for GGN can not only reduce the mortality of lung cancer but also minimize overtreatment. Although most of persistent GGNs are finally diagnosed as adenocarcinoma or precursor glandular lesions, the GGN-featured lung cancer is characterized as indolent growth or even non-growth. Therefore, scheduled follow-up might be safe for the special radiologic type under a certain condition. We should design the individualized diagnosis and treatment strategy for each patient. The treatment decision-making depends on various factors, including invasion, dynamic change, efficacy and safety of the treatment, as well as physical and psychic condition of the patients. Different from other types of lung cancer, the indolent feature of GGN-featured lung cancer allows a long time to intervene. Therefore, the determination of proper timing for intervention should be made cautiously. Surgical resection is still the principal treatment for GGN-featured lung cancer. However, there is still no consensus on the optimal surgical approach for GGN-featured lung adenocarcinoma. Currently, sublobar resection without lymphadenectomy has been recommended to the patients with precursor glandular lesions. In light of the GGN-featured lung cancer which generally represents a local lesion, local ablation therapies have been used in those patients, especially in the ones who are inoperable or refuse to undergo surgery. The percutaneous local ablation includes different techniques: radiofrequency ablation, microwave ablation and argon-helium cryoablation. The local ablation is safe, minimally invasive and repeatable. In addition, it offers the advantage to biopsy and treatment synchronously. Percutaneous ablation has the potential to be an alternative of surgery to cure GGN-featured lung cancer based on emerging evidences. The efficacy of transbronchial ablation guided by ultrasound or electromagnetic navigational system in the treatment of GGN-featured lung cancer has been primarily validated. As a developing technology, it might be a promising approach but needs further exploration. With the advance in ablation technology, we do believe that the interventional therapy will play an equal role as surgery in curative treatment of GGN-featured lung cancer in the future. Personalized treatment considering the condition of patients and the features of the lesion will maximize the benefit of every patient. This article will explore the diagnosis and treatment strategies of GGN on the basis of further understanding of GGN, and introduce the application of ablation therapy in GGN from the perspective of respiratory intervention.
Objective To explore the independent risk factors for tumor invasiveness of ground-glass nodules and establish a tumor invasiveness prediction model. Methods A retrospective analysis was performed in 389 patients with ground-glass nodules admitted to the Department of Thoracic Surgery in the First Hospital of Lanzhou University from June 2018 to May 2021 with definite pathological findings, including clinical data, imaging features and tumor markers. A total of 242 patients were included in the study according to inclusion criteria, including 107 males and 135 females, with an average age of 57.98±9.57 years. CT data of included patients were imported into the artificial intelligence system in DICOM format. The artificial intelligence system recognized, automatically calculated and output the characteristics of pulmonary nodules, such as standard diameter, solid component size, volume, average CT value, maximum CT value, minimum CT value, central CT value, and whether there were lobulation, burr sign, pleural depression and blood vessel passing. The patients were divided into two groups: a preinvasive lesions group (atypical adenomatoid hyperplasia/adenocarcinoma in situ) and an invasive lesions group (minimally invasive adenocarcinoma/invasive adenocarcinoma). Univariate and multivariate analyses were used to screen the independent risk factors for tumor invasiveness of ground-glass nodules and then a prediction model was established. The receiver operating characteristic (ROC) curve was drawn, and the critical value was calculated. The sensitivity and specificity were obtained according to the Yorden index. Results Univariate and multivariate analyses showed that central CT value, Cyfra21-1, solid component size, nodular nature and burr of the nodules were independent risk factors for the diagnosis of tumor invasiveness of ground-glass nodules. The optimum critical value of the above indicators between preinvasive lesions and invasive lesions were –309.00 Hu, 3.23 ng/mL, 8.65 mm, respectively. The prediction model formula for tumor invasiveness probability was logit (P)=0.982–(3.369×nodular nature)+(0.921×solid component size)+(0.002×central CT value)+(0.526×Cyfra21-1)–(0.0953×burr). The areas under the curve obtained by plotting the ROC curve using the regression probabilities of regression model was 0.908. The accuracy rate was 91.3%. Conclusion The logistic regression model established in this study can well predict the tumor invasiveness of ground-glass nodules by CT and tumor markers with high predictive value.
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.
With the development of multi-slice spiral computed tomography (CT) technology and the popularization of low-dose spiral CT screening, more and more adenocarcinomas presenting ground-glass nodule (GGN) are found. Pathological invasiveness is one of the important factors affecting the choice of treatment strategy and prognosis of patients with early lung adenocarcinoma. Imaging features have attracted wide attention due to their unique advantages in predicting the pathologic invasiveness of early lung adenocarcinoma. The imaging characteristics of GGN can be used to predict the pathologic invasiveness of lung adenocarcinoma and provide evidence for clinical decisions. However, the imaging parameters and numerical values for predicting pathologic invasiveness are still controversial, which will be reviewed in this paper.
Objective To explore the correlation between the quantitative and qualitative features of CT images and the invasiveness of pulmonary ground-glass nodules, providing reference value for preoperative planning of patients with ground-glass nodules. MethodsThe patients with ground-glass nodules who underwent surgical treatment and were diagnosed with pulmonary adenocarcinoma from September 2020 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were collected. Based on the pathological diagnosis results, they were divided into two groups: a non-invasive adenocarcinoma group with in situ and minimally invasive adenocarcinoma, and an invasive adenocarcinoma group. Imaging features were collected, and a univariate logistic regression analysis was conducted on the clinical and imaging data of the patients. Variables with statistical difference were selected for multivariate logistic regression analysis to establish a predictive model of invasive adenocarcinoma based on independent risk factors. Finally, the sensitivity and specificity were calculated based on the Youden index. Results A total of 555 patients were collected. The were 310 patients in the non-invasive adenocarcinoma group, including 235 females and 75 males, with a meadian age of 49 (43, 58) years, and 245 patients in the invasive adenocarcinoma group, including 163 females and 82 males, with a meadian age of 53 (46, 61) years. The binary logistic regression analysis showed that the maximum diameter (OR=4.707, 95%CI 2.060 to 10.758), consolidation/tumor ratio (CTR, OR=1.027, 95%CI 1.011 to 1.043), maximum CT value (OR=1.025, 95%CI 1.004 to 1.047), mean CT value (OR=1.035, 95%CI 1.008 to 1.063), spiculation sign (OR=2.055, 95%CI 1.148 to 3.679), and vascular convergence sign (OR=2.508, 95%CI 1.345 to 4.676) were independent risk factors for the occurrence of invasive adenocarcinoma (P<0.05). Based on the independent predictive factors, a predictive model of invasive adenocarcinoma was constructed. The formula for the model prediction was: Logit(P)=–1.293+1.549×maximum diameter of lesion+0.026×CTR+0.025×maximum CT value+0.034×mean CT value+0.72×spiculation sign+0.919×vascular convergence sign. The area under the receiver operating characteristic curve of the model was 0.910 (95%CI 0.885 to 0.934), indicating that the model had good discrimination ability. The calibration curve showed that the predictive model had good calibration, and the decision analysis curve showed that the model had good clinical utility. Conclusion The predictive model combining quantitative and qualitative features of CT has a good predictive ability for the invasiveness of ground-glass nodules. Its predictive performance is higher than any single indicator.
The subtype of lung cancer that presents as subsolid nodules on imaging exhibits unique biological behavior and favorable prognosis. Recently, the American Association for Thoracic Surgery (AATS) issued "The 2023 American Associationfor Thoracic Surgery (AATS) expert consensus document: Management of subsolid lung nodules". This consensus, based on the latest literature and current clinical experience, proposes updated strategies for managing subsolid nodules. It emphasizes the correlation between imaging findings and pathological classification, individualized follow-up and surgical management strategies for subsolid nodules, and multimodal treatment approaches for multiple subsolid pulmonary nodules.