ObjectiveTo emphasize the important role of video-assisted thoracoscopic surgery (VATS) in treatment of mediastinal bronchogenic cysts (MBCs).MethodsWe retrospectively reviewed the clinical data of 112 patients (53 males and 59 females) of mediastinal bronchogenic cysts who underwent VATS in our institution between April 2001 and Aprial 2016. Median age was 4–75 (45.6±15.0) years. All patients underwent chest CT preoperatively. The patients were divided into two groups: an anterior mediastinum group, 47 patients; a middle and posterior mediastinum group, 65 patients including 35 patients in the middle mediastinum, 30 patients in the posterior mediastinum. The average diameter was 0.5–22.0 (3.50±2.33) cm. The average CT attenuation was 0–67 (35.5±15.3) Hu on unenhanced CT. We began each operation with the VATS technique.ResultsThe CT diagnostic accuracy for group middle and posterior mediastinum with CT value≤20 Hu was higher than others (61.5% vs. 13.1%, χ2=17.675, P<0.001). A total of 111 patients underwent VATS, only one patient converted to open thoracotomy. Cyst resection and thymectomy were conducted in 45 patients, cyst resection and extended thymectomy were conducted in 2 patients in the anterior mediastinum group. Simply cyst resection were performed in the middle and posterior mediastinum group (n=65). The average operative time was 40–360 (104.5±43.1) min. The average intraoperative blood loss was 5–600 (57.9±88.9) mL. The intraoperative complication rate was 3.6% and the incomplete resection rate was 6.3%. The main reason for these was severe adhesion between the cyst and mediastinal structure. No serious postoperative complication was found. Follow-up was done in 99 patients, and the mean follow-up time was 42 (12–191) months. There was no local recurrence.ConclusionVATS resection of MBCs is a safe and efficacious procedure, and minimally invasive and surgical resection should be performed as early as possible for MBCs.
With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.
