ObjectiveTo explore the application of artificial intelligence (AI) in the standardized training of thoracic surgery residents, specifically in enhancing clinical skills and anatomical understanding through AI-assisted lung nodule identification and lung segment anatomy teaching. MethodsThoracic surgery residents undergoing standardized training at Peking Union Medical College Hospital from September 2023 to September 2024 were selected. They were randomly assigned to an experimental group and a control group using a random number table. The experimental group used AI-assisted three-dimensional reconstruction technology for lung nodule identification, while the control group used conventional chest CT images. After basic teaching and self-practice, the ability to identify lung nodules on the same patient CT images was evaluated, and feedback was collected through questionnaires. ResultsA total of 72 residents participated in the study, including 30 males (41.7%) and 42 females (58.3%), with an average age of (24.0±3.0) years. The experimental group showed significantly better overall diagnostic accuracy for lung nodules (91.90% vs. 73.30%) and lung segment identification (100% vs. 83.70%) compared to the control group (91.90% vs. 73.30% and 100% vs. 83.70%, respectively), and the reading time was significantly shorter [(118.5±10.5) s vs. (332.1±20.2) s, P<0.01]. Questionnaire results indicated that approximately 94% of the residents had a positive attitude toward AI technology, believing it significantly improved diagnostic accuracy. ConclusionAI-assisted teaching significantly improved thoracic surgery residents' ability to read images and clinical thinking, providing a new direction for the reform of standardized training.
Lung cancer is the most frequent cancer and the leading cause of cancer death all around the world. Anti-programmed cell death 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) therapies have significantly improved the outcomes of non-small cell lung cancer (NSCLC) patients in recent years. However, the objective response rate in non-screened patients is only about 20%. It is very important to screen out the potential patients suitable for immunotherapy. Immunohistochemical staining of tumor tissue biopsies with PD-L1 antibodies can predict the therapeutic response to immunotherapy to some extent, but it still has some limitations. Recently some clinical studies have shown that PD-L1 expression in circulating tumor cells (CTC-PD-L1) is a potential independent biomarker and may provide important information for immunotherapy in NSCLC. This article will review technology for CTC-PD-L1 detection and the predictive value of CTC-PD-L1 for immunotherapy in NSCLC and review the latest clinical research progress.
With the broad application of high-resolution computed tomography (CT) and high rates of early lung cancer screening, the number of patients diagnosed with synchronous multiple primary lung cancer (sMPLC) has been increasing. It becomes of great prominence to distinct sMPLC from intrapulmonary metastases in clinical practice. An increasing number of studies have developed high-throughput sequencing based genetic approaches to specify the molecular characteristics of sMPLC, which contributes to a better understanding of its tumorigenesis. The genetic profile of sMPLC also benefits its diagnosis, which mainly relies on its clinicopathological criteria. Here, we summarize the progresses on the diagnostic criteria for sMPLC, and also molecular features of sMPLC from the perspective of clonality analysis.
Objective To explore the diagnostic value of circulating tumor cells (CTC) measured by magnetic nanoparticle method in lung cancer. Methods (1) We measured binding capability of A549 or NCI-H1965 cell lines with recognition peptide and capture efficiency by adding tumor cells into the whole blood of healthy human. (2) We measured CTC of 34 patients suspected with lung cancer, and the counting results of CTC were compared with the following pathological results. Results (1) The binding capability was 80.0%±6.0% for A549 and 70.1%±4.8% for H1957, while the capture efficiency was 57.3%±7.0% for A549 and 37.3%±6.1% for H1975. (2) CTCs were identified in 71.9% of patients with lung cancer. The specificity was 83.3%, and area under receiver operating characteristic (ROC) curve was 0.792 (P=0.003). Conclusion CTC measured by magnetic nanoparticle method has promising application in the diagnosis of lung cancer.
Objective To explore the efficacy of a novel detection technique of circulating tumor cells (CTCs) to identify benign and malignant lung nodules. Methods Nanomagnetic CTC detection based on polypeptide with epithelial cell adhesion molecule (EpCAM)-specific recognition was performed on enrolled patients with pulmonary nodules. There were 73 patients including 48 patients with malignant lesions as a malignant group and 25 patients with benign lesion as a benign group. There were 13 males and 35 females at age of 57.0±11.9 years in the malignant group and 11 males and 14 females at age of 53.1±13.2 years in the benign group. e calculated the differential diagnostic efficacy of CTC count, and conducted subgroup analysis according to the consolidation-tumor ratio, while compared with PET/CT on the efficacy. Results CTC count of the malignant group was significantly higher than that of the benign group (0.50/ml vs. 0.00/ml, P<0.05). Subgroup analysis according to consolidation tumor ratio (CTR) revealed that the difference was statistically significant in pure ground glass (pGGO) nodules 1.00/mlvs. 0.00/ml, P<0.05), but not in part-solid or pure solid nodules. For pGGO nodules, the area under the receiver operating characteristic (ROC) curve of CTC count was 0.833, which was significantly higher than that of maximum of standardized uptake value (SUVmax) (P<0.001). Its sensitivity and specificity was 80.0% and 83.3%, respectively. Conclusion The peptide-based nanomagnetic CTC detection system can differentiate malignant tumor and benign lesions in pulmonary nodules presented as pGGO. It is of great clinical potential as a noninvasive, nonradiating method to identify malignancies in pulmonary nodules.