Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

Lung cancer, a highly prevalent and deadly malignancy globally, poses a significant disease burden in China and is the leading cause of cancer death. Despite rapid advances in medicine, its incidence and mortality rates remain stubbornly high, making it a major challenge in public health. Against the backdrop of rapid progress in precision medicine, the paradigm of lung cancer treatment is shifting from single traditional therapy to multi-dimensional integration. This article comprehensively reviews the innovations and challenges in lung cancer surgery in 2024, aiming to explore the future development of surgical treatment with colleagues and to improve patients' quality of life and achieve the goal of "cure".
.
Humans ; Lung Neoplasms/surgery*

The application of robots in thoracic surgery is mainly based on the da Vinci general surgery robot. With the popularization of artificial intelligence (AI) application scenarios, the combination of AI and robots is more closely, and there is a strong clinical demand and huge application space for the development of specialized disease-specific robotic systems for thoracic surgery. This article aims to systematically describe the history of the rise of specialized surgical robots and the status of the localization of surgical robots in China, propose the concept of applying AI to the research and development of integrated specialized disease-specific robots in thoracic surgery, and clarify the ethics and prospects that intelligent specialized disease-specific surgical robots will face.