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.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

With the accelerating aging of the population, the proportion of elderly patients with lung cancer continues to rise, presenting multiple challenges to perioperative management. This paper systematically reviews the clinical characteristics of elderly lung cancer patients. Based on the comprehensive geriatric assessment, it proposes incorporating seven major geriatric syndromes—frailty, delirium, sarcopenia, cognitive impairment, malnutrition, dysphagia, and mood disorders—into the core evaluation system. By integrating multimorbidity management with complication prevention and control, an integrated "geriatric syndrome-multimorbidity-complication" perioperative management model is constructed. Furthermore, this paper outlines stratified intervention strategies for geriatric syndromes, a "five-step" workflow for comorbidity management, and a comprehensive intervention pathway for complications across the preoperative, intraoperative, and postoperative phases. Multidisciplinary team (MDT) collaboration serves as the core mechanism to achieve individualized comprehensive treatment. This paper aims to provide a novel perioperative comprehensive treatment model for elderly lung cancer patients, which is centered on geriatrics, supported by multidisciplinary collaboration, and guided by precision medicine.

Objective To evaluate the performance of lightweight Chinese large language models (LLMs) in answering specialized lung cancer questions, and to explore the impact of retrieval-augmented generation (RAG) on model performance. Methods Eleven lightweight Chinese LLMs with parameter sizes ranging from 7B to 32B were included. A lung cancer-specific evaluation dataset consisting of 200 questions [100 A1-type (basic knowledge) and 100 A2-type (clinical case) questions], constructed based on clinical guidelines and thoracic surgery textbooks, was used for assessment. Model performance was evaluated under two conditions (with and without RAG). Accuracy was used to assess model performance, and response latency was recorded to reflect inference efficiency. An accuracy–latency scatter plot was constructed for descriptive analysis of overall model performance. Results All models successfully completed the evaluation. With the introduction of RAG, the overall average accuracy improved from 61.68% to 76.36%. Smaller models demonstrated the most significant improvement (e.g., the accuracy of DeepSeek-7B increased from 32.50% to 60.00%, P<0.001). The average response latency increased from 12.58 s to 13.80 s. The Qwen3 series showed the best overall performance, and Qwen3-32B achieved the highest accuracy under both conditions (76.50% and 84.00%, respectively). After RAG integration, performance differences among model families were markedly reduced. Based on the accuracy-latency trade-off, Qwen3-32B achieved the best balance between accuracy and response latency under the baseline condition, whereas Qwen3-14B demonstrated superior overall performance in terms of accuracy, latency, and computational cost after RAG integration. Conclusion The integration of RAG technology improves the ability of lightweight Chinese LLMs to answer specialized lung cancer questions. Under the dual practical constraints of limited computational resources and medical data security requirements, the "lightweight model+RAG" technical framework may represent a promising deployment solution.

Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

The Consolidated Standards of Reporting Trials (CONSORT) statement aims to enhance the quality of reporting for randomized controlled trial (RCT) by providing a minimum item checklist. It was first published in 1996, and updated in 2001 and 2010, respectively. The latest version was released in April 2025, continuously reflecting new evidence, methodological advancements, and user feedback. CONSORT 2025 includes 30 essential checklist items and a template for a participant flow diagram. The main changes to the checklist include the addition of 7 items, revision of 3 items, and deletion of 1 item, as well as the integration of multiple key extensions. This article provides a comprehensive interpretation of the statement, aiming to help clinical trial staff, journal editors, and reviewers fully understand the essence of CONSORT 2025, correctly apply it in writing RCT reports and evaluating RCT quality, and provide guidance for conducting high-level RCT research in China.

China is facing the double burden of high incidence of lung cancer and tuberculosis epidemic. Lung cancer combined with tuberculosis has a high incidence and complexity in clinical practice. High-risk groups include immunocompromised people, long-term smokers and people with a history of tuberculosis. The coexistence of the two diseases not only increases the difficulty of diagnosis and treatment decision-making, but also increases the risk of treatment-related adverse reactions and drug interactions. The guideline was developed by Committee of Integrated Rehabilitation for Lung Cancer, Chinese Anti-Cancer Association; Chinese and Western Integrated Lung Cancer Committee of Chinese Anti-Cancer Association; Society of Tuberculosis, Chinese Medical Association, aiming to standardize the diagnosis and treatment of lung cancer complicated with pulmonary tuberculosis. The guideline emphasizes the core position of combined diagnosis of multimodal imaging, etiology and pathology. It is proposed that anti-tuberculosis and anti-tumor treatment should be coordinated under the framework of multidisciplinary team, and drug interactions and timing optimization should be paid attention to. For surgical treatment, minimally invasive resection combined with systematic lymph node dissection is recommended after infection control. Systemic therapy requires individualized risk stratification and dynamic monitoring of efficacy and adverse reactions. Based on evidence-based medicine and Chinese clinical practice, combined with the accessibility of drugs and technologies, this guideline proposes a whole-process management pathway covering screening, diagnosis, treatment and follow-up, in order to improve the prognosis and quality of life of patients.

With the change of coronavirus disease 2019 (COVID-19) prevention and control strategy in China, the number of COVID-19 cases has increased significantly recently, which has also brought new challenges to the perioperative risk control of thoracic surgery. This paper puts forward several suggestions, aiming to standardize the preoperative screening and evaluation during the COVID-19 period, strictly grasp the indications and timing of surgery, optimize the medical management process, individualize surgical decision-making, and minimize the risk of COVID-19 infection to surgery.

Objective:To evaluate the changes in topological properties of brain functional network after induction of general anesthesia in the patients with glioma.Methods:Twenty-two patients scheduled for elective intracranial glioma resection were selected.Resting-state functional magnetic resonance imaging was performed during wakefulness and general anesthesia with endotracheal intubation in patients with glioma. Large-scale functional brain networks of each patient were constructed based on 123 regions of interest in non-surgical hemisphere. Global properties (local efficiency, clustering parameter, shortest path length, global efficiency, small world) and nodal properties (nodal degree, nodal efficiency, and between centrality) in brain functional networks were then compared between wakefulness and general anesthesia.Results:Eighteen patients were finally enrolled. Compared with the status during wakefulness, the local efficiency and clustering parameter on non-surgical side significantly decreased ( P<0.05), no significant change was found in the shortest path length and global efficiency ( P>0.05), and small world was greater than 1 throughout the entire density range; the nodal degree, nodal efficiency and between centrality of nodes located in the medial/mesal regions, such as the medial prefrontal cortex, posterior cingulate gyrus/precuneus, medial temporal lobe, anterior cingulate gyrus, thalamus, amygdala, were significantly reduced ( P<0.05); however, these node parameters increased significantly in the lateral brain regions ( P<0.05) except for the primary auditory and somatosensory cortex, which also decreased significantly after induction of general anesthesia( P<0.05). Conclusions:The functional segregation of brain functional network is widely inhibited after induction of general anesthesia, but the functional integration is still retained. The lateral brain regions show no anticorrelation with the medial brain region during general anesthesia.