The management of non-small cell lung cancer is rapidly advancing toward precision and individualized care. The National Comprehensive Cancer Network (NCCN) has recently released the 2026 version 1, 2, and 3 updates of the NCCN Clinical Practice Guidelines in Oncology: Non-Small Cell Lung Cancer. These updated guidelines introduce significant revisions in several core areas, including the adoption of the AJCC 9th Edition TNM Staging System, optimization of recommendations for lung cancer screening and mediastinal staging, updates on perioperative immunotherapy and targeted therapy strategies, and the inclusion of emerging biomarkers such as neuregulin 1 (NRG1) fusions and mesenchymal-epithelial transition factor (MET) protein overexpression. This article provides a systematic interpretation of these key updates and focuses on integrating international guidelines with clinical practice in China. Specifically, considering the high prevalence of epidermal growth factor receptor (EGFR) mutations in the Chinese population, it analyzes the first-line positioning of osimertinib- and lazertinib-based combination regimens and evaluates the clinical value of domestic third-generation tyrosine kinase inhibitors. In response to the NCCN's cautious stance on “conversion therapy” for initially unresectable disease, the article elaborates on China’s exploratory efforts and the evolving evidence base for conversion strategies utilizing programmed death receptor 1 (PD-1) inhibitors (e.g., tislelizumab, toripalimab). Furthermore, it clarifies the “dual-track” diagnostic approach distinguishing MET pathway mutations from protein overexpression. Aimed at aligning with China’s clinical context, this review seeks to offer clinicians a practical reference that synthesizes cutting-edge international perspectives with local applicability.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

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.

Driven by the widespread adoption of minimally invasive thoracic surgical techniques and the deepening integration of enhanced recovery after surgery (ERAS) protocols, evidence-based perioperative airway management has emerged as a pivotal determinant of surgical safety, pulmonary functional recovery, and long-term prognosis. Currently, substantial regional disparities in thoracic surgical expertise persist across China, manifesting as heterogeneity in preoperative assessment standardization, inconsistent airway preparation strategies, divergent approaches to complication prevention, and limited penetration of advanced anesthetic technologies. To promote the standardization of clinical practices in this domain among hospitals at all levels, a multidisciplinary expert consortium has initiated the development of the Chinese Guidelines for Perioperative Airway Management in Thoracic Surgery (2026 edition). This protocol systematically delineates the methodological framework and structural blueprint underpinning the guideline’s formulation. Compared to the 2020 edition, the updated guideline places specific emphasis on refining stratified criteria for preoperative cardiopulmonary reserve assessment, establishing standardized management pathways for non-intubated spontaneous-ventilation techniques, formulating individualized multimodal perioperative pulmonary rehabilitation regimens, and defining precise intervention workflows for persistent postoperative cough. Collectively, these measures aim to further reduce the baseline incidence of perioperative pulmonary complications and to provide robust support for the clinical translation of enhanced recovery principles within the field of thoracic surgery.

Objective To analyze the characteristics and changing trends of lung cancer-related projects funded by National Natural Science Foundation of China (NSFC) during 2019–2023, providing a reference for researchers in topic selection and for optimizing scientific research management and resource allocation. Methods Relevant databases, including the NSFC Big Data Knowledge Management Service Platform, were systematically searched to collect information on lung cancer-related funded projects from 2019 to 2023. Quantitative methods were applied to evaluate project categories, funding amounts, and the distribution of host institutions. Project themes were classified according to their titles, keywords, and abstracts, and were subsequently compared with those of projects funded during 2014–2018. Results A total of 1 327 lung cancer-related projects were funded from 2019 to 2023, with total funding of 554.92 million yuan. The Young Scientists Fund and the General Program accounted for 90.3% (1 199/1 327) of the projects and 84.1% (46 675/55 492) of the total funding; the top 10 host institutions comprised approximately 32.0% of all funded projects. Thematic analysis revealed that the core funding directions in the lung cancer field during 2019–2023 focused on molecular mechanisms and signaling pathways. Major research themes included metastasis and recurrence, tumor microenvironment and immunity, and targeted therapy resistance and drug development. Compared with 2014–2018, the proportion of projects focused on tumor microenvironment and immunity, as well as artificial intelligence and medical engineering, increased substantially. Overall, the themes of funded lung cancer projects were dominated by mechanistic studies and demonstrated cross-cutting features intersecting with disease progression, therapeutic resistance, immune regulation, and clinical translation. Conclusion The number of NSFC-funded lung cancer-related projects has continued to grow. Research themes have evolved from pure mechanistic exploration toward immune regulation and translational research, exhibiting characteristics of interdisciplinary integration with emerging technologies and multi-theme cross-fertilization. Researchers are encouraged to align with policy orientations and to emphasize the innovation and translational value of their proposed topics.

With the aging population and the widespread implementation of lung cancer screening programs, an increasing number of elderly patients are undergoing curative lung resection. Due to diminished physiological reserve and complex comorbidities, this demographic exhibits a significantly higher incidence of severe perioperative complications (defined as Clavien-Dindo grade≥Ⅲ), which adversely affects both perioperative safety and long-term prognosis. In recent years, the research paradigm has shifted from univariate analysis toward multidimensional risk integration and the development of predictive models. This article systematically reviews the key risk factors for severe perioperative complications in elderly lung cancer patients, encompassing biological aging processes, frailty and sarcopenia, cardiopulmonary and renal functional reserves, inflammatory-immune and coagulation status, and perioperative interventions. Furthermore, it traces the evolution of risk assessment tools from traditional regression models to machine learning models that integrate multimodal data. The review also discusses common challenges in this field, including the standardization of outcome definitions, external validation, calibration assessment, and clinical translation. Future efforts should prioritize the deep integration of predictive tools with clinical decision support systems to establish a closed-loop care pathway from risk identification to stratified intervention, thereby effectively reducing complication rates and enhancing surgical outcomes for elderly lung cancer patients.

This paper systematically summarizes the practical experience of the 2025 Dingri earthquake emergency medical rescue in Tibet. It analyzes the requirements for earthquake medical rescue under conditions of high-altitude hypoxia, low temperature, and low air pressure. The paper provides a detailed discussion on the strategic layout of earthquake medical rescue at the national level, local government level, and through social participation. It covers the construction of rescue organizational systems, technical systems, material support systems, and information systems. The importance of building rescue teams is emphasized. In high-altitude and cold conditions, rapid response, scientific decision-making, and multi-party collaboration are identified as key elements to enhance rescue efficiency. By optimizing rescue organizational structures, strengthening the development of new equipment, and promoting telemedicine technologies, the precision and effectiveness of medical rescue can be significantly improved, providing important references for future similar disaster rescues.

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 widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.