Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

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.

ObjectiveTo systematically review the effect of dual-task training (DTT) on mobility and balance in stroke patients. MethodsCNKI, Wanfang Data, VIP, PubMed, Web of Science, Embase and Cochrane Library were searched for randomized controlled trials (RCT) on DTT in stroke rehabilitation from inception to June, 2025. The quality of the included studies was assessed using the Cochrane Risk of Bias Tool and the PEDro scale. Meta-analysis was performed using RevMan 5.4. ResultsA total of eleven RCT involving 517 stroke patients were included, and the scores of PEDro scale were four to eight. DTT was more effective on the score of Fugl-Meyer Assessment-Lower Extremity (MD = 3.70, 95%CI 1.88 to 5.53, P < 0.001) and Berg Balance Scale (BBS) (MD = 3.04, 95%CI 1.58 to 4.49, P < 0.001), fall incidence (OR = 0.32, 95%CI 0.11 to 0.87, P = 0.025), gait speed under a single-task condition (MD = 0.08, 95%CI 0.03 to 0.12, P = 0.002), gait speed under a dual-task condition (MD = 0.13, 95%CI 0.10 to 0.16, P < 0.001), Timed Up and Go Test time (MD = -2.78, 95%CI -3.85 to -1.72, P < 0.001), and score of modified Barthel Index (MD = 4.77, 95%CI 1.88 to 7.66, P = 0.001). Subgroup analysis indicated that motor-motor DTT was more effective than cognitive-motor DTT on BBS scores and single-task gait speed. ConclusionsDTT can improve mobility and balance in stroke patients, especially with motor-motor tasks.

Artificial intelligence (AI) has made preliminary advances in the management of postoperative follow up for lung cancer; however, a systematic review of its application value across the entire follow-up continuum remains lacking. Taking the four core dimensions of postoperative follow-up management as its framework, including surveillance candidate selection, follow-up interval optimization, follow-up protocol design, and follow-up modality selection, this review examines the progress of AI in patient risk stratification, surveillance frequency optimization, content design, and supportive platform development. The review further delineates the current challenges and future directions for AI in this domain, and ultimately seeks to promote the standardized and scaled application of AI in postoperative follow-up management for lung cancer, with the goal of establishing a "care beyond hospitalization" life-cycle management framework that improves the long-term quality of survival for patients undergoing lung cancer surgery.

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.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Objective:To investigation the situation of water improvement projects in villages affected by drinking water-type endemic fluorosis in Qinghai Province and the prevalence of dental fluorosis among children, in order to provide a basis for consolidating the achievements in prevention and control of drinking water-type endemic fluorosis and adjusting prevention and control measures.Methods:The monitoring data on drinking water-type endemic fluorosis were collected from the disease prevention and control centers in various counties of Qinghai Province from 2021 to 2023, the situation of water improvement projects, the fluorine content of domestic drinking water and the prevalence of dental fluorosis in children aged 8 to 12 years old were retrospectively analyzed.Results:From 2021 to 2023, the numbers of villages affected by drinking water-type endemic fluorosis in Qinghai Province were 338, 335, and 328, respectively. The numbers of water improvement projects were 125, 127 and 124, respectively. The normal operation rates were 100%, 100% and 99.19% (123/124), respectively. The qualified rates of water fluoride level were 100%, 99.21% (126/127) and 99.19% (123/124), respectively. The detection rates of dental fluorosis among children aged 8 to 12 were 4.34% (515/11 877), 5.70% (646/11 331) and 4.48% (490/10 943), respectively. There was a statistically significant difference in the detection rate of dental fluorosis among children in different years (χ 2 = 22.79, P < 0.001). Conclusions:The overall operation status of water improvement project in villages affected by drinking water-type endemic fluorosis in Qinghai Province is generally good, but there has been some relaxation in management and maintenance in the later stage, and there is a phenomenon of project intermittency. The detection rate of dental fluorosis among children aged 8 to 12 remains low, and endemic fluorosis caused by drinking water is under continuous control.

Objective To explore the types of symptom clusters in patients with bladder instillation and identify the core symptoms through symptom network analysis,and to provide evidence for symptom management of the patients.Methods Convenience sampling was used to select 226 patients with bladder instillation for bladder cancer at a Tier-IIIA hospital in Guiyang from March 2023 to July 2024.Data were collected through review of medical record,general information questionnaires and symptom assessment questionnaires specific to the patients with bladder instillation.Exploratory factor analysis was used to extract symptom clusters,and symptom relationships were analysed using R language for network layout and centrality calculations.Results A total of 223 patients with bladder cancer completed the survey.Four symptom clusters were identified,namely the urinary system symptom cluster,digestive system symptom cluster,somatic symptom cluster and psychological symptom cluster,with a cumulative variance contribution rate of 66.038%.The symptom network analysis showed strong associations between dysuria and urethral pain during catheterisation,incomplete emptying and dysuria,as well as distress and poor sleep quality.In terms of centrality indicators,the top three symptoms by strength were dysuria(rS=2.26),incomplete emptying(rS=0.99)and poor sleep quality(rS=0.90).Of the expected impacts,the top three were dysuria(rE=2.09),poor sleep quality(rE=1.34)and frequency of urinary(rE=0.55).Regarding the closeness centrality,the top three were dysuria(rC=2.23),incomplete emptying(rC=0.64)and poor sleep quality(rC=0.54).Dysuria had the highest strength,expected impact and closeness centrality,indicating the dysuria was the core symptom in the patients with bladder cancer.Conclusion For the patients with bladder instillation experience,multiple and interconnected symptoms appear at the same time and together they form a symptom clusters,of which the pain in urination is the core symptom.Medical staff should consider symptom prevalence and core symptoms and to develop the targeted and precise management strategies for targeted nursing.

OBJECTIVE To provide standardized whole-process guidance on drug traceability codes for medical institutions in Sichuan province， ensuring medication safety and compliance with medical insurance supervision requirements. METHODS Based on evidence-based principles and expert consensus， Expert Consensus on Whole-process Management of Drug Traceability Codes in Medical Institutions of Sichuan Province （hereinafter referred to as the Consensus） was formulated through systematic literature review， field investigations， establishment of a multidisciplinary expert committee and multiple rounds of questionnare consultation via the modified Delphi method， and finalized through consensus meetings. RESULTS & CONCLUSIONS The Consensus clarifies key operating procedures for code verification， code assignment and code return， whole-process operational standards for drug warehouse acceptance and storage， drug warehouse outbound delivery and pharmacy acceptance check， drug distribution and dispensing in pharmacy and intravenous admixture center， medication administration in nursing units and examination departments， as well as drug return process. Key recommendations are proposed such as improving the core functions of the drug traceability system， unifying the hospital-wide traceability code database， strengthening the management of traceability codes for backup medications， establishing a management organization and institutional framework， and optimizing the architectural design and data governance requirements of the drug traceability system. The release of the Consensus will provide scientific， standardized and implementable practical guidelines for medical institutions of Sichuan province， helping to improve closed-loop management of the drug traceability system， strengthen medication safety and fulfil medical insurance fund supervision.