The 2025 American Society of Clinical Oncology (ASCO) Annual Meeting was held in Chicago. At the meeting, researches on the treatment of epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) once again took the spotlight. Combination therapy strategies have demonstrated the potential to overcome resistance to EGFR tyrosine kinase inhibitor (EGFR-TKI) and prolong survival. Meanwhile, progress has also been made in individualized treatment strategies for young patients and those with fibrotic interstitial lung disease. However, the complexity of resistance mechanisms, special treatment considerations for different populations, and the impact of socioeconomic factors on treatment accessibility remain challenges in the field of EGFR-mutant NSCLC treatment. In the future, it is necessary to further explore more effective treatment regimens and expand the accessibility of precision medicine to maximize patient benefits.

The 26th World Conference on Lung Cancer (WCLC) was held in Barcelona during September 6-9, 2025. As the world's largest and most influential academic meeting in the field of lung cancer, this year's congress unveiled long-term follow-up data from several pivotal studies and significant advances in novel therapeutic strategies. In the realm of targeted therapy, a next-generation combination strategy has been established as the new standard of care for the first-line treatment of patients with advanced epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC), demonstrating a significant improvement in overall survival. In immunotherapy, novel combination regimens have not only addressed the therapeutic challenge of acquired resistance to EGFR targeted therapies, but also shown clear long-term survival benefits in both the perioperative and locally advanced settings. These findings pave the way for shifting the treatment paradigm to earlier stages for patients with NSCLC. Antibody-drug conjugates have made remarkable strides in this field. They have shown outstanding efficacy in patients with specific resistance mutations and those with brain metastases, and have also demonstrated immense potential in treating patients with HER2-aberrant lung cancer and broader NSCLC populations. This offers new therapeutic options for patients with refractory lung cancer.However, significant challenges remain, including the heterogeneity of resistance mechanisms, the selection of optimal treatment regimens, and management strategies for special populations. Future research should focus on identifying novel precision biomarkers and optimizing therapeutic strategies to ultimately improve clinical outcomes for all patients with lung cancer.

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.

ObjectiveBiochemistry and Molecular Biology, a discipline that elucidates life phenomena at the molecular level, serves as a core foundational course in medical education. It provides the theoretical basis for studying other basic and clinical medical subjects, as well as for understanding pathogenesis, disease diagnosis, and treatment. However, its complex content and highly abstract concepts have posed a dual challenge to traditional teaching models: “inefficient instruction” and “inadequate learning outcomes”. Within limited classroom hours, how to engage students and stimulate their intrinsic motivation, and how to help them recognize, understand, and develop a passion for biochemistry from the perspective of the discipline’s essence, have long been key focuses of curriculum research. MethodsUsing the lipid metabolism chapter as an example, this study employs “Rain Classroom”, a generative artificial intelligence (AI)-assisted platform, to support education in four dimensions: teaching, learning, evaluation, and research. In teaching, it assists instructors through virtual experiments, lesson preparation support, knowledge mapping, and assignment design. For learning, it serves as an intelligent study assistant for students, providing automated assignment review, enabling educational resource sharing, and facilitating personalized learning pathways. In evaluation, the platform automates assignment grading, analyzes student performance data, and offers diagnostic feedback and teaching recommendations. In research, it aids educators in collecting and analyzing teaching data, as well as searching for and summarizing relevant literature. ResultsThe results indicate that an educational model integrating teacher-led instruction, student-centered learning, and generative AI assistance significantly enhances teaching quality, students’ self-directed learning abilities, and knowledge mastery. Furthermore, with the support of generative AI, curriculum-based ideological education—focusing on cutting-edge disciplinary advances and topical medical issues—helps cultivate students’ medical spirit of “honoring life and healing the wounded”, thereby fostering the establishment of appropriate professional values. Finally, while generative AI presents both opportunities and challenges for higher education, this study also analyzes potential risks in its teaching applications, emphasizing the need for both instructors and students to avoid over-reliance and to ensure that technological tools consistently serve the fundamental goals of education. ConclusionThis study demonstrates that integrating generative AI, specifically via the “Rain Classroom” platform, can effectively enhance biochemistry education. By supporting teaching, learning, evaluation, and research, this approach improves both educational effectiveness and student outcomes. It also facilitates the incorporation of cutting-edge knowledge and professional ethics, nurturing a patient-centered mindset. Additionally, the study addresses potential implementation risks to ensure that such technological tools remain aligned with the core purpose of education.

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.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Aim To clarify the mechanism by which Qishen Yixin Granules improved microcirculation vas-cular endothelial dysfunction(VED)in mice,through activating the Nrf2/HO-1 signaling pathway to regulate oxidative stress.Methods C57 mice were randomly divided into six groups:blank group,model group,pos-itive drug group,and low-,medium-,and high-dose groups of Qishen Yixin Granules.The VED model was established by long-term infusion of Ang Ⅱ combined with a high-fat diet.Each treatment group received the corresponding drug intervention.After four weeks of drug intervention,cardiac function was assessed by echocardiography.Carstairs staining was used to ob-serve the formation of microthrombi in myocardial tis-sue.The micro vascular ischemia was evaluated by Hei-denhain staining.The ultrastructure of endothelial cells was observed by electron microscopy.The levels of EMPs,ROS,NO,ET-1,TF,TM,VWF,and TXA2 in serum were measured by ELISA.The expression levels of MDA,SOD,and GSH-Px in mouse heart tissue were determined by chemical methods.Cardiac microvascu-lar density and the expression of Nrf2,Keap1,and HO-1 proteins were detected by Immunohistochemical stai-ning.The protein expressions of Keap1,cytoplasmic Nrf2,nuclear Nrf2,and HO-1 in myocardial tissue were detected by Western blot.Results Qishen Yixin Granules could effectively improve the cardiac function of mice,alleviate the damage of endothelial cells and endothelial function.They could up-regulate serum NO levels and the activities of antioxidant enzymes SOD and GSH-Px,while down-regulating the expression of ROS and vascular inflammatory injury factors such as ET-1,VWF,TXA2,TF,TM,and EMPs.Qishen Yixin Granules also increased the positive counts of CD34,Nrf2,and HO-1,as well as microvessel density.Fur-thermore,they inhibited the expression of MDA,Keap1,and cytoplasmic Nrf2 protein in myocardial tis-sue,while increasing the expression of nuclear proteins HO-1 and Nrf2.Conclusions Qishen Yixin Granules may inhibit oxidative stress and inflammatory response by regulating the Nrf2/HO-1 signaling pathway,thereby improving vascular endothelial damage and cardiac function in VED mice.

AIM To study the chemical constituents from the stems and leaves of Dendrobium formosum Roxb.ex Lindl.and their biological activities.METHODS The 95％ethanol extract from the stems and leaves of D.formosum was isolated and purified by silica gel,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their inhibitory activities onα-glucosidase were determined by PNPG method,and their in vitro anti-inflammatory activities were evaluated by RAW264.7 model.RESULTS Fifteen compounds were isolated and identified as coniferyl p-coumarate(1),(-)-pinoresinol(2),2,5,7-trihydroxy-4-methoxy-9,10-dihydrophenanthrene(3),naringenin(4),spiropreussomerin A(5),7-hydroxy-14-de-O-methyl-lasiodiplodin(6),(4S,5S,6Z,8E)-5-hydroxydeca-6,8-dien-4-olide(7),(6S,9R)-blumenol C(8),p-hydroxybenzoic acid(9),m-hydroxybenzoic acid(10),p-hydroxy benzenepropanoic acid(11),5,7-dihydroxy-isobenzofuran(12),2-(4-hydroxyphenyl)-ethanol(13),β-sitostenone(14),β-sitosterol(15).The IC50 values of compounds 1 and 4 on α-glucosidase inhibition were(65.60±3.31)and(98.95±2.53)μmol/L,respectively.Compound 3 presented inhibitory activity on NO production in RAW 264.7 cells,with IC50 value of(3.97±0.12)μmol/L.CONCLUSION Compounds 5-6,8 and 12 are isolated from Orchidacae family for the first time,and 2-15 are first isolated from this plant.Compounds 1 and 4 have α-glucosidase inhibitory activities,and 3 has anti-inflammatory activity.