Lung cancer, the malignancy with the highest global mortality rate, is currently undergoing a paradigm shift from precision medicine to intelligent medicine in its diagnostic and therapeutic models. Artificial intelligence (AI), leveraging its core advantages in multimodal data fusion and high-dimensional featureextraction, has deeply permeated the entire disease continuum of lung cancer screening, diagnosis, and individualized treatment. AI demonstrates significant value in improving patient outcomes and enhancing clinical efficiency, thereby reshaping the fundamental logic and clinical practice pathways of lung cancer management. This article reviews the latest advances of AI in real-world clinical diagnosis and treatment scenarios for lung cancer, with a focus on multimodal data fusion architectures, breakthrough applications of AI-assisted lung cancer diagnosis and treatment, and the practical barriers to clinical translation. It critically analyzes core challenges including data standardization, privacy protection, and ethical compliance. Finally, it envisions the future landscape of a nationwide intelligent ecosystem for lung cancer diagnosis and treatment—driven by federated learning and empowered by data elements—providing a reference for the standardized application and innovative development of AI in precision lung cancer care.

Ground-glass nodules (GGNs) are common imaging manifestation in the early screening of lung adenocarcinoma. With the widespread use of low-dose computed tomography (LDCT) in lung cancer screening, the detection rate of GGNs has significantly increased. According to the presence or absence of a solid component, GGNs are mainly classified into pure ground-glass nodules (pGGNs) and mixed ground-glassnodules (mGGNs), which differ in their natural course and biological behavior. In general, pGGNs tend to progress more slowly, whereas mGGNs are more likely to develop invasive features. The vast majority of pGGNs remain stable for years, but some pGGNs and mGGNs may show an increase in size or in the solid component. The "indolence" observed on the surface of GGNs hides complex genomic, metabolic, and immune changes, which are difficult to capture with traditional image-based data. In recent years, multi-omics analysis, radiomics, and artificial intelligence models have provided new tools for identifying high-risk GGNs. However, there is still controversy over the clinical generalizability, interpretability, and standardization of these models. Furthermore, there is no consensus on whether surgical resection is required. This article reviews the molecular mechanisms, metabolic, and immune microenvironment changes involved in the progression of GGNs, discusses the advantages and limitations of imaging prediction models, and combines domestic and international guidelines and survival studies to explore the controversial points in follow-up and surgical strategies, aiming to provide references for the personalized management of GGNs.

With the widespread application of low-dose computed tomography (CT), the detection rate of multiple lung cancers (MLCs) is gradually increasing. The diagnosis and treatment of MLCs have become a major challenge in clinical practice in thoracic surgery and oncology. In April 2025, the Lung Cancer Professional Committee of the China Anti-Cancer Association (CACA) organized multidisciplinary experts from both domestic and international fields to release the first edition of the CACA Expert Consensus on the Diagnosis and Treatment of Multiple Lung Cancers, providing systematic recommendations for the diagnostic system, molecularassessment strategies, and surgical and non-surgical management of MLCs. This article provides a detailed interpretation of the core content of this consensus and, by incorporating the latest research progress in the field, delves into the pathogenesis, precise diagnostic strategies, and individualized treatment pathways for multiple lung cancers, aiming to offer a more comprehensive reference for clinical practice.

To evaluate the clinical value of nebulized indocyanine green(ICG) combined with near-infrared fluorescence imaging for intraoperative detection of air leaks during pulmonary resection.

Based on plasma lipidomics combined with machine learning approaches, this study aimed to screen molecular biomarkers for the diagnosis of early-stage lung cancer in elderly patients and to evaluate their diagnostic performance.

In the tumor microenvironment, a bidirectional feedback regulation exists between aberrant angiogenesis and antitumor immunity, which provides a basis for the synergistic effects of antiangiogenic therapy and immunotherapy. The underlying mechanisms include relief of immunosuppression mediated by proangiogenic factors, induction of vascular normalization to reshape the immune microenvironment, and formation of a positive immune-vascular feedback loop. Based on these mechanisms, the combination of antiangiogenic therapy and immunotherapy has demonstrated synergistic efficacy in advanced non-small cell lung cancer (NSCLC) and is gradually being moved forward to the neoadjuvant setting. However, in the perioperative treatment of resectable lung cancer, this regimen still faces challenges, including primary resistance, a brief window of vascular normalization, and an unclear understanding of spatial interaction mechanisms. This review systematically elucidates the synergistic mechanisms of antiangiogenic agents combined with immunotherapy in NSCLC and the latest advances of this combination strategy in the neoadjuvant therapy setting, aiming to provide a reference for clinical practice.

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.

ObjectiveTo analyze the willingness to receive human papilloma virus (HPV) vaccine and its influencing factors among adult women in Qinghai Province, so as to provide scientific basis for the formulation of targeted health intervention strategies. MethodsA multi-stage whole cluster random sampling method was used to conduct a questionnaire survey on the willingness to receive HPV vaccine in eight cities or states of Qinghai Province from December 2024 to January 2025. Hierarchical multiple regression analyses were used to identify the influencing factors associated with HPV vaccination willingness. ResultsThe results of the 923 participants participated the questionnaire survey revealed that adult women’s HPV vaccination willingness scores differed statistically significant by educational level (F=9.596, P<0.001) and occupation (F=8.494, P<0.001). The hierarchical multiple regression modeling showed that perception of susceptibility (β=0.078, P<0.05), cues to action(β=0.134, P<0.05), and subjective norms (β = 0.407, P<0.001) were positively associated with HPV vaccination willingness among adult women, while perceptual disorder (β =-0.113, P<0.001) was negatively associated with it. ConclusionThe willingness to receive HPV vaccine among adult women in Qinghai Province is influenced by perception of susceptibility, perceptual disorder, cues to action, and subjective norms. Policy makers and public health agencies should therefore adopt targeted strategies, such as optimizing health education, improving vaccine accessibility, lowering barriers to vaccination, and reinforcing supportive social networks, to increase HPV vaccine uptake and ultimately achieve the goal of effective prevention, control, and elimination of cervical cancer.

Gastric polyps, as a common digestive system disease, have shown an increasing detection rate and are regarded as one of the precancerous states of gastric cancer. In TCM, gastric polyps can be classified as diseases such as "stomachache" and "fullness and distension in the stomach". TCM holds that the disease is often related to the accumulation of pathogenic factors such as phlegm and blood stasis in the body to form "kenang", which has the characteristics of insidiousness, stubbornness, and recurrence. Deficiency of healthy qi and disorder of qi movement can lead to the adhesion of phlegm and blood stasis, which highly aligns with the etiology and pathogenesis of gastric polyps. The treatment principles are promoting blood circulation to remove blood stasis, regulating qi movement, and strengthening the body resistance to consolidate the essence. Understanding the etiology, pathogenesis, and treatment principles of gastric polyps from the theory of "kenang" has important guiding significance for clinical diagnosis and treatment, providing new ideas and methods for the treatment of gastric polyps.

Non-small cell lung cancer, characterized by high incidence and mortality rates, significantly threatens human health. Precisely assessing patient prognosis and implementing adaptive treatment strategies have emerged as pivotal issues in contemporary thoracic oncology. Postoperative minimal residual disease (MRD) detection through liquid biopsy has demonstrated substantial potential. Circulating tumor DNA (ctDNA) enables real-time dynamic monitoring of tumor alterations. With advancements in ctDNA detection technologies, ctDNA has become a core method for MRD assessment. In the context of postoperative surveillance, ctDNA detection facilitates more accurate prognostic stratification and early prediction of tumor recurrence. Regarding therapeutic interventions, ctDNA detection can predict the efficacy of neoadjuvant and adjuvant therapies. In the future, further elucidating the value of assessing ctDNA status in predicting patient prognosis and guiding drug therapies will contribute to the advancement of precision medicine and adaptive treatments.