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.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

Objective To develop a symptom and function assessment scale for patients after Nuss procedure for pectus excavatum and to test its reliability and validity. Methods Following the principles and procedures of patient-reported outcome (PRO) scale development stipulated by the U.S. Food and Drug Administration, an initial draft was formed through literature analysis, qualitative interviews, and Delphi expert consultation. The preliminary draft was used to conduct a pre-survey on patients who underwent Nuss procedure for pectus excavatum at Guangdong Provincial People's Hospital, and the reliability and validity of the scale were tested. Results A preliminary PRO-based symptom and function scale was constructed, covering two domains: symptoms and impact on daily functions. The symptom dimension included six items: chest tightness, palpitations, pain, shortness of breath, foreign body sensation of the steel plate, and distress; while the impact on daily functions included four items: difficulty in getting out of bed or lying down, raising arms, bending over, and standing or sitting for a long time. A total of 73 patients who underwent Nuss procedure for pectus excavatum were included in the questionnaire survey, with 70 valid questionnaires collected, including 64 males and 6 females, with 56 patients aged 12-17 years and 14 patients ≥18 years. Through exploratory factor analysis, two common factors were extracted, with a cumulative variance contribution rate of 70%. The Cronbach’s α coefficient of the scale was 0.917. Conclusion The scale developed in this study has good reliability and validity, high reliability and stability, and can be used as an evaluation tool for the recovery status of patients after Nuss procedure for pectus excavatum.

Objective To propose a framework for risk assessment and stepwise management of pulmonary nodules based on existing guidelines and key studies, and to clarify the boundaries for triage, workup escalation, and referral in different detection scenarios. Methods A problem-oriented evidence synthesis approach was used, prioritizing Chinese expert consensus, Fleischner Society Guidelines, British Thoracic Society (BTS) Guidelines, and Lung-RADS. This was supplemented by studies on risk models, imaging measurements, liquid biopsy, tissue sampling, and surgical treatments. This paper is not presented as a formal consensus or a validated clinical pathway. Results A three-tiered framework consisting of basic non-invasive assessment, advanced assessment, and invasive diagnosis was established. It specified the applicable populations, boundaries for the use of risk models, imaging review, management of conflicting results, evaluation of histopathological consistency, and post-intervention follow-up. Liquid biopsy, genetic testing, and artificial intelligence were defined as investigational or adjunctive tools. Conclusion This framework can serve as a preliminary reference for multidisciplinary teams in developing local protocols. It cannot replace applicable guidelines, pathological diagnosis, shared decision-making with patients, or real-world validation.

Objective To explore the necessity of routine chest X-ray (CXR) examination after lung surgery based on patient symptom burden. Methods A retrospective study was conducted on the patients who underwent thoracoscopic lung resection at the Department of Thoracic Surgery of Guangdong Provincial People's Hospital from March 2020 to April 2023. Routine CXR findings obtained within 24 h post-operation, after chest tube removal, and at 1-month post-discharge, as well as subsequent clinical interventions triggered by these examinations, were collected and analyzed. The Perioperative Symptom Assessment Lung (PSA-Lung) inventory was used to quantify symptom burden including pain, dyspnea, and sleep disturbance. Results A total of 2 101 patients were collected, including 986 males and 1 115 females, with a median age of 56 years. The rate of clinical interventions triggered by routine CXR examinations was very low, being 1.0%, 0.5%, and 1.3% within 24 h post-operation, within 12 h after extubation, and at 1-month post-discharge, respectively. One month after discharge, patients in the intervention group exhibited significantly higher scores for dyspnea (3 points vs. 2 points, P=0.015), pain (2 points vs. 1 point, P=0.039), and sleep disturbance (3 points vs. 2 points, P=0.036) compared with the non-intervention group. Conclusion Very few routine postoperative CXR examinations change patients’ management, and patients who need extra-intervention tended to have more severe symptom burden after surgery.

Breast cancer remains one of the most prevalent malignancies among women worldwide,with distant organ metastasis being the leading cause of mortality.The lungs are a common site of metastasis in breast cancer and are associated with significantly poor prognosis.Although notable progress has been made in therapeutic strategies,efficacy remains markedly limited once pulmonary metastasis develops.Therefore,elucidating the molecular mechanisms that drive pulmonary metastasis,characterizing the immune microenvironment,and as-sessing therapeutic responsiveness are critical.Recent advances in multi-omics approaches-including genomics,transcriptomics,proteomics,metabolomics,and epigenetics-have been widely applied to investigate breast cancer metastasis,offering new insights into the complex mo-lecular networks underlying pulmonary dissemination.This review synthesizes current findings on key genes,proteins,metabolites,and epi-genetic regulatory mechanisms involved in the metastatic cascade from a multi-omics perspective.

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 explore the indication, surgical technique, and clinical efficacy of the cross bar based on the Nuss procedure in pectus excavatum. Methods The clinical data of patients who underwent cross bar based on the Nuss procedure from August 2023 to August 2024 in Guangdong Provincial People's Hospital were retrospectively analyzed. Results A total of 88 patients including 85 males and 3 females with a mean age of (17.56±5.20) years were enrolled. All operations were performed successfully without intraoperative cardiac injury, pericardial injury or diaphragmatic injury. The mean operation time was (147.65±47.75) min. The mean blood loss was (13.30±9.06) mL. The mean postoperative hospitalization stay was (4.81±1.55) days, without perioperative death. Six (6.82%) patients developed early postoperative complications, including 3 patients of pleural effusion, 1 patient of subcutaneous hematoma, 1 patient of suffocation and 1 patient of bar rotation. The postoperative outcomes were excellent in 71 (80.68%) patients, good in 16 (18.18%) patients and moderate in 1 (1.13%) patient. The excellent and good rate was 98.86%. Conclusion The cross bar technique is safe and convenient, with satisfactory results. It is worth promoting in clinical application.

With the widespread adoption of lung cancer screening and growing public awareness, the detection rate of pulmonary nodules has increased substantially, posing new challenges for clinical management. Artificial intelligence (AI) has emerged as a powerful tool across the entire management spectrum of pulmonary nodules. Beyond improving detection sensitivity and consistency in chest radiographs and low-dose CT, AI has demonstrated promising applications in malignancy risk assessment, molecular subtype prediction, preoperative 3D planning, intraoperative navigation, and postoperative monitoring. This review summarizes recent advances in the application of AI to pulmonary nodule screening, longitudinal evaluation, pathology prediction, multi-omics integration, and perioperative management. It also discusses the technical characteristics, clinical performance, current limitations, and future prospects of various AI models. The continuous development of AI is reshaping the clinical pathway of pulmonary nodules toward more efficient and individualized care.

Breast cancer remains one of the most prevalent malignancies among women worldwide,with distant organ metastasis being the leading cause of mortality.The lungs are a common site of metastasis in breast cancer and are associated with significantly poor prognosis.Although notable progress has been made in therapeutic strategies,efficacy remains markedly limited once pulmonary metastasis develops.Therefore,elucidating the molecular mechanisms that drive pulmonary metastasis,characterizing the immune microenvironment,and as-sessing therapeutic responsiveness are critical.Recent advances in multi-omics approaches-including genomics,transcriptomics,proteomics,metabolomics,and epigenetics-have been widely applied to investigate breast cancer metastasis,offering new insights into the complex mo-lecular networks underlying pulmonary dissemination.This review synthesizes current findings on key genes,proteins,metabolites,and epi-genetic regulatory mechanisms involved in the metastatic cascade from a multi-omics perspective.