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.

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 compare the efficacy， safety， and cost-effectiveness of two different formulations of short-acting recombinant human growth hormone （rhGH） in the treatment of patients with short stature. METHODS Data from patients with short stature treated with short-acting rhGH at the Leshan People’s Hospital from August 2016 to June 2023 were collected. Patients were divided into powder formulation group and aqueous formulation group based on the rhGH formulation used. The changes in growth-related efficacy indicators and the occurrence of adverse drug reactions were compared between two groups after 12 months of treatment； cost-effectiveness analysis and sensitivity analysis were used to compare the cost per unit of effect achieved； subgroup analysis was performed by dividing the patients into growth hormone deficiency （GHD） subgroup and idiopathic short stature （ISS） subgroup based on clinical diagnosis. RESULTS After 12 months of treatment， the height and the levels of insulin-like growth factor-1 and insulin-like growth factor binding protein-3 in serum in aqueous formulation group and powder formulation group were significantly increased compared to before treatment （P＜0.001）， but there was no statistically significant difference in the changes of the above indicators between the two groups（P＞0.05）. The analysis results of GHD and ISS subgroups were consistent with the overall population. In the overall population， the cost-effectiveness ratio of powder formulation group （2 582 yuan/cm） was significantly better than that of aqueous formulation group （6 729 yuan/cm）， with a statistically significant difference （P＜0.001）， and the result was consistent in the GHD and ISS subgroups as well as in the sensitivity analysis. No serious adverse drug reactions occurred in either powder formulation or aqueous formulation group， and there was no statistically significant difference in the incidence of various adverse reactions between two groups （P＞0.05）. CONCLUSIONS Short-acting rhGH powder and aqueous formulations have equivalent efficacy and safety， but the powder formulation has greater economic advantages.

Objective:To mine the risk signals of adverse events (AEs) in mavacamten treatment for hypertrophic cardiomyopathy, and provide reference for safe use of the drug in clinic.Methods:AE reports on mavacamten from June 2022 to June 2024 were collected by searching US Food and Drug Administration Adverse Event Reporting System (FAERS) database. AEs were classified and standardized according to the system organ class (SOC) and preferred term (PT) of Medical Dictionary for Regulatory Activities version 26.1. Reporting odds ratio (ROR) method and comprehensive standard method of the UK Medicines and Healthcare Products Regulatory Agency (MHRA) were used to mine the AE risk signals. An AE that simultaneously met the criteria of ≥3 reports, lower limit of the 95% confidence interval ( CI) of ROR>1, PRR ≥2, and χ2 ≥4 was defined as a risk signal. Descriptive statistical analysis on signals was performed. Results:A total of 1 041 AE reports were collected, involving 47 PTs and 12 SOCs. The top 10 risk signals based on the number of AE reports were dyspnea, dizziness, fatigue, atrial fibrillation, cardiac failure, palpitation, nasopharyngitis, chest pain, COVID-19, and weight increased. Except dizziness and heart failure, above AEs were not recorded in the label. The top 10 risks in signal intensity were acquired left ventricle outflow tract obstruction, transvalvular pressure gradient increased, cardiovascular symptom, echocardiogram abnormal, hypervolaemia, left ventricular failure, ejection fraction decreased, coronavirus infection, brain fog, and atrial fibrillation. Except cardiovascular symptom, left ventricular failure, and ejection fraction decreased, above AEs were not recorded in the label.Conclusions:The AE risk signals of mavacamten in the treatment for hypertrophic cardiomyopathy recorded in the label are mainly heart failure and ejection fraction decreased. Clinicians and pharmacists should also be vigilant against risk signals not recorded in the lakel, such as atrial fibrillation, fatigue, nasopharyngitis, coronavirus infection, and brain fog, etc.

Objective:To investigate the implementation of boxed warning of drug labels in US Food and Drug Administration (FDA) from 2019 to 2024, and compared it with the relevant situations in China, in order to provide reference for the revision of drug labels and safe drug use.Methods:Data on boxed warning revisions in the US FDA "Drugs Safety Related Labeling Changes" Database from January 1, 2019 to December 31, 2024 were retrieved, and the revised contents were classified. The drug labels for newly marketed drugs in the United States during the same period were collected, the boxed warnings were recorded and summarized, and compared with the warning statements in drug labels of relevant drugs approved in China.Results:From 2019 to 2024, FDA revised boxed warnings in 209 drug labels. Among them, 22 items (10.53%) were newly added, 63 items (30.14%) were major updates, 115 items (55.02%) were minor updates, and 9 items (4.31%) were removed. A total of 293 new drugs were approved in the United States from 2019 to 2024, of which 69 (23.55%) had boxed warnings when they were approved, and 4 (1.37%) were added boxed warnings when they were revised in the labels. Up to December 31, 2024, 92 of the 293 new drugs had been approved in China. Compared with the labeling in the United States, some drugs lacked warning statements section in China, including zolpidem tartrate, dexzopiclone, zaleplon, montelukast sodium, denosumab, terlipressin, etc.Conclusions:The warning statements in some Chinese drug labels are inconsistent with the boxed warnings in the American drug labels. It is suggested that the revision of the boxed warning by US FDA should be regarded as one of the new sources of safety information to assess the risks of related drugs and determine whether it is necessary to revise the relevant drug labels in China.

Objective:To investigate the implementation of boxed warning of drug labels in US Food and Drug Administration (FDA) from 2019 to 2024, and compared it with the relevant situations in China, in order to provide reference for the revision of drug labels and safe drug use.Methods:Data on boxed warning revisions in the US FDA "Drugs Safety Related Labeling Changes" Database from January 1, 2019 to December 31, 2024 were retrieved, and the revised contents were classified. The drug labels for newly marketed drugs in the United States during the same period were collected, the boxed warnings were recorded and summarized, and compared with the warning statements in drug labels of relevant drugs approved in China.Results:From 2019 to 2024, FDA revised boxed warnings in 209 drug labels. Among them, 22 items (10.53%) were newly added, 63 items (30.14%) were major updates, 115 items (55.02%) were minor updates, and 9 items (4.31%) were removed. A total of 293 new drugs were approved in the United States from 2019 to 2024, of which 69 (23.55%) had boxed warnings when they were approved, and 4 (1.37%) were added boxed warnings when they were revised in the labels. Up to December 31, 2024, 92 of the 293 new drugs had been approved in China. Compared with the labeling in the United States, some drugs lacked warning statements section in China, including zolpidem tartrate, dexzopiclone, zaleplon, montelukast sodium, denosumab, terlipressin, etc.Conclusions:The warning statements in some Chinese drug labels are inconsistent with the boxed warnings in the American drug labels. It is suggested that the revision of the boxed warning by US FDA should be regarded as one of the new sources of safety information to assess the risks of related drugs and determine whether it is necessary to revise the relevant drug labels in China.

Objective:To mine the risk signals of adverse events (AEs) in mavacamten treatment for hypertrophic cardiomyopathy, and provide reference for safe use of the drug in clinic.Methods:AE reports on mavacamten from June 2022 to June 2024 were collected by searching US Food and Drug Administration Adverse Event Reporting System (FAERS) database. AEs were classified and standardized according to the system organ class (SOC) and preferred term (PT) of Medical Dictionary for Regulatory Activities version 26.1. Reporting odds ratio (ROR) method and comprehensive standard method of the UK Medicines and Healthcare Products Regulatory Agency (MHRA) were used to mine the AE risk signals. An AE that simultaneously met the criteria of ≥3 reports, lower limit of the 95% confidence interval ( CI) of ROR>1, PRR ≥2, and χ2 ≥4 was defined as a risk signal. Descriptive statistical analysis on signals was performed. Results:A total of 1 041 AE reports were collected, involving 47 PTs and 12 SOCs. The top 10 risk signals based on the number of AE reports were dyspnea, dizziness, fatigue, atrial fibrillation, cardiac failure, palpitation, nasopharyngitis, chest pain, COVID-19, and weight increased. Except dizziness and heart failure, above AEs were not recorded in the label. The top 10 risks in signal intensity were acquired left ventricle outflow tract obstruction, transvalvular pressure gradient increased, cardiovascular symptom, echocardiogram abnormal, hypervolaemia, left ventricular failure, ejection fraction decreased, coronavirus infection, brain fog, and atrial fibrillation. Except cardiovascular symptom, left ventricular failure, and ejection fraction decreased, above AEs were not recorded in the label.Conclusions:The AE risk signals of mavacamten in the treatment for hypertrophic cardiomyopathy recorded in the label are mainly heart failure and ejection fraction decreased. Clinicians and pharmacists should also be vigilant against risk signals not recorded in the lakel, such as atrial fibrillation, fatigue, nasopharyngitis, coronavirus infection, and brain fog, etc.

Healthy lifestyles and good living habits are effective strategies and important approaches to prevent chronic non-communicable diseases. With the development of evidence-based medicine, the evidence translation system has made some achievements in clinical practice. There is, however, no comprehensive, professional and efficient system for translating lifestyle evidence globally. Therefore, the Health Lifestyle Evidence (HLSE) Group of Lanzhou University constructed the HLSE Evidence Translation System (https://hlse.cn/), with the aim of synthesizing, evaluating, translating, and applying lifestyle-related evidence resources. This paper aims to introduce the functional module design of the evidence translation system, the system development process and testing, introduce the interface design and function demonstration, and explain the significance of constructing the HLSE.

Healthy lifestyles and good living habits are effective strategies and important approaches to prevent chronic non-communicable diseases. With the development of evidence-based medicine, the evidence translation system has made some achievements in clinical practice. There is, however, no comprehensive, professional and efficient system for translating lifestyle evidence globally. Therefore, the Health Lifestyle Evidence (HLSE) Group of Lanzhou University constructed the HLSE Evidence Translation System (https://hlse.cn/), with the aim of synthesizing, evaluating, translating, and applying lifestyle-related evidence resources. This paper aims to introduce the functional module design of the evidence translation system, the system development process and testing, introduce the interface design and function demonstration, and explain the significance of constructing the HLSE.