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.

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.

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.

Under the background of carbon peaking and carbon neutrality goals, the Ministry of Ecology and Environment, together with 15 national ministries and commissions, has formulated the Implementation Plan on Establishing a Carbon Footprint Management System, and it is urgent for traditional Chinese medicine(TCM) pharmaceutical enterprises to carry out research on carbon footprint accounting methods of related products. Based on the life cycle assessment(LCA) theory, taking mulberry leaf extract produced by a certain enterprise as an example, this study analyzed the carbon footprint of TCM extracts during the life cycle. The results show that for every 1 kg of product produced, the carbon emissions from the stages of raw material acquisition, transportation, and extract production are-20.569, 1.205, and 173.577 kgCO_2eq(CO_2 equivalent), respectively. The carbon footprint of the product is 154.213 kgCO_2eq·kg~(-1). In addition, the carbon emission is the highest in the production stage, in which the consumption of ethanol solvents makes the greatest contribution to the carbon footprint, accounting for 25.71%, more than one-fourth of the total carbon footprint. The second contribution was from the treatment process of TCM residues, accounting for 19.67%, closely followed by wastewater treatment(17.71%), the consumption of hot steam(17.43%), and drinking water(16.90%). The consumption of electric power and packaging materials has a smaller carbon emission of 2.58%. In particular, the carbon emission caused by the consumption of packaging materials is only 0.04%, which is negligible. The results of the study are expected to provide a reference for TCM enterprises to carry out research on the carbon footprint of products, offer ideas for collaborative innovation in reducing pollution and carbon emissions throughout the entire industry chain of TCM, and develop new quality productivity of modern TCM industry based on green and low-carbon manufacturing.
Morus/chemistry* ; Plant Leaves/chemistry* ; Carbon Footprint ; Drugs, Chinese Herbal/chemistry* ; Plant Extracts/analysis* ; Medicine, Chinese Traditional

Objective To compare the benefits and drawbacks of primary patch expansion versus pericardial tube right ventricular-pulmonary artery connection in patients diagnosed with pulmonary atresia with ventricular septal defect (PA/VSD). Methods A retrospective study was conducted on patients diagnosed with PA/VSD who underwent primary right ventricular-pulmonary artery connection surgery at our center between 2010 and 2020. Patients were categorized into two groups based on the type of right ventricular-pulmonary artery connection: a pericardial tube group and a patch expansion group. Clinical data and imaging findings were compared between the two groups. Results A total of 51 patients were included in the study, comprising 31 males and 20 females, with a median age of 12.57 (4.57, 49.67) months. The pericardial tube group included 19 patients with a median age of 17.17 (7.33, 49.67) months, while the patch expansion group consisted of 32 patients with a median age of 8.58 (3.57, 52.72) months. In both groups, the diameter of pulmonary artery, McGoon index, and Nakata index significantly increased after treatment (P<0.001). However, the pericardial tube group exhibited a longer extracorporeal circulation time (P<0.001). The reoperation rate was notably high, with 74.51% of patients requiring further surgical intervention, including 26 (81.25%) patients in the patch expansion group and 12 (63.16%) patients in the pericardial tube group. No statistical differences were observed in long-term cure rates or mortality between the two groups (P＞0.005). Conclusion In patients with PA/VSD, both patch expansion and pericardial tube right ventricular-pulmonary artery connection serve as effective initial palliative treatment strategies that promote pulmonary vessel development and provide a favorable foundation for subsequent radical operations. However, compared to the pericardial tube approach, the patch expansion technique is simpler to perform and preserves some intrinsic potential for pulmonary artery development, making it the preferred procedure.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

OBJECTIVE: Investigating the clinical efficacy of treating dorsally displaced distal radial double-column Die-punch fractures using a dorsal approach external fixator combined with Kirschner wires. METHODS: Retrospectively analyzed the clinical data of 15 patients with distal radial double-column Die-punch fractures treated with an external fixator combined with Kirschner wire between July 2020 and January 2023. There were 10 males and 5 females;6 cases on the left side and 9 on the right;age ranged from 22 to 76 years old. Recorded the preoperative and the final follow-up Cooney wrist function scores for the patients. The fracture healing time, and occurrence of complications were recorded. RESULTS: All 15 patients were followed up ranged from 12 to 16 months post-operation. All fractures achieved bony union, healing time ranging form 8 to 16 weeks. Not a single patient exhibited complications such as surgical site infection, fracture redislocation, or tendon injury. All individuals had their Kirschner wires and external fixation devices removed six weeks post-operatively and commenced rehabilitative therapy for wrist articulation. The Cooney wrist function scores at preoperative and ranged from 5 to 45 scores, at the latest follow-up ranged from 65 to 100 scores. At the final follow-up, the results were assessed as excellent in 10 patients, good in 4 patients, and fair in 1 patient. CONCLUSION The clinical efficacy of treating distal radial double-column Die-punch fractures using a dorsal approach external fixator combined with Kirschner wires is satisfactory.
Humans ; Male ; Female ; Middle Aged ; Adult ; External Fixators ; Bone Wires ; Aged ; Retrospective Studies ; Radius Fractures/physiopathology* ; Young Adult ; Fracture Fixation/methods*

OBJECTIVE: To reveal the molecular basis of knee osteoarthritis (KOA) with Yang deficiency and blood stasis syndrome by analyzing the gene expression profiles in synovial fluid and blood of KOA patients with this syndrome. METHODS: A total of 80 KOA patients were recruited from October 2022 to June 2024, including 40 cases in the non-Yang deficiency and blood stasis group (27 males and 13 females), with an average age of (61.75±3.45) years old;and 40 cases in the Yang deficiency and blood stasis group (22 males and 18 females), with an average age of (62.00±2.76) years old. The levels of body mass index (BMI), high-density lipoprotein (HDL), low-density lipoprotein (LDL), fibrinogen, total cholesterol, and D-dimer were recorded and summarized. Blood and synovial fluid samples from patients were collected for gene expression profile microarray sequencing, and then PCR and immunohistochemistry were used for clinical verification on the patients' synovial fluid and cartilage samples. RESULTS: Logistic regression analysis showed that compared with KOA patients with non-Yang deficiency and blood stasis syndrome, those with Yang deficiency and blood stasis syndrome had increased BMI, LDL, fibrinogen, total cholesterol, and D-dimer, and decreased HDL, with a clear correlation between the two groups. There were 562 differential genes in the blood, among which 322 were up-regulated and 240 were down-regulated;755 differential genes were found in the synovial fluid, with 350 up-regulated and 405 down-regulated. KEGG signaling pathway analysis of synovial fluid revealed changes in lipid metabolism-related pathways, including cholesterol metabolism, fatty acid metabolism, and PPARG signaling pathway. Analysis of the involved differential genes identified 6 genes in synovial fluid that were closely related to lipid metabolism, namely LRP1, LPL, ACOT6, TM6SF2, DGKK, and PPARG. Subsequently, PCR and immunohistochemical verification were performed using synovial fluid and cartilage samples, and the results were consistent with those of microarray sequencing. CONCLUSION This study explores the clinical and genomic correlation between traditional Chinese medicine syndromes and knee osteoarthritis from the perspective of lipid metabolism, and proves that abnormal lipid metabolism is closely related to KOA with Yang deficiency and blood stasis syndrome from both clinical and basic aspects.
Humans ; Male ; Female ; Middle Aged ; Synovial Fluid/metabolism* ; Osteoarthritis, Knee/metabolism* ; Yang Deficiency/complications* ; Aged