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.

To report two cases of psychiatric symptoms associated with zonisamide, an antiepileptic drug, and raise clinical awareness of this potential adverse effect. Two male patients with epilepsy treated with zonisamide were retrospectively analyzed. Case 1 (25 years old) developed acute emotional and behavioral abnormalities (e.g., insomnia, aggression, incoherent speech) after switching from sodium valproate to zonisamide (200 mg/day). Case 2 (48 years old) had long-term zonisamide use (≥5 years) with persistent treatment-resistant psychotic symptoms (e.g., delusions, command hallucinations). Clinical courses, medication adjustments, and symptom responses were documented. In Case 1, psychiatric symptoms resolved after discontinuing zonisamide and switching to sodium valproate, with improved mood stability and reduced impulsivity. In Case 2, despite escalating antipsychotic medications (risperidone, clozapine), psychotic symptoms persisted, likely due to ongoing zonisamide use. Both cases highlighted zonisamide’s potential to exacerbate or induce psychiatric manifestations, possibly via mechanisms involving sodium/calcium channel inhibition and neurotransmitter dysregulation (e.g., dopamine, serotonin). Zonisamide can cause or worsen psychiatric symptoms, particularly in vulnerable individuals. Clinicians should monitor for mental health changes during zonisamide treatment and consider drug discontinuation or substitution with alternative antiepileptics (e.g., sodium valproate) if psychiatric adverse effects emerge. Awareness of this association is crucial to avoid misdiagnosis and optimize epilepsy management.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

OBJECTIVE To establish a method for simultaneous determination of venetoclax， busulfan and voriconazole in plasma of patients with acute myeloid leukemia （AML）， and apply it clinically. METHODS Plasma samples were subjected to protein precipitation using acetonitrile and subsequently analyzed by liquid chromatography-tandem mass spectrometry （LC-MS/MS） using venetoclax-D 8 ， busulfan-D 8 and posaconazole as internal standards. The separation was performed on a Phenomenex Kinetex ® C 18 column with a mobile phase composed of 0.1% formic acid solution （2 mmol/L ammonium acetate）-0.1% formic acid in acetonitrile （gradient elution） at a flow rate of 0.8 mL/min. The column temperature was set at 40 ℃， the sample size was 5 μL， and the total run time was 3.10 min. An electrospray ionization source was employed， and positive ion scanning was conducted using multiple reaction monitoring mode. The ion pairs used for quantitative analysis included m/z 868.4→636.3 （venetoclax）， m/z 264.1→151.1 （busulfan）， and m/z 350.1→224.0 （voriconazole）. The above LC-MS/MS method was adopted to determine plasma concentrations of venetoclax and voriconazole in 10 AML patients， as well as plasma concentration of busulfan in 5 patients undergoing conditioning treatment for allogeneic hematopoietic stem cell transplantation. RESULTS The linear ranges of venetoclax， busulfan and voriconazole were 50-10 000， 15-3 000 and 50-10 000 ng/mL， respectively （ R 2 ≥0.999 0）， with lower limits of quantification of 50， 15 and 50 ng/mL， respectively. The RSDs of intra-day and inter-day precision tests for all three analytes were all less than 10%， with accuracy （relative errors） ranging from －10.00% to 12.96%. The average extraction recovery ranged from 92.54% to 100.95%， and the average matrix effect was 89.98%-101.49%. Dilution reliability covered all dilution factors used in the test samples， and the absolute values of relative errors in stability tests were all≤16.25%. The plasma concentrations of venetoclax， busulfan and voriconazole in enrolled patients were 496.20-4 250.45， 233.48-2 002.28 and 475.51-5 710.18 ng/mL， respectively. CONCLUSIONS The LC-MS/MS method established in this study is rapid， sensitive and easy to operate， and can be used for the therapeutic drug monitoring of venetoclax， busulfan and voriconazole.

Objective To investigate the value of 3.0 T MRI in-phase/opposed-phase (IP-OP) imaging features in the quantitative assessment of hepatic steatosis after liver transplantation. Methods Clinical data of 115 patients who underwent liver transplantation and completed MRI IP-OP examination and liver biopsy within 3 months were retrospectively collected. According to the gold standard of pathological results, patients were divided into the steatosis group (n=18) and the non-steatosis group (n=97). The relative signal intensity of hepatic parenchyma was measured on MRI IP-OP sequences, and the hepatic fat fraction (HFF) was calculated. Differences in clinical data and imaging features between the two groups were compared, and the efficacy of HFF in the diagnosis of graft hepatic steatosis was evaluated using the receiver operating characteristic (ROC) curve. Results Among the 115 recipients, 18 cases were diagnosed with hepatic steatosis by liver biopsy, including 13 cases of mild steatosis, 3 cases of moderate steatosis and 2 cases of severe steatosis. Compared with the non-steatosis group, the steatosis group had a lower proportion of hepatitis B virus (HBV)-related hepatocellular carcinoma, higher proportions of HBV-related liver failure and alcoholic liver cirrhosis, and higher platelet levels (all P<0.05). There were statistically significant differences in hepatic imaging features between the two groups (all P<0.05). The ROC curve showed that the area under the curve (AUC) of the quantitative value calculated by IP-OP in the diagnosis of graft hepatic steatosis was 0.925 (95% confidence interval 0.870-0.980). Conclusions 3.0 T MRI IP-OP sequence may accurately and non-invasively quantitatively assess the severity of hepatic steatosis after liver transplantation, which is of great value for the early detection of graft hepatic steatosis and the guidance of clinical intervention.

ObjectiveTo construct a stable monoclonal human embryonic kidney 293 （HEK293） cell line expressing recombinant human coagulation factor Ⅶ （rhFⅦ） and evaluate the expression level and procoagulant bioactivity of rhFⅦ. MethodsThe plasmid pCDNA3.1-EGFP-FⅦ was transfected into HEK293 cells to verify the effectiveness of the transfection system. The plasmid pCDNA3.1-FⅦ was transfected into HEK293 cells， and monoclonal stable transfected cell lines were selected using geneticin （G418）. The transcription of the FⅦ gene was identified by reverse transcription polymerase chain reaction （RT-PCR）. The expression level of rhFⅦ in the supernatant of the monoclonal stable transfected cell line was detected by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Western blot. The concentration of rhFⅦ was determined by enzyme-linked immunosorbent assay （ELISA）， and the procoagulant activity of rhFⅦ was measured by human coagulation factor Ⅶ potency assay. ResultsHEK293 cells transfected with pcDNA3.1-EGFP-FⅦ showed green fluorescence， indicating that rhFⅦ was successfully expressed in the supernatant of HEK293 cells after transient transfection with pcDNA3.1-FⅦ. The monoclonal stable transfected cell line was obtained by G418 screening. RT-PCR identified that the FⅦ gene was integrated into the genome of the monoclonal stable transfected cell line. The cell viability was good as detected by Cell Counting Kit-8， and a single band of rhFⅦ was obtained by purification of the cell supernatant. The highest rhFⅦ expression was （1.27±0.09） mg/L， and the highest procoagulant activity was （380.29±13.80）%. ConclusionThe monoclonal HEK293 cell lines which can express rhFⅦ protein efficiently and stably with excellent procoagulant bioactivity is successfully screened.

Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

OBJECTIVE To explore and establish a full-cycle management model for drug traceability codes that aligns with national policy requirements and the practical needs of healthcare institutions， thereby enhancing the refinement of drug management and the level of medication safety. METHODS A tripartite strategy integrating “hardware deployment， system transformation， and process re-engineering” was adopted. This involved the introduction of intelligent identification devices （personal digital assistant， high-definition industrial reader）， the modification of the hospital information system interface， and the re-engineering of workflows （drug warehousing， dispensing and distribution， drug withdrawal， uploading to the insurance platform） to achieve comprehensive， informatized collection and association of drug traceability codes throughout all stages. RESULTS A full-cycle management model for drug traceability codes was successfully established， realizing the goals of making drugs “traceable to their source， trackable in their distribution， and accountable in their responsibility”. The patient waiting time for medication dispensing before and after the implementation was [3.08（1.67，5.58）] min and [3.28（1.77，5.98）] min， respectively. Among them， the patient waiting time under the pre-preparation mode was [3.60（2.13，6.35）] min and [3.50（2.03，6.30）] min， respectively； the patient waiting time under the real-time mode was [2.05（0.83，4.03）] min and [2.78（1.18，5.38）] min， respectively； the number of dispensing errors was 3， 0， respectively； the staffing of relevant positions had not been increased. CONCLUSIONS The drug traceability code management model constructed from a full-cycle perspective effectively meets national policy requirements. It provides data support for refined hospital management and offers solid technical and procedural safeguards for ensuring patient medication safety and strengthening medical insurance fund supervision， demonstrating practical value.

High myopia has become a major public health concern worldwide, particularly in China and Southeast Asia. It is associated not only with a variety of fundus diseases but also with earlier onset and greater severity of cataracts, resulting in significant visual impairment. Phacoemulsification is currently the main surgical treatment for cataracts. However, intraoperative fluctuations in perfusion pressure and exposure to ultrasonic energy may affect the vitreoretinal structures, potentially accelerating the progression of fundus pathology after surgery in highly myopic eyes. This article summarizes current evidence on the progression of posterior scleral staphyloma, myopic maculopathy, and retinal detachment following phacoemulsification in highly myopic eyes.