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.

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.

Lumbar disc (LD) herniation and aging are prevalent conditions that can result in substantial morbidity. This study aimed to clarify the mechanisms connecting the LD aging and herniation, particularly focusing on cellular senescence and molecular alterations in the nucleus pulposus (NP). We performed a detailed analysis of NP samples from a diverse cohort, including individuals of varying ages and those with diagnosed LD herniation. Our methodology combined histological assessments with single-nucleus RNA sequencing to identify phenotypic and molecular changes related to NP aging and herniation. We discovered that cellular senescence and a decrease in nucleus pulposus progenitor cells (NPPCs) are central to both processes. Additionally, we found an age-related increase in NFAT1 expression that promotes NPPC senescence and contributes to both aging and herniation of LD. This research offers fresh insights into LD aging and its associated pathologies, potentially guiding the development of new therapeutic strategies to target the root causes of LD herniation and aging.
Intervertebral Disc Displacement/metabolism* ; Humans ; Aging/pathology* ; Nucleus Pulposus/pathology* ; Male ; Female ; Transcriptome ; Middle Aged ; Lumbar Vertebrae/pathology* ; Adult ; Cellular Senescence ; Stem Cells/pathology* ; Aged ; Intervertebral Disc Degeneration/metabolism*

OBJECTIVE: To explore the genetic basis of a patient suspected for Loeys-Dietz syndrome (LDS). METHODS: A adult male patient with aneurysmal dilation of the aortic root identified during the treatment for chronic myeloid leukemia at Anzhen Hospital of Capital Medical University in 2021 was selected as the study subject. Clinical data of the patient were retrospectively collected. Peripheral blood samples were collected from the patient and his family members and subjected to whole-exome sequencing (WES). Candidate variant was verified by bioinformatic analysis, with a focus on the genes associated with hereditary aortic aneurysms. Candidate variant was validated by Sanger sequencing. The online SpliceAI software was used for the prediction of protein function. The results, combined with information from public databases, were used to classify the pathogenicity of the candidate variant according to the guidelines from the American College of Medical Genetics and Genomics (ACMG). This study was approved by the Ethics Committee of Beijing Anzhen Hospital (Ethics No. 2023163X). RESULTS: Imaging analysis revealed that the patient had aneurysmal dilation of the aortic root. Based on his clinical features and past history, a provisional diagnosis of LDS was established. WES revealed that the patient had harbored a heterozygous splice site variant c.206+2T>G in the SMAD3 gene (NM_005902). The variant was not reported in public databases and was predicted to be pathogenic by SpliceAI. Sanger sequencing showed that the variant was also present in the proband's mother, sister, nephew, and daughter, but not in his father. Based on the guidelines from the ACMG, the variant was classified as likely pathogenic (PVS1+PM2_Supporting). CONCLUSION The heterozygous splice site variant c.206+2T>G of the SMAD3 gene probably underlay the disease in this patient. Above discovery has enriched the mutational spectrum of LDS, which may facilitate delineation of the genotype-phenotype correlation and provide a basis for further risk stratification and personalized treatment of LDS.
Adult ; Humans ; Male ; Exome Sequencing ; Loeys-Dietz Syndrome/genetics* ; Mutation ; Pedigree ; Smad3 Protein/genetics*

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Humans ; Asthma/drug therapy* ; Biological Therapy

Humans ; East Asian People ; Nausea/drug therapy* ; Vomiting/drug therapy* ; Antineoplastic Agents/adverse effects* ; Neoplasms/drug therapy*

Humans ; Multiple Pulmonary Nodules ; Lung ; Lung Neoplasms/surgery* ; Solitary Pulmonary Nodule/surgery* ; Bronchoscopy