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.

Objective:To evaluate the relationship between postoperative changes in femoral head coverage (FHC) after S 2-Alar-Iliac (S 2AI) screw fixation and the development of sagittal imbalance during follow-up in patients with adult spinal deformity (ASD), providing insights for clinical assessment and treatment strategies. Methods:A consecutive cohort of 98 ASD patients who underwent S2AI fixation between September 2019 and September 2021 was retrospectively analyzed. Patients were divided into two groups based on changes in femoral head coverage (ΔFHC): the FHC-C group (upper quartile ΔFHC, 25 cases) and the FHC-NC group (lower quartile ΔFHC, 24 cases). Additionally, patients were classified into proximal junctional kyphosis (PJK) and non-PJK groups based on their clinical outcomes at the last follow-up. Standing full-spine anteroposterior and lateral X-rays were taken preoperatively, postoperatively, and at the two-year follow-up to measure and document the following spinal parameters: Cobb angle, proximal lumbar lordosis (PLL), distal lumbar lordosis (DLL), lumbar lordosis (LL), lordosis distribution index (LDI), sagittal vertical axis (SVA), coronal balance distance (CBD), thoracic kyphosis (TK), T 1 pelvic angle (T 1PA), pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), PI-LL, and proximal junctional angle (PJA). Parameters related to hip joint coverage included: femoral head coverage (FHC), lateral center-edge angle (LCE angle), acetabular index (AI), Sharp angle, and extrusion index (EI). Comparisons of radiographic indicators between the two groups were performed at preoperative, postoperative, and final follow-up assessments. The visual analogue scale (VAS) was used to evaluate the hip pain and back pain. Results:At final follow-up, the incidence of PJK was significantly higher in the FHC-NC group [37.5% (9/24)] compared to the FHC-C group [16.0% (4/25)] (χ 2=3.952, P=0.042). Moreover, the increase in sagittal vertical axis (ΔSVA) was significantly greater in the FHC-NC group (35.9±44.7 mm vs. 14.6±31.8 mm, t=2.216, P=0.031). Patients with PJK had significantly higher preoperative T 1PA (36.8°±10.8° vs. 31.9°±18.4°, t=2.150, P=0.034) and lower immediate postoperative ΔFHC (1.7%±1.5% vs. 3.3%±2.5%, t=2.987, P=0.004), as well as lower changes in lateral center-edge angle during follow-up (0.3°±3.0° vs. 1.1°±8.9°, t=2.334, P=0.022). Pearson correlation analysis revealed significant negative correlations between postoperative ΔFHC and both ΔSVA ( r=-0.374, P=0.008) and proximal junctional angle changes (ΔPJA, r=-0.429, P=0.006). Additionally, increases in VAS leg pain scores correlated negatively with immediate postoperative FHC ( r=-0.314, P=0.025) and ΔFHC ( r=-0.298, P=0.031). Logistic regression indicated that immediate postoperative ΔFHC was a protective factor against PJK [ OR=0.722, 95% CI (0.541, 0.963), P=0.009), with a ROC-determined optimal ΔFHC cut-off of 3.90% (AUC=0.723, Youden index=0.847). Conclusions:Postoperative evaluation of femoral head coverage is clinically important for ASD patients undergoing S2AI screw fixation. A pre-to-post ΔFHC below 3.90% may indicate reduced hip compensation capacity, increasing risks for hip pain, sagittal imbalance progression, and PJK postoperatively.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Objective:To explore the structural relationship between WMSDs in the upper limbs and various risk factors in the occupational population in China, based on a large sample epidemiological survey and structural equation analysis, and to establish a structural equation model, so as to lay a foundation for the prevention and control of such diseases.Methods:The Chinese version of the Musculoskeletal Disorders Electronic Questionnaire was used to conduct a nationwide survey on the prevalence of WMSDs in the upper extremity. Six factors related to WMSDs in the upper extremity were extracted by the classification standard of adverse ergonomic factors and their source and confirmatory factor analysis, including work organization, work type, upper extremity work posture, individual factors, upper extremity fatigue and upper extremity WMSDs. The structural equation analysis was carried out and the structural equation model was established.Results:The incidence of WMSDs and fatigue in the upper limbs was 24.44% and 43.76%, respectively. The adjusted structural equation model fitting indicators were generally up to the standard (GFI=1.000, AGFI=1.000, RMSEA=0.043, NFI=0.808, TLI=0.784) . The four exogenous latent variables of work organization, work type, upper limb work posture and individual factors were correlated. There was a strong positive correlation between job type and upper limb work posture ( r=0.865) , a moderate positive correlation between work organization and job type and upper limb work posture ( r=0.570, 0.490) , and a weak negative correlation between individual factors and the other three exogenous latent variables. Upper limb work posture and individual factors had direct effects on upper limb WMSDs, and the effect coefficients were 0.10 and 0.06, respectively. Upper limb fatigue played a mediating role between work organization, work type, upper limb work posture and upper limb WMSDs. The effect coefficient was 0.46, and the composition ratios of indirect effects were 100.0%, 100.0%, and 38.3%, respectively. The direct path effect of upper limb work posture, individual factors and upper limb WMSDs was weaker than the mediating path through upper limb fatigue. Conclusion:When carrying out the prevention and control of upper limbWMSDs, it is necessary to comprehensively consider the pathogenesis path of upper limb muscle fatigue and upper limb WMSDs caused by work organization, work type, and upper limb work posture, so as to provide theoretical reference for improving the prevention and control level of such diseases.

This study analyzed the genetic evolutionary characteristics of sandflies and their effects on the spread of kala-azar in various environments in endemic provinces in China,to provide a scientific basis for kala-azar disease prevention and control.Sand-flies were collected in kala-azar endemic areas such as southern Xinjiang,the large hilly areas of southern Gansu,the northern Sich-uan and Taihang Mountains,and surrounding small hills.The cytochrome c oxidase subunit I and cytochrome b gene fragments of mito-chondrial DNA were amplified to identify sandfly species.The COI and Cytb gene sequences of sandflies from southern Xinjiang and Si-chuan recorded in NCBI were also collected.The intraspecific and interspecific genetic differences of sandflies were calculated in MEGA11.0,and a phylogenetic tree was constructed through the neighbor-joining method,for analysis of the genetic and evolutionary characteristics of sandfly populations and their effects on disease transmission.A total of 155 sandflies were collected from nine sam-pling sites in seven provinces of China;the species included Phlebotomus chinensis,Phlebotomus wui,and Sergentomyia squamirostris.Five sandfly species belonging to two genera were collected:P.chinensis,P.wui,and Phlebotomus alexandri in the genus Phleboto-mus,and S.squamirostris in the genus Sergentomyia.Genetic evolution analysis based on COI and Cytb gene sequences indicated intra-specific genetic distances of 0-0.062 and 0-0.056,respectively,and interspecific genetic distances of 0.126-0.176 and 0.110-0.171,respectively.The phylogenetic tree indicated that P.wui,P.alexandri,Phlebotomus longiductus,and S.squamirostris clus-tered into one branch.The sequences of P.chinensis in the large and small hilly areas clustered into two geographical clades.In the small hilly areas,the sequences of P.chinensis aggregates showed small genetic differences,the pathogen infection was consistent,and the cases showed an epidemic spread trend.Large genetic differences at the molecular level were observed among sandflies in dif-ferent ecological regions,thus indicating key effects on leishmaniasis transmission.On the basis of these findings,prevention and con-trol strategies should be adapted to local conditions,and precise and effective prevention and control measures should be formulated according to the genetic evolution characteristics of sandflies in different regions,to better control the transmission of Kala-azar.

The European Society for Medical Oncology Asia Congress 2024 was held in Singapore from December 6 to 8,2024.The conference unveiled several groundbreaking studies in the field of hepatobiliary and pancreatic tumors,covering clinical applications related to neoadjuvant and adjuvant therapies,translational treatments,later-line therapies,and tumor biomarkers.These studies provide new insights into the clinical diagnosis and treatment of hepatobiliary and pancreatic malignancies and drive the development of related fields.This article focuses on the key topics in hepatobiliary and pancreatic malignancies presented at the conference,aiming to interpret the latest advances in the field and explore the hot issues and future directions for development in this area.

Objective:To evaluate the relationship between postoperative changes in femoral head coverage (FHC) after S 2-Alar-Iliac (S 2AI) screw fixation and the development of sagittal imbalance during follow-up in patients with adult spinal deformity (ASD), providing insights for clinical assessment and treatment strategies. Methods:A consecutive cohort of 98 ASD patients who underwent S2AI fixation between September 2019 and September 2021 was retrospectively analyzed. Patients were divided into two groups based on changes in femoral head coverage (ΔFHC): the FHC-C group (upper quartile ΔFHC, 25 cases) and the FHC-NC group (lower quartile ΔFHC, 24 cases). Additionally, patients were classified into proximal junctional kyphosis (PJK) and non-PJK groups based on their clinical outcomes at the last follow-up. Standing full-spine anteroposterior and lateral X-rays were taken preoperatively, postoperatively, and at the two-year follow-up to measure and document the following spinal parameters: Cobb angle, proximal lumbar lordosis (PLL), distal lumbar lordosis (DLL), lumbar lordosis (LL), lordosis distribution index (LDI), sagittal vertical axis (SVA), coronal balance distance (CBD), thoracic kyphosis (TK), T 1 pelvic angle (T 1PA), pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), PI-LL, and proximal junctional angle (PJA). Parameters related to hip joint coverage included: femoral head coverage (FHC), lateral center-edge angle (LCE angle), acetabular index (AI), Sharp angle, and extrusion index (EI). Comparisons of radiographic indicators between the two groups were performed at preoperative, postoperative, and final follow-up assessments. The visual analogue scale (VAS) was used to evaluate the hip pain and back pain. Results:At final follow-up, the incidence of PJK was significantly higher in the FHC-NC group [37.5% (9/24)] compared to the FHC-C group [16.0% (4/25)] (χ 2=3.952, P=0.042). Moreover, the increase in sagittal vertical axis (ΔSVA) was significantly greater in the FHC-NC group (35.9±44.7 mm vs. 14.6±31.8 mm, t=2.216, P=0.031). Patients with PJK had significantly higher preoperative T 1PA (36.8°±10.8° vs. 31.9°±18.4°, t=2.150, P=0.034) and lower immediate postoperative ΔFHC (1.7%±1.5% vs. 3.3%±2.5%, t=2.987, P=0.004), as well as lower changes in lateral center-edge angle during follow-up (0.3°±3.0° vs. 1.1°±8.9°, t=2.334, P=0.022). Pearson correlation analysis revealed significant negative correlations between postoperative ΔFHC and both ΔSVA ( r=-0.374, P=0.008) and proximal junctional angle changes (ΔPJA, r=-0.429, P=0.006). Additionally, increases in VAS leg pain scores correlated negatively with immediate postoperative FHC ( r=-0.314, P=0.025) and ΔFHC ( r=-0.298, P=0.031). Logistic regression indicated that immediate postoperative ΔFHC was a protective factor against PJK [ OR=0.722, 95% CI (0.541, 0.963), P=0.009), with a ROC-determined optimal ΔFHC cut-off of 3.90% (AUC=0.723, Youden index=0.847). Conclusions:Postoperative evaluation of femoral head coverage is clinically important for ASD patients undergoing S2AI screw fixation. A pre-to-post ΔFHC below 3.90% may indicate reduced hip compensation capacity, increasing risks for hip pain, sagittal imbalance progression, and PJK postoperatively.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Objective:To analyze the clinical features and risk factors for renal injury in children with antineutrophil cytoplasmic antibody (ANCA)-positive IgA vasculitis (IgAV).Methods:A case-control study was conducted. Seventy-two ANCA-positive IgAV children hospitalized at the Children′s Hospital of Chongqing Medical University from January 2017 to October 2022 were enrolled as the ANCA-positive group. Propensity score matching (1∶4) using the nearest neighbor was performed with age and gender as covariate, and 288 cases ANCA-negative IgAV children were included as the ANCA-negative group. Patients with renal injury were named ANCA-positive IgAV nephritis (IgAVN) group and ANCA-negative IgAVN group, respectively. The ANCA-positive IgAVN group was further divided into myeloperoxidase (MPO) group and proteinase 3 (PR3) group based on the type of ANCA. Clinical data including manifestations, laboratory tests, renal injury, and prognosis were collected. Comparisons between groups were performed using independent sample t-tests, Mann-Whitney U tests, χ2 tests, or Fisher′s exact tests. Kaplan-Meier curves were used to assess differences in the time to renal injury onset, and multivariate logistic regression was performed to identify independent risk factors for renal injury. Results:Among the 72 ANCA-positive IgAV children (41 males, 31 females, age of 7.7 (5.3, 11.2) years), no significant difference in age or gender was observed compared to the ANCA-negative group (both P>0.05). The ANCA-positive group had higher IgM levels, a higher incidence of recurrent rash, and shorter thrombin time (all P<0.05). Among children with renal injury, the ANCA-positive group showed significant differences in the incidence of hematuria, clinical classification, and grade A prognosis compared to the ANCA-negative group (all P<0.05), but no difference was found in the time to renal involvement onest or renal pathology (all P>0.05). The MPO group had higher rates of microscopic hematuria, gross hematuria, acute renal insufficiency, glomerular sclerosis, and grade B prognosis compared to the ANCA-negative IgAVN group (all P<0.05), with a later onset of renal involvement ( P<0.05). Elevated serum creatinine ( OR=1.08, 95% CI 1.03-1.14) and shortened thrombin time ( OR=0.71, 95% CI 0.55-0.92) were independent risk factors for renal injury in ANCA-positive IgAV children (all P<0.05). Conclusions:Children with ANCA-positive IgAV are more likely to experience recurrent rash. MPO-ANCA-positive IgAVN children have higher risks of hematuria, acute kidney injury and glomerular sclerosis, with later-onset but poorer renal prognosis compared to ANCA-negative IgAVN children. Higher serum creatinine levels and shorter thrombin time may be associated with renal injury in children with ANCA-positive IgAV.

Objective:This study investigated the expression of C-X-C motif chemokine receptor 3 (CXCR3) on CD45RO? T cells in the peripheral blood of patients with Alzheimer′s disease (AD) and its association with clinical features.Methods:A total of 41 AD patients and 30 age-and sex-matched healthy controls (HCs) were recruited from the Department of Neurology at the Medical Division of PLA General Hospital between September 2022 and March 2024. Flow cytometry was used to quantify CXCR3 expression on CD45RO? T cell subsets in peripheral blood. Dementia severity in AD patients was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Spearman correlation analysis examined the relationship between CD45RO?CXCR3? T cell levels and cognitive function in the AD group. Receiver operating characteristic (ROC) curve analysis determined the predictive utility of CD45RO?CXCR3? T cells for AD, quantified by the area under the curve (AUC).Results:Compared to healthy controls, AD patients exhibited significantly elevated levels of CD8?CD45RO?CXCR3? T cells [17.8% (7.2%, 40.3%) vs. 8.2% (5.1%, 12.3%), Z=-2.59, P<0.05]. However, no significant differences were observed for CD4?CD45RO?CXCR3? T cells, CD4?CD45RO?CXCR3? T cells, or CD8?CD45RO?CXCR3? T cells ( P>0.05). Spearman correlation analysis revealed a negative correlation between CD8?CD45RO?CXCR3? T cell levels and cognitive scores (MMSE: r=-0.72, P<0.05; MoCA: r=-0.70, P<0.05). ROC analysis demonstrated an AUC of 0.81 for CD8?CD45RO?CXCR3? T cells in predicting AD, with a sensitivity of 59.0% and specificity of 93.3%. Conclusions:CXCR3 expression is significantly upregulated on CD8?CD45RO? T cells in AD patients, and its levels correlate with cognitive impairment severity. These findings suggest that CD8?CD45RO?CXCR3? T cells may serve as a potential biomarker for AD diagnosis and progression monitoring.