Background Frailty serves as a significant precursor to falls, disability, and mortality. Epidemiological evidence examining the impact of working hours on frailty remains scarce. Objective To explore the prevalence of frailty and the relationship between frailty and working hours among full-time middle-aged and older workers in China. Methods Data were derived from the 2020 China Health and Retirement Longitudinal Study (CHARLS). The study included full-time workers aged 45 years and above with a weekly working duration exceeding 35 h. Frailty was assessed using the Frailty Index (FI). First, the dose-response relationship between working hours and FI was explored using a generalized additive model (GAM). Second, univariate analyses were performed using t-tests, χ² tests, and the Jonckheere–Terpstra trend test. Restricted cubic splines (RCS) were introduced for modeling. Based on ordinal logistic regression models, covariates were adjusted sequentially to examine whether differences in frailty prevalence existed across different weekly working hour categories. Finally, subgroup analyses were performed. Results Among the 5504 included participants, the mean age was (56.41±7.30) years, with 2304 being female (41.86%). A total of 807 participants (14.66%) were identified as frail, and 2 686 participants (48.80%) were pre-frail. After standardization for gender and age according to the seventh national census data, the standardized prevalence of frailty and prefrailty were 18.89% and 47.25%, respectively. Significant higher proportions of frail individuals were found among those in non-managerial (73.73%), agricultural (64.06%), and non-employed (65.68%) occupations. Long working hours were prevalent: 2596 (47.17%) workers worked 41–60 h per week, 1649 (29.96%) worked >60 h per week, and only 1259 (22.87%) worked 35–40 h per week. GAM analysis showed a non-linear relationship between weekly working hours and FI. Restricted cubic spline analysis showed a statistically significant association between weekly working hours and frailty status (P=0.022). Logistic regression analysis revealed that individuals working more than 60 h per week had a 23% higher odds of frailty compared to those working 41–60 h (OR=1.23, 95%CI: 1.09, 1.38, P<0.01), while those working 35–40 h per week showed a 15% higher odds of frailty (OR=1.15, 95%CI: 1.01, 1.31, P=0.034). The subgroup analysis revealed that the positive association between working more than 60 h per week and frailty remained generally consistent across sex, age, urban/rural residence, occupational attributes, industry, or employment type, although the association did not reach statistical significance among non-rural residents and individuals aged 60 years and older. Conclusion The prevalence of frailty among full-time middle-aged and older labor force in China is relatively high, and the odds of frailty increases significantly among those who work more than 60 h per week. It is recommended to implement frailty prevention and management strategies targeting long-working-hour workers and to prioritize the impact of working duration on frailty.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

Objective To explore the protective effect and possible mechanism of modified Shenqi Dihuang Decoction on podocytes in db/db mice based on ROS/NLRP3/GSDMD signaling pathway.Methods Fifty 8-week-old male db/db mice(SPF grade)were randomly divided into the model group,losartan group and TCM low-,medium-and high-dosage groups,with 10 mice in each group.Ten heterozygous db/m mice served as the blank group.Interventions were administered respectively for 12 weeks.The body mass,random blood glucose,serum creatinine,blood urea nitrogen and 24 h urinary protein content were detected,HE,PAS,PASM,Masson and Sirius red staining was used to observe the morphology of renal tissue,transmission electron microscopy was used to observe the ultrastructure of renal tissue,fluorescent probes were used to observe the release of ROS in renal tissue,immunofluorescence staining was used to detect the expression of Nephrin,NLRP3,Cleaved Caspase-1 and GSDMD-N in renal tissue,and Western blot was used to detect the expression of NLRP3,Cleaved Caspase-1,GSDMD-N,IL-1β,IL-18,Nephrin,Podocin,PODXL,WT-1 and Desmin proteins.Results Compared with the blank group,the body mass and random blood glucose of the model group mice significantly increased(P<0.05),and the contents of serum creatinine,blood urea nitrogen and 24 h urinary protein were significantly increased(P<0.05);glomerular hypertrophy,dilation of renal glomeruli and tubules,thickening of basement membrane,matrix proliferation in mesangial area,abnormal deposition of collagen fibers in renal interstitium,accompanied by damage to renal tubular epithelial structure and focal glomerulosclerosis,significant increase in type Ⅰ collagen deposition,extensive fusion of podocyte processes,and scattered electron dense material in the basement membrane or subepithelial layer;the ROS content in renal tissue significantly increased(P<0.05),and the protein expression of NLRP3,Cleaved Caspase-1,GSDMD-N,IL-1β,IL-18 and Desmin significantly increased(P<0.05),the protein expression of Nephrin,Podocin,PODXL and WT1 significantly decreased(P<0.05).Compared with the model group,the body mass and random blood glucose of mice in each dosage of TCM group were relatively stable,the contents of serum creatinine,blood urea nitrogen and 24 h urinary protein decreased;the pathological damage to renal tissue was reduced,the ultrastructure of podocytes was improved,and the density of podocytes increased;the ROS content decreased,and the protein expression of NLRP3,Cleaved Caspase-1,GSDMD-N,IL-1β,IL-18 and Desmin decreased,while the protein expression of Nephrin,Podocin,WT1 and PODXL increased.With the dosage of modified Shenqi Dihuang Decoction increased,the improvement effect gradually strengthened,and the differences in TCM high-dose group was statistically significant(P<0.05).Conclusion Modified Shenqi Dihuang Decoction can protects podocytes in db/db mice,potentially by modulating the ROS/NLRP3/GSDMD signaling pathway.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To explore the correlation between skeletal muscle mass and strength with metabolic syndrome in children.Methods:This cross-sectional study was conducted involving 383 children aged 10 to 15 years who visited the Department of Child Health Care, Children′s Hospital of Nanjing Medical University from June 2021 to December 2022. Their height, weight, waist circumference, body composition, grip strength and blood pressure were measured. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were calculated. The levels of fasting blood glucose, lipids and insulin were tested. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Children were divided into the metabolic syndrome group and the non-metabolic syndrome group. Independent t test or Mann-Whitney U test etc. was used to compare the difference between groups. Spearman correlation analysis and binary Logistic regression were used to investigate the correlation between skeletal muscle mass and strength and metabolic syndrome. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the accuracy of the index of skeletal muscle in predicting metabolic syndrome in children. Results:Among the 383 children, 282 (73.6%) were male, at the age of 11.4 (10.6, 12.5) years. There were 216 children (56.4%) diagnosed with obesity and 90 children (23.5%) diagnosed with metabolic syndrome. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index of the metabolic syndrome group were all lower than those in the non-metabolic syndrome group (all P<0.001). After adjusting for sex and age, relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were all negatively correlated with body mass index ( r=-0.84, -0.38, -0.63), waist circumference ( r=-0.76, -0.36, -0.70), systolic blood pressure ( r=-0.42, -0.21, -0.38), diastolic blood pressure ( r=-0.33, -0.18, -0.24), triglycerides ( r=-0.29, -0.13, -0.23), fasting insulin ( r=-0.28, -0.20, -0.29), and HOMA-IR ( r=-0.26, -0.18, -0.26) (all P<0.05), and positively correlated with high-density lipoprotein cholesterol ( r=0.38, 0.13, 0.31, all P<0.01). After adjusting for sex and age, high relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index all decreased the risks of metabolic syndrome ( OR=0.80, 0.55, 0.90), obesity ( OR=0.53, 0.64, 0.82), hypertension ( OR=0.86, 0.58, 0.92), low high-density lipoprotein cholesterol ( OR=0.83, 0.62, 0.92), hypertriglyceridemia ( OR=0.88, 0.78, 0.96). After adjusting for sex and age, high relative skeletal muscle mass and high grip strength-to-body weight index all decreased the risks of hyperglycemia ( OR=0.93 and 0.95, all P<0.05). ROC curve analysis showed that the relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index all had good predictive accuracy of metabolic syndrome in children (AUC=0.79, 0.71, 0.76), with optimal cutoff values of 40%, 1.2, and 35%, respectively. Conclusions:High relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index are all protective factors for metabolic syndrome in children. Regular measurement of skeletal muscle mass and grip strength can aid in the early identification and prevention of obesity and metabolic syndrome during childhood .

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

Objective To explore the protective effect and possible mechanism of modified Shenqi Dihuang Decoction on podocytes in db/db mice based on ROS/NLRP3/GSDMD signaling pathway.Methods Fifty 8-week-old male db/db mice(SPF grade)were randomly divided into the model group,losartan group and TCM low-,medium-and high-dosage groups,with 10 mice in each group.Ten heterozygous db/m mice served as the blank group.Interventions were administered respectively for 12 weeks.The body mass,random blood glucose,serum creatinine,blood urea nitrogen and 24 h urinary protein content were detected,HE,PAS,PASM,Masson and Sirius red staining was used to observe the morphology of renal tissue,transmission electron microscopy was used to observe the ultrastructure of renal tissue,fluorescent probes were used to observe the release of ROS in renal tissue,immunofluorescence staining was used to detect the expression of Nephrin,NLRP3,Cleaved Caspase-1 and GSDMD-N in renal tissue,and Western blot was used to detect the expression of NLRP3,Cleaved Caspase-1,GSDMD-N,IL-1β,IL-18,Nephrin,Podocin,PODXL,WT-1 and Desmin proteins.Results Compared with the blank group,the body mass and random blood glucose of the model group mice significantly increased(P<0.05),and the contents of serum creatinine,blood urea nitrogen and 24 h urinary protein were significantly increased(P<0.05);glomerular hypertrophy,dilation of renal glomeruli and tubules,thickening of basement membrane,matrix proliferation in mesangial area,abnormal deposition of collagen fibers in renal interstitium,accompanied by damage to renal tubular epithelial structure and focal glomerulosclerosis,significant increase in type Ⅰ collagen deposition,extensive fusion of podocyte processes,and scattered electron dense material in the basement membrane or subepithelial layer;the ROS content in renal tissue significantly increased(P<0.05),and the protein expression of NLRP3,Cleaved Caspase-1,GSDMD-N,IL-1β,IL-18 and Desmin significantly increased(P<0.05),the protein expression of Nephrin,Podocin,PODXL and WT1 significantly decreased(P<0.05).Compared with the model group,the body mass and random blood glucose of mice in each dosage of TCM group were relatively stable,the contents of serum creatinine,blood urea nitrogen and 24 h urinary protein decreased;the pathological damage to renal tissue was reduced,the ultrastructure of podocytes was improved,and the density of podocytes increased;the ROS content decreased,and the protein expression of NLRP3,Cleaved Caspase-1,GSDMD-N,IL-1β,IL-18 and Desmin decreased,while the protein expression of Nephrin,Podocin,WT1 and PODXL increased.With the dosage of modified Shenqi Dihuang Decoction increased,the improvement effect gradually strengthened,and the differences in TCM high-dose group was statistically significant(P<0.05).Conclusion Modified Shenqi Dihuang Decoction can protects podocytes in db/db mice,potentially by modulating the ROS/NLRP3/GSDMD signaling pathway.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To explore the correlation between skeletal muscle mass and strength with metabolic syndrome in children.Methods:This cross-sectional study was conducted involving 383 children aged 10 to 15 years who visited the Department of Child Health Care, Children′s Hospital of Nanjing Medical University from June 2021 to December 2022. Their height, weight, waist circumference, body composition, grip strength and blood pressure were measured. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were calculated. The levels of fasting blood glucose, lipids and insulin were tested. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Children were divided into the metabolic syndrome group and the non-metabolic syndrome group. Independent t test or Mann-Whitney U test etc. was used to compare the difference between groups. Spearman correlation analysis and binary Logistic regression were used to investigate the correlation between skeletal muscle mass and strength and metabolic syndrome. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the accuracy of the index of skeletal muscle in predicting metabolic syndrome in children. Results:Among the 383 children, 282 (73.6%) were male, at the age of 11.4 (10.6, 12.5) years. There were 216 children (56.4%) diagnosed with obesity and 90 children (23.5%) diagnosed with metabolic syndrome. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index of the metabolic syndrome group were all lower than those in the non-metabolic syndrome group (all P<0.001). After adjusting for sex and age, relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were all negatively correlated with body mass index ( r=-0.84, -0.38, -0.63), waist circumference ( r=-0.76, -0.36, -0.70), systolic blood pressure ( r=-0.42, -0.21, -0.38), diastolic blood pressure ( r=-0.33, -0.18, -0.24), triglycerides ( r=-0.29, -0.13, -0.23), fasting insulin ( r=-0.28, -0.20, -0.29), and HOMA-IR ( r=-0.26, -0.18, -0.26) (all P<0.05), and positively correlated with high-density lipoprotein cholesterol ( r=0.38, 0.13, 0.31, all P<0.01). After adjusting for sex and age, high relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index all decreased the risks of metabolic syndrome ( OR=0.80, 0.55, 0.90), obesity ( OR=0.53, 0.64, 0.82), hypertension ( OR=0.86, 0.58, 0.92), low high-density lipoprotein cholesterol ( OR=0.83, 0.62, 0.92), hypertriglyceridemia ( OR=0.88, 0.78, 0.96). After adjusting for sex and age, high relative skeletal muscle mass and high grip strength-to-body weight index all decreased the risks of hyperglycemia ( OR=0.93 and 0.95, all P<0.05). ROC curve analysis showed that the relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index all had good predictive accuracy of metabolic syndrome in children (AUC=0.79, 0.71, 0.76), with optimal cutoff values of 40%, 1.2, and 35%, respectively. Conclusions:High relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index are all protective factors for metabolic syndrome in children. Regular measurement of skeletal muscle mass and grip strength can aid in the early identification and prevention of obesity and metabolic syndrome during childhood .

Objective:To investigate the value of intratumoral and peritumoral radiomics models based on 18F-FDG PET-CT in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma and interpret peritumoral radiomics features. Methods:This study was a cross-sectional study. Patients with lung adenocarcinoma who underwent 18F-FDG PET-CT at the Third Affiliated Hospital of Soochow University between January 2018 and April 2022 were retrospectively collected and samplied into a training set (309 cases) and a test set (206 cases) in a 6∶4 ratio randomly. Radiomics features were extracted from the intratumoral and peritumoral regions of interest based on PET and CT images, respectively, and the optimal feature sets were selected. Radiomics models were established using the XGBoost algorithm, and radiomics scores (intratumoral CT label, peritumoral CT label, intratumoral PET label, peritumoral PET label) were calculated. Logistic regression analysis was used to construct a clinical model and a combined model (incorporating PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features). The predictive performance of the models was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Unsupervised clustering, Spearman correlation analysis, and visualization methods were used for the interpretability of peritumoral radiomics features. Results:In both the training and test sets, the AUC value of CT peritumoral labels was greater than that of CT intratumoral labels for predicting EGFR mutation status in lung adenocarcinoma (training set: Z=3.84, P<0.001; test set: Z=1.99, P=0.046). In the test set, the AUC value of PET intratumoral labels (0.684) was slightly higher than that of PET peritumoral labels (0.672) for predicting EGFR mutation status, but the difference was not statistically significant ( P>0.05). The combined model had the highest AUC value for predicting EGFR mutation status of lung adenocarcinoma in both the training and test sets and was significantly better than the clinical model (training set: Z=6.52, P<0.001; test set: Z=2.31, P=0.021). Interpretability analysis revealed that CT peritumoral radiomics features were correlated with CT shape features, and there were significant differences in CT peritumoral features between different EGFR mutation statuses. Conclusions:The value of CT peritumoral labels is superior to that of CT intratumoral labels in predicting EGFR mutation status in lung adenocarcinoma. The predictive performance of the model can be improved by combining PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features.