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 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 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 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 examine the association of hypertension onset age with diabetes, so as to provide insights into reducing the the risk of cardiovascular events. Methods: Permanent residents aged 35 to 75 years were selected through the program of early screening and comprehensive intervention for the high-risk cardiovascular disease population in Changning District and Baoshan District, Shanghai Municipality from 2016 to 2020. Demographic information, disease history, hypertension onset age, blood pressure and fasting blood glucose were collected through questionnaire surveys, physical examination and laboratory tests. The residents were divided into four groups based on the onset age of hypertension: <45, 45-<55, 55-<65 and ≥65 years old, and the residents with normal blood pressure were selected as control. The association of hypertension onset age with prediabetes and diabetes were identified using a multivariable logistic regression model. Results: A total of 25 228 residents were recruited, including 8 753 males (34.70%) and 16 475 females (65.30%). The prevalence of hypertension was 43.80%. There were 1 779, 3 274, 3 781 and 2 217 cases with hypertension onset age of <45, 45-<55, 55-<65 and ≥65 years old, respectively, and 14 177 residents with normal blood pressure. The prevalence of prediabetes and diabetes were 24.01% and 11.29%, respectively. Multivariable logistic regression analysis showed that after adjusting for confounding factors such as gender, marital status and educational level, compared with the normal blood pressure group, the risk of prediabetes was higher in the hypertension onset age groups of <45 (OR=1.345, 95%CI: 1.164-1.553), 45-<55 (OR=1.365, 95%CI: 1.212-1.536) and 55-<65 years old (OR=1.376, 95%CI: 1.239-1.527), and the risk of diabetes was higher in the hypertension onset age groups of <45 (OR=2.302, 95%CI: 1.906-2.775), 45-<55 (OR=2.349, 95%CI: 2.016-2.734), 55-<65 (OR=1.909, 95%CI: 1.667-2.184) and ≥65 years old (OR=1.315, 95%CI: 1.131-1.526). Conclusion There are statistically significant associations between hypertension onset age with prediabetes and diabetes.

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.

Objective: To investigate the associations between the single nucleotide polymorphism(SNP) in vestigial like family member 4(VGLL4) gene and Helicobacter pylori(H. pylori) infection. Methods: The blood samples of 450 normal physical examiners were collected , and the samples were divided into H. pylori negative group( n =220) and H. pylori positive group(n = 230) using enzyme⁃linked immunosorbent assay(ELISA) . SNP rs1803489 ,rs7617620 , and rs13078528 in VGLL4 gene were genotyped using polymerase chain reaction ( PCR) Ⅳrestriction fragment length polymorphism ( RFLP) technology. Results: SNP rs1803489 , rs7617620 , and rs13078528 in VGLL4 gene were not associated with H. pylori infection in the Han population in Baotou , Inner Mongolia. Conclusion SNP rs1803489 , rs7617620 , and rs13078528 in VGLL4 gene may not play a major role in H. pylori infection in Baotou Han population.

Objective: To investigate the associations of the single nucleotide polymorphism(SNP) rs1803489, rs7617620 and rs13078528 in vestigial like family member 4(VGLL4) gene with the risk of non-cardia gastric cancer. Methods: The case-control study design was used. 450 cases with non-cardia gastric cancer confirmed by pathology and 450 normal controls were collected in the Han population in Baotou; SNP rs1803489, rs7617620, and rs13078528 inVGLL4were genotyped using polymerase chain reaction(PCR)-restriction fragment length polymorphism(RFLP) technology. Results: VGLL4gene SNP rs1803489 was associated with the risk of non-cardia gastric cancer in the Han population in Baotou. Compared with the carriers of GG genotype, the carriers of AG genotype had a higher risk of non-cardia gastric cancer(OR=1.511, 95%CI=1.095-2.085,P<0.05). SNP rs7617620 and rs13078528 were not associated with non-cardia gastric cancer(P>0.05). Conclusion SNP rs1803489 inVGLL4may have an effect on the risk of non-cardia gastric cancer in the Han population in Baotou. SNP rs7617620 and rs13078528 may not play a major role in the risk of non-cardia gastric cancer.

Objective: To explore the efficacy of valvuloplasty on mitral valve regurgitation (MR) in infants and small children, in order to provide evidence for clinical treatment. Methods: Retrospective analysis was conducted in patients aged less than 3 years old who have undergone mitral repair surgeries at Department of Cardiovascular Surgery, General Hospital of Western Theater Command from January 2015 to December 2017.Cardiopulmonary bypass (CPB) surgery was performed under general anesthesia.The corresponding repair operations were selected for various MR types.All the children were examined by cardiac ultrasound before discharge.Patients were followed up after surgery and mitral regurgitation was recorded. Results: A total of 69 patients were enrolled, including 30 boys and 39 girls, with an average age of (19.3±11.6) months and an average weight of (9.6±2.1) kg.There were 32 cases of Carpentier type Ⅰ, 30 cases of type Ⅱ and 7 cases of type Ⅲ.Twenty-nine patients were diagnosed as simple MR, while the other 40 patients were combined with other cardiac malformations.One patient died at postoperative day 7.One patient still had severe MR after surgery, and then second operation was performed on the 10^th day after first surgery.Sixty-five patients were followed up from 1 month to 3 years[(15.9±10.9) months]. During the follow-up period, no patient died or had reoperation.Aggravated MR was observed in 4 patients. Conclusions Mitral valvuloplasty is a safe and effective surgical method for treating infants and younger children with MR.The key to the success of surgery is to choose the appropriate individualized repair operation.At the same time, the growth potential of mitral valve should be fully considered to avoid restrained development.

Objective To investigate the clinical benefits and the impacts on distribution and antibiotic resistance of pathogenic bacterium associated with fluoroquinolone prophylaxis during neutropenia in patients with acute leukemia.MethodsA total of 62 infection episodes occurred in patients with acute leukemia were retrospectively analyzed from October 2013 to October 2015.The patients admitted in ward A (group A,n=31) received Cephalosporin drugs as contrast, and received quinolones in ward B (group B,n=31).ResultsTo observe the implementation of quinolones in treatment of patients, the bacteria decreased significantly, the Klebsiella pneumoniae was the most common, but compared with the control group, there are still differences (P<0.05, and the observation group of patients with oral ulcer infection rate was 9.68%, upper respiratory tract infection rate was 6.45%, the infection rate of lung 6.45%, the digestive tract infection rate was 6.45%, the infection rate was 3.23%, perianal skin infection rate was 3.23%, the infection rate was 3.23%, which is significantly lower than the control group (P<0.05, and the observation group of patients with severe neutropenia, duration of neutropenia lack degree is significantly better than the control group (P<0.05).ConclusionThe fluoroquinolone prophylaxis induces diminishing proportion of normal flora and increasing frequency of Escherichia coli in severely neutropenic patients with acute leukemia,may not influence the distribution of other bacteria.The susceptility of main pathogens may not be affected by antibiotic prophylaxis.The fluoroquinolone don't decrease the incidence of septicemia and infection in gastrointestinal tract.Our data suggest that more prudent use of antibiotic prophylaxis may be reasonable even in patients at high-risk for developing infection.