Objective: To investigate the association between the promoter region-938 polymorphism of B-cell lymphoma/leukemia-2 (Bcl-2) gene and the esophageal cancer (EC) and gastric cardia adenocarcinoma (GCA) in Hebei Province. Methods: From 2007 to 2010, 145 esophageal cancer patients and 169 cardiaccancer patientsfrom the outpatient department of the Fourth Hospital of Hebei Medical Universitywereselected in a case group, and 195 non-tumor patients were selected in a control group during the same period. A questionnaire survey was used to collect information of research subjects. Pathological tissues were collected to extract genomic DNA and detect the genotype of bcl-2 gene -938. A multivariate logistic regression model was used to analyze the association between the bcl-2 gene locus 938 CC genotype and the EC and GCA. The interaction between age, gender, smoking, drinking, upper gastrointestinal family history and the bcl-2 gene locus 938 CC genotype was analyzed by likelihood ratio test. Results: The age of the esophageal and cardiac cancer groups was (56.3±8.3) and (57.1±8.4) years old, and that of the control group was (54.7±7.1) years old. The proportion of the bcl-2 gene locus 938 CC genotype in the esophageal group [48.3% (70/145)] and the cardiac cancer group [48.5% (82/169)] was higher than that in the control group [33.8% (66/195)] (both P values<0.05).Compared with the AA genotype, the risk of esophageal cancer and cardiac cancerin people with the CC genotype was 2.386 (1.20-4.76) and 2.564 (1.27-5.18) respectively. In the population with CC genotype, compared with the positive family history, drinking, and male, the negative family history, non-drinking, and female had a higher risk of esophageal cancer; compared with the non-smoking, negative family history, non-drinking and male, the smoking, positive family history, drinking, and female had a higher risk of cardiac cancer (all the P interaction values were <0.05). Conclusion People with bcl-2 gene locus 938 CC genotype in Hebei Provincewere more likely to suffer from the esophageal and gastric cardia adenocarcinoma.

Objective:Biological behavior, pathological characteristics and prognostic factors of 355 cases with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) were analyzed in this retrospective study.Methods:In our study, 355 patients pathologically diagnosed as GEP-NENs were identified from April 2006 to November 2017 in the Fourth Hospital of Hebei Medical University. The biological behavior, pathological characteristics and prognosis were analyzed retrospectively.Results:There were 355 patients (228 males and 127 females) with a mean age of 58.3±10.7 years. GEP-NENs were detected most frequently in the stomach (48.2%), followed by the pancreas (16.1%), colorectum (14.1%), esophagus (7.6%), duodenum/jejunum(5.6%), liver (4.2%), appendix (2.3%) and gallbladder/bile duct (2.0%). The main clinical manifestations of non-functional GEP-NENs were abdominal pain (88/350, 25.14%), ventosity (77/350, 22.00%) and dysphagia (68/350, 19.43%), which were generally lacking specificity at the first diagnosis. 295 patients were treated surgically, including 45 cases of endoscopic resection and 250 cases of laparoscopic operation. Concerning to pathological grading, there were 22.5% (80/355) patients in grade 1 (G1), 12.7% (45/355) in grade 2 (G2), and 58.9% (209/355) in grade 3 (G3). The median follow-up time was 34 months. Furthermore, the 1-, 3- and 5-year overall survival calculated by Kaplan-Meier method were 80.1%, 59.8%, and 57.5%, respectively. Univariate analysis revealed that tumor site, treatment, operation type, depth of tumor invasion, TNM staging, pathological grading, vascular embolus, lymph node metastasis, tumor size, preoperative leukomonocyte level and preoperative plasma albumin were associated with overall survival (all P<0.05). Multivariate analysis showed that treatment, operation type, depth of tumor invasion, TNM staging, pathological grading, vascular embolus, lymph node metastasis and tumor size were independent prognostic factors for GEP-NENs (all P<0.05). Conclusions:The clinicopathological characteristics of GEP-NENs should be mastered by clinicians, and the standard treatment measures were also needed to be formulated based on the prognostic factors in order to improve the prognosis of patients.

Objective:Biological behavior, pathological characteristics and prognostic factors of 355 cases with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) were analyzed in this retrospective study.Methods:In our study, 355 patients pathologically diagnosed as GEP-NENs were identified from April 2006 to November 2017 in the Fourth Hospital of Hebei Medical University. The biological behavior, pathological characteristics and prognosis were analyzed retrospectively.Results:There were 355 patients (228 males and 127 females) with a mean age of 58.3±10.7 years. GEP-NENs were detected most frequently in the stomach (48.2%), followed by the pancreas (16.1%), colorectum (14.1%), esophagus (7.6%), duodenum/jejunum(5.6%), liver (4.2%), appendix (2.3%) and gallbladder/bile duct (2.0%). The main clinical manifestations of non-functional GEP-NENs were abdominal pain (88/350, 25.14%), ventosity (77/350, 22.00%) and dysphagia (68/350, 19.43%), which were generally lacking specificity at the first diagnosis. 295 patients were treated surgically, including 45 cases of endoscopic resection and 250 cases of laparoscopic operation. Concerning to pathological grading, there were 22.5% (80/355) patients in grade 1 (G1), 12.7% (45/355) in grade 2 (G2), and 58.9% (209/355) in grade 3 (G3). The median follow-up time was 34 months. Furthermore, the 1-, 3- and 5-year overall survival calculated by Kaplan-Meier method were 80.1%, 59.8%, and 57.5%, respectively. Univariate analysis revealed that tumor site, treatment, operation type, depth of tumor invasion, TNM staging, pathological grading, vascular embolus, lymph node metastasis, tumor size, preoperative leukomonocyte level and preoperative plasma albumin were associated with overall survival (all P<0.05). Multivariate analysis showed that treatment, operation type, depth of tumor invasion, TNM staging, pathological grading, vascular embolus, lymph node metastasis and tumor size were independent prognostic factors for GEP-NENs (all P<0.05). Conclusions:The clinicopathological characteristics of GEP-NENs should be mastered by clinicians, and the standard treatment measures were also needed to be formulated based on the prognostic factors in order to improve the prognosis of patients.

Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers.The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.