Exploring the landscape, hot topics, and trends of bariatric metabolic surgery with machine learning and bibliometric analysis.

Background: This study aimed to analyze the landscape of publications on bariatric metabolic surgery through machine learning and help experts and scholars from various disciplines better understand bariatric metabolic surgery's hot topics and trends. Methods: In January 2021, publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Bariatric Surgery' from 1946 to 2020 were downloaded. Python was used to extract publication dates, abstracts, and research topics from the metadata of publications for bibliometric evaluation. Descriptive statistical analysis, social network analysis (SNA), and topic modeling with latent Dirichlet allocation (LDA) were used to reveal bariatric metabolic surgery publication growth trends, landscape, and research topics. Results: A total of 21,798 records of bariatric metabolic surgery-related literature data were collected from PubMed. The number of publications indexed to bariatric metabolic surgery had expanded rapidly. Obesity Surgery and Surgery for Obesity and Related Diseases are currently the most published journals in bariatric metabolic surgery. The bariatric metabolic surgery research mainly included five topics: bariatric surgery intervention, clinical case management, basic research, body contour, and surgical risk study. Conclusion: Despite a rapid increase in bariatric metabolic surgery-related publications, few studies were still on quality of life, psychological status, and long-term follow-up. In addition, basic research has gradually increased, but the mechanism of bariatric metabolic surgery remains to be further studied. It is predicted that the above research fields may become potential hot topics in the future.

Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study.

RATIONALE AND OBJECTIVES: Contrast-enhanced computed tomography (CE-CT) was used to establish radiomics nomogram to evaluate the malignant potential of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS: A total of 500 GIST patients were enrolled in this study and divided into training cohort (n = 346, our center) and validation cohort (n = 154, another center). Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select the feature subset with the best discriminant features from the three phases image, and five classifiers were used to establish four radiomics signatures. Preoperative radiomics nomogram was constructed by adding the clinical features determined by multivariate logistic regression analysis. The performance of radiomics signatures and nomogram were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). The calibration of nomogram was appraised by calibration curve. RESULTS: A total of 13 radiomic features were extracted from tri-phase combined CE-CT images. Tri-phase combined CE-CT features + Support Vector Machine (SVM) was the best combination at predicting the malignant potential of GIST, with an AUC of 0.895 (95% CI 0.858-0.931) in the training cohort and 0.847 (95% CI 0.778-0.917) in the validation cohort. The nomogram also had good calibration. In the training cohort and the validation cohort, preoperative radiomics nomogram reached AUCs of 0.927 and 0.905, respectively, which were higher than clinical. CONCLUSION: The radiomics nomogram had a good predictive effect and generalization on the malignant potential of GIST, which could effectively help guide preoperative clinical decision.

A novel immune-related genes signature after bariatric surgery is histologically associated with non-alcoholic fatty liver disease.

Increasing evidence shows that immune-related genes (IRGs) play an important role in bariatric surgery (BS). We identified differentially expressed immune-related genes (DEIRGs) of adipose tissue after BS by analysing the two expression profiles of GEO (GSE59034 and GSE29409). Subsequently, enrichment analysis, GSEA and PPI networks were examined to identify the hub IRGs and related pathways. The performance of the signature was evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). CIBERSORT algorithm was used to evaluate the relative abundance of infiltrated immune cells.42 DEIRGs were found between the GSE59034 and GSE29409 datasets. The AUC of the signature was 0.904 and 0.865 in the GSE58979 and GSE48452, respectively. Interestingly, the signature also showed good performance in diagnosing non-alcoholic fatty liver disease (NAFLD) (AUC was 0.834 and 0.800, respectively). The number of neutrophils, macrophages M2, macrophages M0 and dendritic cells activated decreased significantly. After BS, the infiltration of T cells regulatory, monocytes, mast cells resting and plasma cells in adipose tissue increased. The novel proposed IRGs signature reveals the underlying immune mechanism of BS and is a promising biomarker for distinguishing the severity of NAFLD. This will provide new insights into strategies for treating obesity and NAFLD.

Clinical Significance of Lymph Node Micrometastasis in pN0 Gastric Cancer Patients.

PURPOSE: To investigate the relationship between lymph node micrometastasis (LNMM) and clinicopathological factors and to evaluate the prognostic effects of LNMM in pN0 gastric cancer (GC) patients. METHODS: One hundred and seventy-two GC patients who received radical gastrectomy with D2 lymph node dissection were enrolled in the present study. 1371 negative lymph nodes from level 2 station confirmed by pathology were examined. The LNMM was diagnosed by telomeric repeat amplification protocol/enzyme-linked immunosorbent assay (TRAP-ELISA). The relationship between clinicopathological factors and LNMM was investigated by multivariate analysis. Survival analysis was performed to evaluate the effects of LNMM on prognosis. RESULTS: LNMM was detected in 423 lymph nodes from 72 patients. The results showed that invasion depth (OR = 3.755, P = 0.004), TNM staging (OR = 3.152, P = 0.002), lymphatic invasion (OR = 2.178, P = 0.009), and tumor differentiation (OR = 1.266, P = 0.013) were independent risk factors associated with LNMM. Survival analysis showed that patients with LNMM had significantly worse 5-year survival compared with those without LNMM (42% vs. 76.4%, P < 0.05). Multivariate analysis demonstrated that LNMM, tumor size, Lauren type, invasion depth, and lymphatic invasion (P < 0.05) were independently factors associated with 5-year survival. CONCLUSIONS: The findings showed that tumor invasion depth, TNM staging, lymphatic invasion, and tumor differentiation were independent risk factors associated with LNMM occurrence. Moreover, LNMM is a clinically negative prognostic factor in pN0 GC patients.