scholarly journals Development and Validation of Nomograms to Predict Operative Link for Gastritis Assessment Any-Stage and Stages III–IV in the Chinese High-Risk Gastric Cancer Population

2021 ◽  
Vol 8 ◽  
Author(s):  
Song Wang ◽  
Fei Ye ◽  
Yuan Sheng ◽  
Wenyong Yu ◽  
Yingling Liu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Training Sample ◽  
Gastric Atrophy ◽  
Multivariate Logistic Regression Model ◽  
Validation Sample ◽  
Cross Sectional ◽  
Training Cohort

Purpose: It is very essential to diagnose gastric atrophy in the area with high prevalence of gastric cancer. Operative link for gastritis assessment (OLGA) was developed to detect the severity of gastric atrophy. The aim of this study was to develop and validate nomograms for predicting OLGA any-stage and stages III–IV in the Chinese high-risk gastric cancer population.Methods: We retrospectively analyzed 7,945 participants obtained by a multicenter cross-sectional study. We randomly selected 55% individuals (4,370 participants, training cohort) to analyze and generate the prediction models and validated the models on the remaining individuals (3,575 participants, validation cohort). A multivariate logistic regression model was used to select variables in the training cohort. The corresponding nomograms were developed to predict OLGA any-stage and stages III–IV, respectively. The area under the receiver operating characteristic curves and the GiViTI calibration belts were used to estimate the discrimination and calibration of the prediction models.Results: There were 1,226 (28.05%) participants in the training sample and 970 (27.13%) in the validation sample who were diagnosed with gastric atrophy. The nomogram predicting OLGA any-stage had an area under the curve (AUC) of 0.610 for the training sample and 0.615 for the validation sample, with favorable calibrations in the overall population. Similarly, the nomogram predicting OLGA stages III–IV had an AUC of 0.702 and 0.714 for the training and validation samples, respectively, with favorable calibrations in the overall population.Conclusions: The prediction model can early identify the occurrence of gastric atrophy and the severity stage of gastric atrophy to some extent.

Preoperative Circulating MiR-210, a Risk Factor for Postoperative Delirium Among Elderly Patients with Gastric Cancer Undergoing Curative Resection

2020 ◽  
Vol 26 (40) ◽  
pp. 5213-5219
Author(s):  
Yun Chen ◽  
Jinwei Zheng ◽  
Junping Chen
Keyword(s):  
Gastric Cancer ◽  
Logistic Regression ◽  
Risk Factor ◽  
Elderly Patients ◽  
Curative Resection ◽  
Postoperative Delirium ◽  
Area Under The Curve ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Clinicopathologic Characteristics

Background: Postoperative delirium (POD) is a very common complication in elderly patients with gastric cancer (GC) and associated with poor prognosis. MicroRNAs (miRNAs) serve as key post-transcriptional regulators of gene expression via targeting mRNAs and play important roles in the nervous system. This study aimed to investigate the potential predictive role of miRNAs for POD. Methods: Elderly GC patients who were scheduled to undergo elective curative resection were consequently enrolled in this study. POD was assessed at 1 day before surgery and 1-7 days after surgery following the guidance of the 5th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM V, 2013). The demographics, clinicopathologic characteristics and preoperative circulating miRNAs by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were compared between patients with or without POD. Risk factors for POD were assessed via univariate and multivariate logistic regression analyses. Results: A total of 370 participants were enrolled, of which 63 had suffered from POD within postoperative 7 days with an incidence of 17.0%. Preoperative miR-210 was a predictor for POD with an area under the curve (AUC) of 0.921, a cut-off value of 1.67, a sensitivity of 95.11%, and a specificity of 92.06%, (P<0.001). In the multivariate logistic regression model, the relative expression of serum miR-210 was an independent risk factor for POD (OR: 3.37, 95%CI: 1.98–5.87, P=0.003). Conclusions: In conclusion, the present study highlighted that preoperative miR-210 could serve as a potential predictor for POD in elderly GC patients undergoing curative resection.

scholarly journals Prevalence of gastric cancer precursors in gastroscopy-screened adults by family history of gastric cancer and of cancers other than gastric

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rui Wu ◽  
Cheng Yang ◽  
Lin Ji ◽  
Zhi-Ning Fan ◽  
Yu-Wen Tao ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Family History ◽  
High Risk ◽  
Intraepithelial Neoplasia ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Younger Age ◽  
Significant Difference ◽  
History Of ◽  
Cancer Precursors

Abstract Background People are at a high risk of gastric cancer if their first-degree relatives suffered from atrophic gastritis (AG), intestinal metaplasia (IM), intraepithelial neoplasia (IEN), dysplasia (DYS), or gastric cancer (GC). This study was performed to analyse the association between FDR-GC and GC precursors. Methods A cross-sectional study was performed to screen the prevalence of GC precursors from November 2016 to September 2019. A total of 1329 participants with FDR-GC, 193 participants with a family history of non-gastric cancer in FDRs (FDR-nGC), and 860 participants without a family history of cancer in FDRs (FDR-nC) were recruited in this study. The logistic regression model was used in this study. Results The prevalence of normal, Non-AG, AG/IM, IEN/DYS, and GC was 31.91, 44.21, 13.81, 8.73, and 1.34%, respectively. The prevalence of IEN/DYS was higher in people with FDR-GC and FDR-nGC (FDR-GC: odds ratio (OR) = 1.655; 95%CI, 1.153–2.376; FDR-nGC: OR = 1.984; 95%CI, 1.122–3.506) than those with FDR-nC. The younger the age at which FDRs were diagnosed with GC, the more likely the participants were to develop AG/IM (Ptrend = 0.019). The risk of precursors to GC was higher in participants whose FDR-GC was the mother than in those whose FDR-GC was the father or sibling (OR, non-AG: 1.312 vs. 1.007, 1.274; AG/IM: 1.430 vs. 1.296, 1.378; IEN/DYS: 1.988 vs. 1.573, 1.542). There was no statistically significant difference in non-AG (OR = 1.700; 95%CI, 0.940–3.074), AG/IM (OR = 1.291; 95%CI, 0.579–2.877), and IEN/DYS (OR = 1.265; 95%CI, 0.517–3.096) between participants with one or more FDR-GC. Conclusion People with FDR-GC and FDR-nGC are at a high risk of IEN/DYS. When an FDR was diagnosed at a younger age, the risk of AG/IM was higher. The risk of GC precursors was higher in people whose FDR-GC was the mother.

scholarly journals Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

2020 ◽  
Vol 71 (16) ◽  
pp. 2079-2088 ◽  
Author(s):  
Kun Wang ◽  
Peiyuan Zuo ◽  
Yuwei Liu ◽  
Meng Zhang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Laboratory Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Extreme Gradient Boosting ◽  
Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

scholarly journals Low Serum Levels of Pepsinogen and Gastrin 17 Are Predictive of Extensive Gastric Atrophy with High-Risk of Early Gastric Cancer

2011 ◽  
Vol 223 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Ryousuke Kikuchi ◽  
Yasuhiko Abe ◽  
Katsunori Iijima ◽  
Tomoyuki Koike ◽  
Nobuyuki Ara ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Early Gastric Cancer ◽  
Serum Levels ◽  
Gastric Atrophy ◽  
Low Serum

Mo1167 Magnifying Image-Enhanced Endoscopy for Diagnosis of Gastric Atrophy and H. Pylori Infection in a Amerindian Population With a High-Risk of Gastric Cancer

2017 ◽  
Vol 85 (5) ◽  
pp. AB446
Author(s):  
Pablo Cortés ◽  
Antonio Rollán ◽  
Robinson G. Gonzalez ◽  
María E. Bufadel ◽  
Raúl Araya ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Gastric Atrophy ◽  
Amerindian Population ◽  
H Pylori

scholarly journals Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
M2 Macrophages ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Training Cohort

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300 ; GSE15459, n = 191 ; and GSE26901, n = 109 ). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort ( n = 600 ) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432 ; GSE84437, n = 431 ; and TCGA, n = 336 ). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort ( AUC > 0.7 ). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC ( p < 0.001 ). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group ( p < 0.001 ), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor ( p = 0.011 ). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score ( p = 0.00085 ). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

scholarly journals Identifying Potential miRNA Biomarkers for Gastric Cancer Diagnosis Using Machine Learning Variable Selection Approach

2022 ◽  
Vol 12 ◽  
Author(s):  
Neda Gilani ◽  
Reza Arabi Belaghi ◽  
Younes Aftabi ◽  
Elnaz Faramarzi ◽  
Tuba Edgünlü ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Variable Selection ◽  
Prediction Models ◽  
Strong Relationship ◽  
Training Sample ◽  
Machine Learning Algorithms ◽  
Molecular Events ◽  
Selection Approach ◽  
Ontological Analysis

Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease.Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships.Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events.Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.

Sign in / Sign up

Export Citation Format

Share Document