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Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required.
Materials And Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set.
Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%.
Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.
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http://dx.doi.org/10.5230/jgc.2021.21.e33 | DOI Listing |
Environ Pollut
September 2025
Department of Geriatrics, Tianjin Medical University General Hospital, Anshan Road No. 154, Tianjin, 300052, China; Key Laboratory of Post-Trauma Neuro-Repair and Regeneration in Central Nervous System, Tianjin Key Laboratory of Injuries, Variations and Regeneration of Nervous System, Tianjin Neurol
This study systematically investigated the association between triclosan (TCS) exposure and Alzheimer's disease (AD) risk via integrated bioinformatics approaches. TCS-AD-related genes were identified using bioinformatics tools and public databases, followed by the screening of key genes through multi-model machine learning algorithms (LASSO, SVM-RFE, RF) to mitigate random errors in small sample sizes. DRD2 was confirmed as the most robust core gene by LASSO confidence interval analysis and SHAP evaluation, while APP and SLC6A3 were validated through cross-method intersection.
View Article and Find Full Text PDFIntroduction: Our study aimed to identify risk factors associated with the survival of gastric cancer patients with Type 2 diabetes mellitus (T2DM) and create a risk-scoring system for predicting their survival probabilities.
Methods: We gathered data from 1,912 individuals with both gastric cancer and T2DM from the Hong Kong Hospital Authority Data Collaboration Laboratory (HADCL), spanning from 2000 to 2020. We used conventional Cox proportional hazards regression and tree-based machine learning algorithms to construct models for prognosis risk prediction.
Front Med (Lausanne)
August 2025
Department of Internal Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Introduction: The coronavirus disease 2019 (COVID-19) pandemic resulted in significant global mortality and morbidity, with emerging mutant strains continuing to potentially precipitate severe respiratory illness. Two clinical assessment tools, namely, the COVID-19 Risk of Complications Score (CRS), based on 13 comorbidities, and the ALKA (age, lactate dehydrogenase, kidney function, and albumin) score have been developed to predict disease severity among patients who are symptomatic at presentation. This study aimed to compare the performance of these two risk-scoring systems in predicting hospital admission, critical illness, and mortality.
View Article and Find Full Text PDFCatheter Cardiovasc Interv
September 2025
Department of Cardiovascular Medicine, Kyushu University Hospital, Fukuoka, Japan.
Background: Cardiac computed tomography (CT) is a well-established process used to diagnose coronary artery disease; however, its specific advantages in predicting the use of atherectomy devices during percutaneous coronary intervention (PCI) for moderate to severe calcified lesions remain to be determined. This study aimed to develop a risk scoring system for predicting the use of atherectomy devices in PCI on the basis of morphological findings obtained by preoperative cardiac CT.
Methods: In this retrospective, multicenter, observational study, we screened patients who underwent cardiac CT 6 months before PCI for the target lesion.
J Clin Med
August 2025
Section of Cardiology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA.
Heart failure readmissions remain a major challenge for healthcare systems, contributing significantly to morbidity, mortality, and increased healthcare costs. Despite advancements in medical and device-based therapies, rehospitalization rates remain high, particularly within the first 30 days of discharge. This review aims to evaluate the primary factors associated with HF readmissions and discuss evidence-based strategies to reduce these rates.
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