ArticleAmerican journal of cancer research2024
Risk factors of positive lymph node metastasis after radical gastrectomy for gastric cancer and construction of prediction models.
Article in American journal of cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Non-invasive prediction of occult peritoneal metastasis (OPM) in gastric cancer using logistic regression and random forest integrative models with CT radiomics and clinical parameters: machine learning prediction of gastric OPM.Journal of gastrointestinal oncology · 2026Article
- Prognostic value of pre-treatment serum CA19-9 and lymphocyte-to-monocyte ratio in HR+/HER2- breast cancer: a retrospective cohort study.BMC cancer · 2026Article
- Establishment and Validation of a Risk Prediction Model for Cephalic Dystocia in Parturients Under Epidural Labor Analgesia.International journal of women's health · 2026Article
- Interpretable machine learning analysis of clinicopathological and immunonutritional biomarkers for predicting lymph node metastasis in gastric cancer.Scientific reports · 2025Article
- Development and validation of a machine learning model for prediction of cephalic dystocia.BMC pregnancy and childbirth · 2025Article
- Identification and validation of calcium signaling pathway-related biomarkers in T1 and T2 lymph node metastatic gastric cancer.Frontiers in genetics · 2025Article
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5 authors.
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Abstract
Positive lymph node metastasis after radical gastrectomy for gastric cancer is a key factor affecting the prognosis of patients, and its mechanism is complex and multifactorial. The aim of this study is to identify the relevant risk factors for positive lymph node metastasis after radical gastrectomy for gastric cancer, and to construct corresponding predictive models. Through a retrospective analysis of clinical data of 316 gastric cancer patients who underwent radical surgery for gastric cancer, we found that age, maximum tumor diameter, degree of tumor differentiation, vascular invasion, depth of tumor infiltration, and CA199 were important factors affecting lymph node metastasis positivity in gastric cancer patients. Based on these factors, we constructed a Nomogram prediction model and found through internal validation that the model has good predictive performance. The area under the receiver operating characteristic curve (AUC) of the training and validation sets were 0.929 and 0.888, respectively. Clinical data of another 390 patients were collected for external verification. External validation results showed that the model had a predictive sensitivity of 75.76% (50/66), a specificity of 91.05% (295/324), and an accuracy of 88.46% (345/390). In addition, we also constructed a neural network prediction model and compared it with the Nomogram model. The results showed that the prediction performance of the Nomogram model was similar to that of the neural network model. The Nomogram model has been validated internally and externally, demonstrating high discrimination and accuracy, providing a convenient, intuitive, and personalized evaluation tool for clinicians, helping to optimize the postoperative management of gastric cancer patients and improve prognosis.
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