ArticleAnnals of surgical oncology2021
Construction and Validation of a Risk-Scoring Model that Preoperatively Predicts Lymph Node Metastasis in Early Gastric Cancer Patients.
Article in Annals of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Risk prediction models for lymph node metastasis in early gastric cancer patients: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- CT Scans Understage Lymph Nodes in Gastric and Gastroesophageal Adenocarcinoma.Journal of surgical oncology · 2025Article
- Artificial neural network model enhancing the accuracy of clinical evaluation for high-risk population of lymph node metastasis in non-intestinal type early gastric cancer: a multicenter real-world study in China.International journal of surgery (London, England) · 2025Article
- Machine learning models based on log odds of positive lymph nodes for predicting survival in TFrontiers in oncology · 2025Article
- Preoperative predictive model for the probability of lymph node metastasis in gastric cancer: a retrospective study.Frontiers in oncology · 2024Article
- Using nomogram, decision tree, and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study.American journal of cancer research · 2023Article
- Characteristics of lymph node stations/basins metastasis and construction and validation of a preoperative combination prediction model that accurately excludes lymph node metastasis in early gastric cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2022Article
- Article
- Construction of a nomogram for preoperative prediction of the risk of lymph node metastasis in early gastric cancer.Frontiers in surgery · 2022Article
- Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.Frontiers in medicine · 2022Article
- A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer.Frontiers in oncology · 2022Article
- Article
- Risk Factors of Lymph Node Metastasis and Its Prognostic Significance in Early Gastric Cancer: A Multicenter Study.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThe aim of this study was to create a risk-scoring model to preoperatively predict the incidence of lymph node metastasis (LNM) in early gastric cancer (EGC) patients to guide treatment.
methodsTo construct the risk-scoring model, we retrospectively analyzed a primary cohort of 548 EGC patients. Univariate analysis and logistic regression were performed. A risk-scoring model for predicting LNM in EGC patients was developed based on preoperative factors, and another cohort of 73 patients was then analyzed to validate the model.
resultsIn the primary cohort, LNM was pathologically confirmed in 72 (13.1%) patients. In the multivariate analysis, the presence of ulceration and tumor size on gastroscopy, undifferentiated histological type, and presence of enlarged lymph nodes on computed tomography or endoscopic ultrasonography were independent risk factors for LNM. A 17-point risk-scoring model was developed to predict LNM risk. The cut-off score of the model was 8, and the area under the receiver operating characteristic curve (AUC) of the model was 0.835 [95% confidence interval (CI) 0.784-0.886]. In the validation cohort, the AUC of the model was 0.829 (95% CI 0.699-0.959).
conclusionsWe developed and validated an effective 17-point risk-scoring model that could preoperatively predict LNM for EGC patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.