ArticleCancer research and treatment2023
Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods.
Article in Cancer research and treatment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence models in predicting lymph node metastasis in early gastric cancer: a systematic review and meta -analysis.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2026Pooled it
- Risk prediction models for lymph node metastasis in early gastric cancer patients: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- Interpretable gradient boosting machine model for predicting in-hospital mortality in sepsis-induced myocardial injury: a multicenter development, validation, and web-based clinical implementation.Frontiers in cardiovascular medicine · 2026Article
- Carotid vulnerable plaque in coronary heart disease: a machine learning-based diagnostic model integrating tongue parameters and blood metabolic biomarkers.Frontiers in cardiovascular medicine · 2026Article
- Interpretable machine learning analysis of clinicopathological and immunonutritional biomarkers for predicting lymph node metastasis in gastric cancer.Scientific reports · 2025Article
- Article
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Establishment of a machine learning model for predicting splenic hilar lymph node metastasis.NPJ digital medicine · 2025Article
- Combining biomarkers to construct a novel predictive model for predicting preoperative lymph node metastasis in early gastric cancer.Frontiers in oncology · 2025Article
- A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.Cancer medicine · 2024Article
- Article
- The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
purposeTo identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method. MATERIALS AND
methodsThe clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines.
resultsLNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively.
conclusionThe GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.
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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.