ArticleScientific reports2021
Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 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
- Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- A Nomogram Based on Nutrition-Related Indicators and Computed Tomography Imaging Features for Predicting Preoperative Lymph Node Metastasis in Curatively Resected Esophagogastric Junction Adenocarcinoma.Annals of surgical oncology · 2023Trial
- Development of a machine learning-based model for predicting postoperative survival in gastric cancer.World journal of gastrointestinal surgery · 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 machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study.International journal of colorectal disease · 2025Article
- Whole-genome resequencing revealed genetic structure and specific breed identification loci of Ningxia Jingyuan chicken breed (Gallus gallus).BMC genomics · 2025Article
- Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.Scientific reports · 2025Article
- Predicting Resistance and Survival of HCC Patients Post-HAIC: Based on Shapley Additive exPlanations and Machine Learning.Journal of hepatocellular carcinoma · 2025Article
- PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.Physical and engineering sciences in medicine · 2024Article
- Low Absolute Lymphocyte Count Correlates with Lymph Node Metastases and Worse Survival of Patients with Gastric Cancer.Annals of surgical oncology · 2024Article
- Analysis of lymph node metastasis and survival prognosis in early gastric cancer patients: A retrospective study.World journal of gastrointestinal surgery · 2024Article
- Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.Scientific reports · 2024Article
- Article
- Machine learning-based classifiers to predict metastasis in colorectal cancer patients.Frontiers in artificial intelligence · 2024Article
- Risk factors of positive lymph node metastasis after radical gastrectomy for gastric cancer and construction of prediction models.American journal of cancer research · 2024Article
- Interpretable deep learning model to predict lymph node metastasis in early gastric cancer using whole slide images.American journal of cancer research · 2024Article
- A discrimination model by machine learning to avoid gastrectomy for early gastric cancer.Annals of gastroenterological surgery · 2023Article
- Application of Machine Learning Based on Structured Medical Data in Gastroenterology.Biomimetics (Basel, Switzerland) · 2023Review
- Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods.Cancer research and treatment · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test group at a ratio of 7:3. The model for lymph node metastasis was established with python machine learning. The Gbdt algorithm in the machine learning results finds that number of resected nodes, lymphovascular invasion and tumor size are the primary 3 factors that account for the weight of LNM. Effect of the LNM model of PDC gastric cancer patients in the training group: Among the 7 algorithm models, the highest accuracy rate was that of GBDT (0.955); The AUC values for the 7 algorithms were, from high to low, XGB (0.881), RF (0.802), GBDT (0.798), LR (0.778), XGB + LR (0.739), RF + LR (0.691) and GBDT + LR (0.626). Results of the LNM model of PDC gastric cancer patients in test group : Among the 7 algorithmic models, XGB had the highest accuracy rate (0.952); Among the 7 algorithms, the AUC values, from high to low, were GBDT (0.788), RF (0.765), XGB (0.762), LR (0.750), RF + LR (0.678), GBDT + LR (0.650) and XGB + LR (0.619). Single machine learning algorithm can predict LNM in poorly differentiated-type intramucosal gastric cancer, but fusion algorithm can not improve the effect of machine learning in predicting LNM.
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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.