ArticleCancer control : journal of the Moffitt Cancer Center
Predicting Peritoneal Metastasis of Gastric Cancer Patients Based on Machine Learning.
Article in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.Briefings in functional genomics · 2025Pooled it
- Development and validation of a machine learning model for predicting liver metastasis in pancreatic cancer: a retrospective cohort study.Gland surgery · 2026Article
- Efficacy and safety of sequential hyperthermic intraperitoneal chemotherapy following surgery for pathological T staging 4 gastric cancer.World journal of gastroenterology · 2026Article
- Article
- Asian consensus on normothermic intraperitoneal and systemic treatment for gastric cancer with peritoneal metastasis.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025Article
- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- Combining radiomics and deep learning to predict liver metastasis of gastric cancer on CT image.Frontiers in oncology · 2025Article
- Dual-energy CT quantitative parameters to evaluate occult peritoneal metastasis in advanced gastric cancer preoperatively.Abdominal radiology (New York) · 2024Article
- Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.BMC cancer · 2024Article
- Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis.Heliyon · 2024Review
- Machine learning methods predict recurrence of pN3b gastric cancer after radical resection.Translational cancer research · 2024Article
- Article
- Integrating Machine Learning and the SHapley Additive exPlanations (SHAP) Framework to Predict Lymph Node Metastasis in Gastric Cancer Patients Based on Inflammation Indices and Peripheral Lymphocyte Subpopulations.Journal of inflammation research · 2024Article
- Application of Machine Learning Based on Structured Medical Data in Gastroenterology.Biomimetics (Basel, Switzerland) · 2023Review
- Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis.BMC gastroenterology · 2023Article
- Predicting metastasis in gastric cancer patients: machine learning-based approaches.Scientific reports · 2023Article
- Article
- The Prediction of Peritoneal Carcinomatosis in Patients with Colorectal Cancer Using Machine Learning.Healthcare (Basel, Switzerland) · 2022Article
- A Machine Learning-Modified Novel Nomogram to Predict Perioperative Blood Transfusion of Total Gastrectomy for Gastric Cancer.Frontiers in oncology · 2022Article
- Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer.Cancer management and research · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim is to explore the prediction effect of 5 machine learning algorithms on peritoneal metastasis of gastric cancer.
methods1080 patients with postoperative gastric cancer were divided into a training group and test group according to the ratio of 7:3. The model of peritoneal metastasis was established by using 5 machine learning (gbm(Light Gradient Boosting Machine), GradientBoosting, forest, Logistic and DecisionTree). Python pair was used to analyze the machine learning algorithm. Gbm algorithm is used to show the weight proportion of each variable to the result.
resultCorrelation analysis showed that tumor size and depth of invasion were positively correlated with the recurrence of patients after gastric cancer surgery. The results of the gbm algorithm showed that the top 5 important factors were albumin, platelet count, depth of infiltration, preoperative hemoglobin and weight, respectively. In training group: Among the 5 algorithm models, the accuracy of GradientBoosting and gbm was the highest (0.909); the AUC values of the 5 algorithms are gbm (0.938), GradientBoosting (0.861), forest (0.796), Logistic(0.741) and DecisionTree(0.712) from high to low. In the test group: among the 5 algorithm models, the accuracy of forest, DecisionTree and gbm was the highest (0.907); AUC values ranged from high to low to gbm (0.745), GradientBoosting (0.725), forest (0.696), Logistic (0.680) and DecisionTree (0.657).
conclusionMachine learning can predict the peritoneal metastasis in patients with gastric cancer.
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Registered trials
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.