ArticlePloS one2020
Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Diagnostic Risk Prediction Models for Upper Gastrointestinal Cancers: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Laparoscopic versus robotic surgery in the setting of combined resection of colorectal cancer and liver metastases: an experience from the I Go MILS registry.Updates in surgery · 2026Article
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- CHI3L1 is a diagnostic biomarker involved in immune infiltration of gastric cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations.Cancer research and treatment · 2025Article
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Article
- GastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer.Biomedicines · 2024Article
- A state-level analysis of macro-level factors associated with hospital readmissions.The European journal of health economics : HEPAC : health economics in prevention and care · 2024Article
- Article
- Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors.BMC gastroenterology · 2023Article
- MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems.PloS one · 2023Article
- A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning.Micromachines · 2022Article
- Radiomics Combined with Multiple Machine Learning Algorithms in Differentiating Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor: More Hands Produce a Stronger Flame.Journal of clinical medicine · 2022Article
- Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia.BMC medical informatics and decision making · 2022Article
- Machine Learning on Early Diagnosis of Depression.Psychiatry investigation · 2022Article
- Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls.BMC cancer · 2022Article
- Based on multiple machine learning to identify the ENO2 as diagnosis biomarkers of glaucoma.BMC ophthalmology · 2022Article
- Development of web-based dynamic nomogram to predict survival in patients with gastric cancer: a population-based study.Scientific reports · 2022Article
- Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer.Clinical Medicine Insights. Oncology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.
aimsTo construct a predictive model for the diagnosis of gastric cancer with high accuracy based on noninvasive characteristics.
methodsA retrospective study of 709 patients at Zhejiang Provincial People's Hospital was conducted. Variables of age, gender, blood cell count, liver function, kidney function, blood lipids, tumor markers and pathological results were analyzed. We used gradient boosting decision tree (GBDT), a type of machine learning method, to construct a predictive model for the diagnosis of gastric cancer and evaluate the accuracy of the model.
resultsOf the 709 patients, 398 were diagnosed with gastric cancer; 311 were health people or diagnosed with benign gastric disease. Multivariate analysis showed that gender, age, neutrophil lymphocyte ratio, hemoglobin, albumin, carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125) and carbohydrate antigen 199 (CA199) were independent characteristics associated with gastric cancer. We constructed a predictive model using GBDT, and the area under the receiver operating characteristic curve (AUC) of the model was 91%. For the test dataset, sensitivity was 87.0% and specificity 84.1% at the optimal threshold value of 0.56. The overall accuracy was 83.0%. Positive and negative predictive values were 83.0% and 87.8%, respectively.
conclusionWe construct a predictive model to diagnose gastric cancer with high sensitivity and specificity. The model is noninvasive and may reduce the medical cost.
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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.