ArticleBMC medical informatics and decision making2023
Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- A Composite Histopathologic Invasion Score Predicts Lymph Node Metastasis in Gastric Cancer.Journal of clinical medicine · 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
- A nomogram based on systemic inflammatory markers: development and validation for predicting postoperative overall survival in gastric cancer.Translational cancer research · 2026Article
- Investigating the utilization of LIME-enhanced AI and generated data for evaluating the risks associated with radial artery puncture procedures.Scientific reports · 2026Article
- Machine learning-based models for intraoperative blood loss of retroperitoneal laparoscopic adrenalectomy.Surgical endoscopy · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Enhancing clinical training evaluation: leveraging artificial intelligence algorithms for effective online practicum assessment.BMC medical education · 2026Article
- Machine learning approaches to early detection of delayed wound healing following gastric cancer surgery.World journal of gastrointestinal oncology · 2026Article
- Development and clinical application of a postoperative complication prognosis prediction model for gastric cancer patients based on automated machine learning with body fat rate.Frontiers in oncology · 2026Article
- Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.BMC cancer · 2025Article
- Machine Learning Insight: Unveiling Overlooked Risk Factors for Postoperative Complications in Gastric Cancer.Cancers · 2025Article
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
- Revealing Potential Therapeutic Targets in Gastric Cancer through Inflammation and Protein-Protein Interaction Hub Networks.Journal of Cancer · 2025Article
- Construction and validation of a perioperative blood transfusion model for patients undergoing total hip arthroplasty with osteonecrosis of the femoral head based on machine learning.Frontiers in medicine · 2025Article
- Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.BMC cancer · 2024Article
- Stability and consistency of symptom clusters in younger versus older patients receiving chemotherapy.BMC geriatrics · 2024Article
- Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study.World journal of gastrointestinal surgery · 2024Article
- Development and Validation of an Inflammation-Combined Prognostic Index (ICPI)-Based Nomogram for Predicting Overall Survival in Gastric Cancer.Journal of inflammation research · 2024Article
- 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
- Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study.Frontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThere is a strong association between gastric cancer and inflammatory factors. Many studies have shown that machine learning can predict cancer patients' prognosis. However, there has been no study on predicting gastric cancer death based on machine learning using related inflammatory factor variables.
methodsSix machine learning algorithms are applied to predict total gastric cancer death after surgery.
resultsThe Gradient Boosting Machine (GBM) algorithm factors accounting for the prognosis weight outcome show that the three most important factors are neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR) and age. The total postoperative death model showed that among patients with gastric cancer from the predictive test group: The highest accuracy was LR (0.759), followed by the GBM algorithm (0.733). For the six algorithms, the AUC values, from high to low, were LR, GBM, GBDT, forest, Tr and Xgbc. Among the six algorithms, Logistic had the highest precision (precision = 0.736), followed by the GBM algorithm (precision = 0.660). Among the six algorithms, GBM had the highest recall rate (recall = 0.667).
conclusionPostoperative mortality from gastric cancer can be predicted based on machine learning.
Indexed as
Identifiers
What OpenQuestion holds
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.