ArticleAnnals of surgical oncology2024
Machine Learning Radiomics Liver Function Model for Prognostic Prediction After Radical Resection of Advanced Gastric Cancer: A Retrospective Study.
Article in Annals of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Prognostic Value of albumin-bilirubin (ALBI) Grade in Patients with Gastric Cancer: A Systematic Review and meta-analysis.Journal of gastrointestinal cancer · 2026Pooled it
- Analysis of the Efficacy of Red Blood Cell Transfusion in Very Low Birth Weight Infants and Construction of a Prediction Model.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2026Article
- Review
- Modified albumin-bilirubin grade predicts overall and progression-free survival in metastatic gastric cancer.Biomolecules & biomedicine · 2026Article
- Article
- Preoperative albumin-bilirubin grade combined with sarcopenia predicts long-term outcomes after laparoscopic gastrectomy for advanced gastric cancer.BMC gastroenterology · 2025Article
- Nomogram for the Therapeutic Efficacy of Apheresis Platelet Transfusion in Hematologic Patients.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2025Article
- Development and internal validation of prediction model for rebleeding within one year after endoscopic treatment of cirrhotic varices: consideration from organ-based CT radiomics signature.BMC medical imaging · 2024Article
- Comprehensive Analysis of Angiogenesis and Ferroptosis Genes for Predicting the Survival Outcome and Immunotherapy Response of Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2024Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
purposeWe aimed to establish a machine learning radiomics liver function model to explore how liver function affects the prognosis of patients with gastric cancer (GC).
methodsPatients with advanced GC were retrospectively enrolled in this study. Eight machine learning radiomic models were constructed by extracting radiomic features from portal-vein-phase contrast-enhanced computed tomography (CE-CT) images. Clinicopathological features were determined using univariate and multifactorial Cox regression analyses. These features were used to construct a GC survival nomogram.
resultsA total of 510 patients with GC were split into training and test cohorts in an 8:2 ratio. Kaplan-Meier analysis showed that patients with type I liver function had a better prognosis. Fifteen significant features were retained to establish the machine learning model. LightBGM showed the best predictive performance in the training (area under the receiver operating characteristic curve [AUC] 0.978) and test cohorts (AUC 0.714). Multivariate analysis revealed that gender, age, liver function, Nutritional Risk Screening 2002 (NRS-2002) score, tumor-lymph node-metastasis stage, tumor size, and tumor differentiation were independent risk factors for GC prognosis. The survival nomogram based on machine learning radiomics, instead of liver biochemical indicators, still had high accuracy (C-index of 0.771 vs. 0.773).
conclusionThe machine learning radiomics liver function model has high diagnostic value in predicting the influence of liver function on prognosis in patients with GC.
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