ArticleBMC cancer2024
Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gastric cancer survival prediction using artificial intelligence models based on electronic health records: a systematic review and meta-analysis.Frontiers in digital health · 2026Pooled it
- Radiomic prediction of early progression at 2 years post-treatment in patients with resectable rectal cancer based on rectal tumor and mesentery characteristics: a two-center study.Journal of gastrointestinal oncology · 2026Article
- Minimally invasive surgery for remnant gastric cancer: a single-center retrospective study of treatment outcomes in Japan.Journal of minimally invasive surgery · 2026Article
- Predicting Postoperative Survival in Patients With Malignant Biliary Obstruction Using an Interpretable Machine Learning Model: A Multicenter Study.Cancer medicine · 2026Article
- Explaining Risk Stratification in Differentiated Thyroid Cancer Using SHAP and Machine Learning Approaches.Biomedicines · 2025Article
- Interpretable machine learning analysis of clinicopathological and immunonutritional biomarkers for predicting lymph node metastasis in gastric cancer.Scientific reports · 2025Article
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17 authors.
Funding
Abstract
objectiveThe purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC).
methodsClinicopathologic data of 286 patients with RGC undergoing operation (radical resection and palliative resection) from a multi-institution database were enrolled and analyzed retrospectively. These individuals were split into training (80%) and test cohort (20%) by using random allocation. Nine commonly used ML methods were employed to construct survival prediction models. Algorithm performance was estimated by analyzing accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), confusion matrices, five-fold cross-validation, decision curve analysis (DCA), and calibration curve. The best model was selected through appropriate verification and validation and was suitably explained by the SHapley Additive exPlanations (SHAP) approach.
resultsCompared with the traditional methods, the RGC survival prediction models employing ML exhibited good performance. Except for the decision tree model, all other models performed well, with a mean ROC AUC above 0.7. The DCA findings suggest that the developed models have the potential to enhance clinical decision-making processes, thereby improving patient outcomes. The calibration curve reveals that all models except the decision tree model displayed commendable predictive performance. Through CatBoost-based modeling and SHAP analysis, the five-year survival probability is significantly influenced by several factors: the lymph node ratio (LNR), T stage, tumor size, resection margins, perineural invasion, and distant metastasis.
conclusionsThis study established predictive models for survival probability at five years in RGC patients based on ML algorithms which showed high accuracy and applicative value.
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