Clinical trial · Observational
Explainable Machine Learning for Predicting Early Gastric Cancer
Explainable Machine Learning for Predicting Early Gastric Cancer: a Retrospective Cohort Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Abstract Background: Early detection of gastric cancer is crucial for improving patient survival rates. Currently, the primary method for diagnosing early-stage gastric cancer is endoscopy, which has various limitations. Additionally, single laboratory tests continue to fall short of the requirements for early screening. This study aims to develop a machine learning (ML) model using clinical data to predict early-stage gastric cancer and apply SHapley Additive exPlanation (SHAP) values to explain the ML model. Methods: This study involved patients who provided gastric tissue samples at Wenzhou Central Hospital from 2019 to 2023. The investigators gathered various laboratory test results from these patients. The investigators constructed and evaluated nine ML models to predict early-stage gastric cancer, using the area under the curve (AUC), accuracy, and sensitivity to assess their performance. For the most effective prediction model, The investigators utilized the SHAP method to determine the features' importance and explain the ML model.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Early Gastric Cancer | Early Gastric Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Explainable machine learning for predicting early gastric cancer
- timeFrame
- From June 2025 to July 2025
- description
- The area under the ROC curve (AUC) was used as the primary outcome measure
Secondary outcomes (1)
- measure
- Explainable machine learning for predicting early gastric cancer
- timeFrame
- From June 2025 to July 2025
- description
- We considered the sensitivity of the model as a secondary outcome measure.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * all patients with a gastric tissue pathology result are included Exclusion Criteria: * unclear or incomplete pathology results * significant missing laboratory data * progressive and advanced gastric cancer
References
Publications (0)
Data not yet available