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Cross-Cancer Transfer Learning for Gastric Cancer Risk Prediction from Electronic Health Records.

Daeyoung Hong¹, Jiung Kim¹, Jiyong Jung¹

¹School of Software Convergence, Myongji University, Seoul 03674, Republic of Korea.

Diagnostics (Basel, Switzerland)

|December 30, 2025

Summary

This summary is machine-generated.

Transfer learning on electronic health records (EHRs) can identify gastric cancer (GC) risk signals. This approach shows promise for early detection and referral, especially when GC data is limited.

Keywords:

electronic health records gastric cancer machine learning prediction model transfer learning

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Area of Science:

Oncology
Medical Informatics
Machine Learning

Background:

Early identification of gastric cancer (GC) risk is crucial for timely endoscopy and referral.
Leveraging shared signals across gastrointestinal/hepatopancreatobiliary cancers may improve GC risk prediction.
Electronic Health Records (EHRs) offer a rich data source for developing risk prediction models.

Purpose of the Study:

To develop and evaluate a cross-cancer transfer learning framework (TransferGC) for gastric cancer risk prediction using structured EHR data.
To compare the performance of TransferGC against traditional machine learning models.
To assess the sample efficiency of transfer learning in scenarios with limited GC data.

Main Methods:

Developed a transfer learning framework (TransferGC) pretraining on non-gastric GI/HPB cancers and adapting to GC using MIMIC-IV structured EHR data.
Included 508 GC cases in the target cohort and compared TransferGC against logistic regression, XGBoost, and multilayer perceptron baselines.
Evaluated models using AUROC, AP, F1, sensitivity, and specificity as primary and secondary endpoints.

Main Results:

TransferGC achieved an AUROC of 0.854 and AP of 0.600 in the full-label setting, outperforming logistic regression and XGBoost.
TransferGC demonstrated improved AUROC and F1 scores compared to a multilayer perceptron trained from scratch, particularly under reduced GC label conditions.
The framework maintained strong performance even with limited gastric cancer data, indicating sample efficiency.

Conclusions:

Cross-cancer transfer learning on structured EHR data provides a sample-efficient method for gastric cancer risk modeling, especially with limited data.
External validation on multi-center and outpatient cohorts is necessary to confirm generalizability before clinical deployment.
The proposed framework has the potential to be integrated into EHR systems for clinical decision support, flagging high-risk patients for timely endoscopic evaluation.