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Radiomics in Gastric Cancer: Advancing Precision Medicine.

Jintao He^1,2, Siwei Pan^3,4,5, Mengxuan Cao^3,4,5

¹Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.

Journal of Gastric Cancer

|July 7, 2026

Summary

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This summary is machine-generated.

Radiomics, extracting quantitative features from medical images, offers a reliable diagnostic method for gastric cancer (GC). This review explores its clinical applications, advances, and future directions for improved cancer management.

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Gastric cancer (GC) is a significant global health threat with challenging diagnosis.
Current medical imaging interpretation for GC relies heavily on subjective expertise.
There is a critical need for objective and reproducible diagnostic tools in GC management.

Purpose of the Study:

To review the clinical applications of radiomics in gastric cancer management.
To examine advances in imaging multi-omics for enhanced predictive accuracy and interpretability.
To discuss limitations and future directions for radiomics in GC research.

Main Methods:

Radiomics involves standardized extraction of high-throughput quantitative features from medical images.

Keywords:

Gastric cancer Multi-omics Precision medicine Radiomics

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Machine learning and deep learning methods are employed for feature analysis.

The review synthesizes current literature on radiomics applications in GC.

Main Results:

Radiomics shows promise in reducing subjective variability in GC diagnosis and treatment.
Integration with multi-omics data enhances predictive models for GC.
Key limitations to clinical translation of radiomics models are identified.

Conclusions:

Radiomics offers a powerful, objective approach to gastric cancer management.
Further research and overcoming limitations are crucial for widespread clinical adoption.
Advancements in radiomics and multi-omics will significantly impact future GC care.