Radiomics in Gastric Cancer: Advancing Precision Medicine

Gastric cancer (GC) is a common malignancy characterized by insidious onset and aggressive invasiveness that poses a serious threat to human health. Although medical imaging plays a critical role in cancer diagnosis and treatment, its interpretation largely relies on the expertise and experience of observers, underscoring the need for more reliable diagnostic techniques. Radiomics, through a series of standardized procedures, enables the extraction of high-throughput quantitative features from medical images across various imaging modalities using machine learning or deep learning methods, thereby reducing the influence of subjective and objective variability. This review summarizes the clinical applications of radiomics in the management of GC. To enhance predictive accuracy and model interpretability, we also examine advances in imaging multi-omics research. Furthermore, we discuss key limitations that may hinder the clinical translation of radiomics models and propose future directions to advance radiomics research in GC.