Radiomics for preoperative assessment of peritoneal metastasis in gastric cancer

Table 1 Integrated summary of radiomics-based approaches for preoperative assessment of peritoneal metastasis in gastric cancer

Dimension	Integrated findings from current evidence	Clinical implications
Imaging modality	Contrast-enhanced CT is the most widely used modality for radiomics analysis in gastric cancer PM. Positron emission tomography/CT and magnetic resonance imaging are explored as complementary modalities, while dual-energy and spectral CT provide additional quantitative information such as iodine distribution	CT-based radiomics is currently the most feasible approach for clinical translation, while multimodal imaging may further improve predictive accuracy in selected settings
Imaging phase strategy	Multiphase CT (arterial, venous, delayed) consistently outperforms single-phase imaging by capturing complementary information related to tumor perfusion, stromal infiltration, and contrast retention. Venous phase remains the most commonly used baseline phase	Multiphase acquisition enhances sensitivity for occult PM and should be preferred when image quality and workflow allow
Region-of-interest definition	Most models focus on the primary tumor, with increasing inclusion of peritumoral fat, omentum, or adjacent structures to reflect tumor-microenvironment interactions. Both 2D and three-dimensional segmentation strategies are used	Incorporating peritumoral regions may improve detection of early peritoneal spread; 2D segmentation offers a pragmatic balance between performance and feasibility
Radiomic feature categories	First-order intensity features, texture features (GLCM, GLRLM, GLSZM), and wavelet-transformed features are most frequently retained after feature selection. Shape features play a secondary role	Texture-based features appear particularly sensitive to subtle heterogeneity associated with early metastatic potential
Feature selection methods	Least absolute shrinkage and selection operator and maximum relevance and minimum redundancy are the dominant feature selection approaches to reduce dimensionality and improve model stability. Intraclass correlation filtering is commonly applied to ensure robustness	Standardized feature selection pipelines are critical to minimize overfitting and improve reproducibility across centers
Modeling strategies	Conventional machine learning models (logistic regression, random forest, support vector machine) are widely applied. Deep learning approaches enable automated feature learning and segmentation, often achieving higher sensitivity	Machine learning models provide interpretable risk estimates, while deep learning offers automation but requires careful validation and explainability
Integration with clinical variables	Combined radiomics-clinical models consistently outperform radiomics-only or clinical-only models. Common clinical variables include tumor stage, cancer antigen 125 (a marker of peritoneal tumor burden), and nodal status, which provide complementary biological and anatomical information to radiomic features	Multimodal integration aligns with precision oncology and improves individualized risk stratification
Predictive performance	Most integrated models achieve good-to-excellent discrimination for PM, with reported area under the curves frequently exceeding 0.80 and, in some cases, approaching 0.90	Radiomics demonstrates meaningful incremental value over conventional imaging, particularly for occult disease
Validation strategy	Internal validation is common; external and multicenter validation is less frequent but increasingly reported, particularly for deep learning models. Prospective validation remains scarce	Robust external validation is essential before routine clinical implementation
Clinical application scenarios	Preoperative risk stratification, selection for staging laparoscopy, avoidance of non-therapeutic surgery, and individualized treatment planning	Radiomics may function as a decision-support tool rather than a standalone diagnostic test
Key methodological limitations	Retrospective design, heterogeneous imaging protocols, variable segmentation strategies, lack of standardization, and limited interpretability of complex models	These limitations currently restrict widespread clinical adoption and regulatory approval
Future research priorities	Standardized imaging and radiomics pipelines, prospective multicenter studies, explainable artificial intelligence, and integration with molecular and pathological biomarkers	Addressing these gaps is essential for clinical translation and guideline incorporation

CT: Computed tomography; PM: Peritoneal metastasis; 2D: Two-dimensional.