Application value of multiphase contrast-enhanced computed tomography radiomics in preoperative evaluation of peritoneal metastasis in gastric cancer

Abstract

BACKGROUND

Peritoneal metastasis occurs in 10%-45% of gastric cancer patients and significantly impacts prognosis and treatment decisions. Traditional computed tomography (CT) imaging has limited sensitivity (60%-80%) for detecting early peritoneal metastases, while laparoscopic exploration is invasive. Multiphase contrast-enhanced CT radiomics offers a non-invasive approach to improve preoperative prediction, yet most existing studies rely on single-phase analysis without fully exploiting multiphase data advantages.

AIM

To construct a preoperative prediction model for gastric cancer peritoneal metastasis based on multiphase contrast-enhanced CT radiomics, compare the diagnostic efficacy between multiphase combined and single-phase analysis, and evaluate its clinical application value.

METHODS

A retrospective analysis was conducted on 200 pathologically confirmed gastric cancer patients from January 2020 to December 2024, all of whom underwent preoperative multiphase contrast-enhanced CT examination. Patients were randomly divided into training set (n = 140) and validation set (n = 60) at a 7:3 ratio. PyRadiomics was used to extract 3920 radiomics features from arterial phase, venous phase, and delayed phase images. Synthetic minority oversampling technique was applied to handle class imbalance. Feature selection was performed through Z-score standardization, univariate screening, collinearity testing, and least absolute shrinkage and selection operator regression. Single-phase and multiphase combined radiomics models were constructed using logistic regression, support vector machine, and random forest algorithms. Model performance was evaluated through receiver operating characteristic curves.

RESULTS

The multiphase combined model achieved an area under the curve (AUC) of 0.876 (95% confidence interval: 0.783-0.941) in the validation set, with sensitivity of 81.0%, specificity of 84.6%, and accuracy of 83.3%, significantly superior to all single-phase models (P < 0.05). Among single-phase models, the venous phase model performed best (AUC = 0.834). Hosmer-Lemeshow test showed good model calibration (P = 0.765). Decision curve analysis demonstrated that at a threshold probability of 0.35, the multiphase combined model could avoid 33.7% of unnecessary exploratory surgeries.

CONCLUSION

The multiphase combined model based on multiphase contrast-enhanced CT radiomics can effectively predict gastric cancer peritoneal metastasis, with diagnostic performance significantly superior to single-phase models, providing a new non-invasive technical approach for individualized preoperative assessment of gastric cancer patients.

Keywords: Gastric cancer; Peritoneal metastasis; Radiomics; Multiphase contrast-enhanced computed tomography; Machine learning; Preoperative prediction

Core Tip: Multiphase contrast-enhanced computed tomography radiomics enables non-invasive preoperative prediction of peritoneal metastasis in gastric cancer. In a 200-patient cohort, a combined arterial-venous-delayed model (logistic regression within a leakage-free pipeline) outperformed single-phase models, achieving area under the curve 0.876 with good calibration and decision-curve net benefit. The approach may help triage candidates for staging laparoscopy, reduce unnecessary exploratory procedures, and support individualized treatment planning. Reporting follows IBSI/TRIPOD-AI, with feature robustness, cross-validation, and external temporal testing recommended for future multicenter deployment.