# Preoperative prediction of tumor deposits in advanced gastric cancer using intratumoral and peritumoral CT radiomics: development and validation of an ensemble model

## TL;DR

This study shows that combining tumor and surrounding tissue CT data with clinical factors can accurately predict tumor deposits in advanced gastric cancer before surgery.

## Contribution

The novel ensemble model integrates intratumoral, peritumoral radiomics, and clinical factors for improved preoperative prediction of tumor deposits.

## Key findings

- The combined radiomics model outperformed standalone intratumoral and peritumoral models in predicting tumor deposits.
- The ensemble model achieved the highest AUCs across training, validation, and test cohorts.
- SHAP and nomogram analyses helped explain the predictive mechanisms of the models.

## Abstract

To investigate the potential of intratumoral and peritumoral radiomics derived from CT to preoperatively predict tumor deposits (TDs) in patients with advanced gastric cancer (AGC).

In this retrospective investigation, a total of 374 patients from two medical centers were recruited and divided into training (n = 186), validation (n = 80), and test (n = 108) cohorts. Intratumoral and peritumoral radiomics models were developed utilizing radiomics features derived from the corresponding 3D regions of interest (ROIs). A combined radiomics model integrating intratumoral and peritumoral features was further constructed through feature-level concatenation. Additionally, an ensemble model was established via the integration of this combined radiomics model with selected independent clinical prognostic factors. All models were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Finally, the Shapley Additive Explanations (SHAP) method and nomogram were employed to elucidate the predictive mechanisms of the three radiomics models (intratumoral, peritumoral, and combined) and the ensemble model.

The combined intratumoral-peritumoral radiomics model showed higher AUC than the standalone intratumoral and peritumoral models across all cohorts (training: 0.874 vs. 0.751 vs. 0.830; validation: 0.846 vs. 0.720 vs. 0.713; test: 0.842 vs. 0.701 vs. 0.675). Moreover, the ensemble model yielded the highest AUCs across all cohorts (0.925, 0.865, 0.878 for training, validation, and test cohorts, respectively).

Both intratumoral and peritumoral radiomics offer meaningful information regarding TDs, while the CT-based ensemble model holds the capacity to preoperatively predict TDs in AGC patients.