Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer

To develop and validate artificial intelligence models based on contrast-enhanced CT(CECT) images of venous phase using deep learning (DL) and Radiomics approaches to predict lymphovascular invasion in gastric cancer prior to surgery. We retrospectively analyzed data from 351 gastric cancer patients, randomly splitting them into two cohorts (training cohort, n = 246; testing cohort, n = 105) in a 7:3 ratio. The tumor region of interest (ROI) was outlined on venous phase CT images as the input for the development of radiomics, 2D and 3D DL models (DL2D and DL3D). Of note, by centering the analysis on the tumor's maximum cross-section and incorporating seven adjacent 2D images, we generated stable 2.5D data to establish a multi-instance learning (MIL) model. Meanwhile, the clinical and feature-combined models which integrated traditional CT enhancement parameters (Ratio), radiomics, and MIL features were also constructed. Models' performance was evaluated by the area under the curve (AUC), confusion matrices, and detailed metrics, such as sensitivity and specificity. A nomogram based on the combined model was established and applied to clinical practice. The calibration curve was used to evaluate the consistency between the predicted LVI of each model and the actual LVI of gastric cancer, and the decision curve analysis (DCA) was used to evaluate the net benefit of each model. Among the developed models, 2.5D MIL and combined models exhibited the superior performance in comparison to the clinical model, the radiomics model, the DL2D model, and the DL3D model as evidenced by the AUC values of 0.820, 0.822, 0.748, 0.725, 0.786, and 0.711 on testing set, respectively. Additionally, the 2.5D MIL and combined models also showed good calibration for LVI prediction, and could provide a net clinical benefit when the threshold probability ranged from 0.31 to 0.98, and from 0.28 to 0.84, indicating their clinical usefulness. The MIL and combined models highlight their performance in predicting preoperative lymphovascular invasion in gastric cancer, offering valuable insights for clinicians in selecting appropriate treatment options for gastric cancer patients.