A CT-based 2.5D deep learning model for preoperative T-staging in gastric cancer: a retrospective multicenter study

To develop and validate a 2.5D multi-angle deep learning (MADL) model for preoperative T-staging in patients with gastric cancer (GC) and to explore the predictive potential for survival outcome.

In this retrospective study, GC datasets from four centers were used to develop a quaternary for preoperative T-staging model by integrating CT images with nine 2D slicers and three angles (0°, + 45°, -45°) from transverse, sagittal, and coronal views. Diagnostic performance (accuracy, sensitivity, specificity, F1-score) were compared between the 2.5D MADL and radiologists. The prognostic potential of 2.5D MADL-derived features was evaluated using Kaplan-Meier analysis and multivariate Cox regression.

A total of 433 patients were divided into the training (n = 346, mean age, 64.8 years ± 10.5 [SD]; 95 female) and the internal validation (n = 87, mean age, 63.0 years ± 11.9; 23 female) sets from Centers 1-3, and 41 patients (mean age, 64.6 years ± 8.9; 10 female) formed the external testing set from Center 4. The 2.5D MADL model exhibited excellent diagnostic performance for T-staging in external testing set (AUCs of T1-T4: 0.962, 0.722, 0.913, 0.962). For T1 staging, it outperformed radiologists in accuracy (95% vs. 83%), sensitivity (75% vs. 12%), and F1 score (86% vs. 22%) in external testing set. Survival analysis showed significant overall survival differences between high- and low-risk groups stratified (P < 0.001), with high-risk patients having shorter survival (HR = 1.632, 95% CI:1.239-2.150).

The 2.5D MADL model achieves accurate and reliable preoperative T staging of gastric cancer, with performance comparable to radiologists and superior accuracy for early-stage disease.