End-to-end deep learning model with multi-channel and attention mechanisms for multi-class diagnosis in CT-T staging of advanced gastric cancer

Bowen Liu, Pengcheng Jiang, Zehui Wang, Xiaoxiao Wang, Zhixuan Wang, Chen Peng, Zhanpeng Liu, Chao Lu, Donggang Pan, Xiuhong Shan,
End-to-end deep learning model with multi-channel and attention mechanisms for multi-class diagnosis in CT-T staging of advanced gastric cancer,
European Journal of Radiology,
Volume 192,
2025,
112408,
ISSN 0720-048X,
https://doi.org/10.1016/j.ejrad.2025.112408.
(https://www.sciencedirect.com/science/article/pii/S0720048X25004942)
Abstract: Background
Homogeneous AI assessment is required for CT-T staging of gastric cancer.
Purpose
To construct an End-to-End CT-based Deep Learning (DL) model for tumor T-staging in advanced gastric cancer.
Materials and Methods
A retrospective study was conducted on 460 cases of presurgical CT patients with advanced gastric cancer between 2011 and 2024. A Three-dimensional (3D)-Convolution (Conv)-UNet based automatic segmentation model was employed to segment tumors, and a SmallFocusNet-based ternary classification model was built for CT-T staging. Finally, these models were integrated to create an end-to-end DL model. The segmentation model’s performance was assessed using the Dice similarity coefficient (DSC), Intersection over Union (IoU) and 95 % Hausdorff Distance (HD_95), while the classification model’s performance was measured with thearea under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, and F1-score.Eventually, the end-to-end DL model was compared with the radiologist using the McNemar test.
Results
The data were divided into Dataset 1(423 cases for training and test set, mean age, 65.0 years ± 9.46 [SD]) and Dataset 2(37 cases for independent validation set, mean age, 68.8 years ± 9.28 [SD]). For segmentation task, the model achieved a DSC of 0.860 ± 0.065, an IoU of 0.760 ± 0.096 in test set of Dataset 1, and a DSC of 0.870 ± 0.164, an IoU of 0.793 ± 0.168 in Dataset 2. For classification task,the model demonstrated a macro-average AUC of 0.882(95 % CI 0.812–0.926), an average sensitivity of 76.9 % (95 % CI 67.6 %–85.3 %) in test set of Dataset 1 and a macro-average AUC of 0.862(95 % CI 0.723–0.942), an average sensitivity of 76.3 % (95 % CI 59.8 %–90.0 %) in Dataset 2. Meanwhile, the DL model’s performance was better than that of radiologist (Accuracy was 91.9 %vs82.1 %, P = 0.007).
Conclusion
The end-to-end DL model for CT-T staging is highly accurate and consistent in pre-treatment staging of advanced gastric cancer.
Keywords: Deep learning; Advanced gastric cancer; X-ray computed tomography; T-stage