European journal of radiology · 2025
End-to-end deep learning model with multi-channel and attention mechanisms for multi-class diagnosis in CT-T staging of advanced gastric cancer.
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Liu Bowen, Jiang Pengcheng, Wang Zehui, Wang Xiaoxiao, Wang Zhixuan, Peng Chen, Liu Zhanpeng, Lu Chao, Pan Donggang, Shan Xiuhong
Аннотация
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.
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.