Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound

Figure 1 Flowchart of the study. EUS: Endoscopic ultrasound; AI: Artificial intelligence.

Figure 2 The MEUS-PCBL dataset. A: Benign lesion case from Beijing Tiantan Hospital; B: Benign lesion case from Beijing Friendship Hospital; C: Malignant tumor lesion case from Beijing Tiantan Hospital; D: Malignant tumor lesion case from Beijing Friendship Hospital.

Figure 3 High/Low-frequency energy ratio distributions of endoscopic ultrasound images derived from the two centers. Center A represents the training data from the Beijing Friendship Hospital, and Center B represents the external validation data from the Beijing Tiantan Hospital. The subscript 0 indicates the cancerous category (Center A_0, Center B_0), while the subscript 1 indicates the noncancerous category (Center A_1, Center B_1).

Figure 4 Architecture of the MCEUS-C2Net model. CSA: Channel self-attention; DiNA: Dilated neighborhood attention; E-MHSA: Efficient multi-head self-attention; GFE: Global feature extraction; KAN: Kolmogorov-Arnold network; LFE: Local feature extraction; MHCA: Multi-head convolutional attention; LFFN: Locally feed-forward network.

Figure 5 Confusion matrix diagrams produced for the four algorithms in the external validation experiment. A: ResNet-50; B: Swin transformer; C: MedViTV2; D: MCEUS-C2Net. T: Pancreatic cancer category; F: Noncancerous lesion category.

Figure 6 Performance curves of MCEUS-C2Net during training and external validation. A: Area under the curve variation curve during training; B: Loss curve during training; C: Receiver operating characteristic curve for the external validation dataset. AUC: Area under the curve.

Figure 7 Clinical utility evaluation of MCEUS-C2Net and baseline models on the external validation dataset. A: Calibration curves comparing the predicted probabilities of pancreatic cancer with the actual observed frequencies; B: Decision curve analysis evaluating the net clinical benefit across different threshold probabilities.

Figure 8 Visual comparison of attention heatmaps generated by different models. A: Original endoscopic ultrasound images; B: Attention heatmaps produced by ResNet-50, Swin transformer, MedViTV2, and MCEUS-C2Net; C: Fusion of original images and attention heatmaps.