Fig. 1

Workflow for DeepRisk model building and evaluation of DPS performance. A Underwent segmentation and patching process from WSIs, all the patches were encoded using a deep CNN model into descriptive feature representations. A pre-trained ResNet50 model was used to extract feature maps, to which average pooling was applied to obtain feature vectors. Attention-based MIL was used to aggregate all patch features of a single case and deliver an output label. Then, we built the DeepRisk network without annotation and further validated in 2 external cohorts (TCGA-STAD and SOBC). Histopathological features, immune contexture, transcriptomics and clinical information were used to investigate the correlations between model output (DPS) and underlying GC features. B C-indices based on DPS under different magnification scales, with and without incorporation of demographic factors (age and gender). C, D Performance of DPS in predicting patient survival in the Zhongshan dataset, using a series of DPS cut-off values for patient dichotomising. E, F Kaplan–Meier curves of OS and DFS for high-DPS (> 50%) and low-DPS (≤ 50%). G, H Kaplan–Meier curves for survival and recurrence of DPS at different TNM stages. ***p < 0.001. CNN, convolutional neural network; GC, gastric cancer; WSI, whole-slide image; DPS, digital pathology signature; MIL, multi-instance learning; TCGA, The Cancer Genome Atlas