Fig. 2
Working diagram of the segmentation model. Diagram of the development and validation of an artificial intelligence prognostic model for pathology. Two pathologists labelled all the HE images based on IHC images. In the training stage, a CNN model (Unet++) was trained using the Zhongda Hospital Affiliated to Southeast University (purple 1) training set, and the AR segmentation model (AR-Net) was trained. In the validation stage, the internal validation set (purple 2) of Zhongda Hospital affiliated to Southeast University and the external validation set (green) of the First People’s Hospital of Huai’an City were input into the convolutional neural network to obtain the average pixel accuracy of each slide, and the output of the image patch blocks was stitched together to obtain a heatmap, which intuitively depicted the AR high expression prediction interval. In the predicted prognostic stage, the predicted AR regions were extracted from the topological and nuclear texture features using Hover-Net, and the predicted ROC curves were obtained by combining clinical data. CNN convolutional neural network, WSI whole slide image, H&E haematoxylin-eosin, IHC Immunohistochemistry, AR Androgen Receptor.
