Automated T1 bladder risk stratification based on depth of lamina propria invasion from H and E tissue biopsies: a deep learning approach

Abstract

Identification of bladder layers from tissue biopsies is the first step towards an accurate diagnosis and prognosis of bladder cancer. We present an automated Bladder Image Analysis System (BLIAS) that can recognize urothelium, lamina propria, and muscularis propria from images of H and E-stained slides of bladder biopsies. Furthermore, we present its clinical application to automate risk stratification of T1 bladder cancer patients based on the depth of lamina propria invasion. The method uses multidimensional scaling and transfer learning in conjunction with convolutional neural networks to identify different bladder layers from H and E images of bladder biopsies. The method was trained and tested on eighty whole slide images of bladder cancer biopsies. Our preliminary findings suggest that the proposed method has good agreement with the pathologist in identification of different bladder layers. Additionally, given a set of tumor nuclei within lamina propria, it has the potential to risk stratify T1 bladder cancer by computing the distance from this set to urothelium and muscularis propria. Our results suggest that a pretrained network trained via transfer learning is better in identifying bladder layers than a conventional deep learning paradigm.