Automated Detection and Grading of Non–Muscle-Invasive Urothelial Cell Carcinoma of the Bladder

Accurate grading of non–muscle-invasive urothelial cell carcinoma is of major importance; however, high interobserver variability exists. A fully automated detection and grading network based on deep learning is proposed to enhance reproducibility. A total of 328 transurethral resection specimens from 232 patients were included, and a consensus reading by three specialized pathologists was used. The slides were digitized, and the urothelium was annotated by expert observers. The U-Net–based segmentation network was trained to automatically detect urothelium. This detection was used as input for the classification network. The classification network aimed to grade the tumors according to the World Health Organization grading system adopted in 2004. The automated grading was compared with the consensus and individual grading. The segmentation network resulted in an accurate detection of urothelium. The automated grading shows moderate agreement (κ = 0.48 ± 0.14 SEM) with the consensus reading. The agreement among pathologists ranges between fair (κ = 0.35 ± 0.13 SEM and κ = 0.38 ± 0.11 SEM) and moderate (κ = 0.52 ± 0.13 SEM). The automated classification correctly graded 76% of the low-grade cancers and 71% of the high-grade cancers according to the consensus reading. These results indicate that deep learning can be used for the fully automated detection and grading of urothelial cell carcinoma.