Clinical use of machine learning-based patho-mics signature for diagnosis and survival prediction of bladder cancer.

Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected a total of 643 hematoxylin-eosin strained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA).