A Deep Learning System to Predict the Histopathological Results From Urine Cytopathological Images

Background: Although deep learning systems (DLSs) have been developed to diagnose urine cytology, more evidence is required to prove if such systems can predict histopathology results as well.

Methods: We retrospectively retrieved urine cytology slides and matched histological results. High-power field panel images were annotated by a certified urological pathologist. A deep learning system was designed with a ResNet101 Faster R-CNN (faster region-based convolutional neural network). It was firstly built to spot cancer cells. Then, it was directly used to predict the likelihood of the presence of tissue malignancy.

Results: We retrieved 441 positive cases and 395 negative cases. The development involved 387 positive cases, accounting for 2,668 labeled cells, to train the DLS to spot cancer cells. The DLS was then used to predict corresponding histopathology results. In an internal test set of 85 cases, the area under the curve (AUC) was 0.90 (95%CI 0.84-0.96), and the kappa score was 0.68 (95%CI 0.52-0.84), indicating substantial agreement. The F1 score was 0.56, sensitivity was 71% (95%CI 52%-85%), and specificity was 94% (95%CI 84%-98%). In an extra test set of 333 cases, the DLS achieved 0.25 false-positive cells per image. The AUC was 0.93 (95%CI 0.90-0.95), and the kappa score was 0.58 (95%CI 0.46-0.70) indicating moderate agreement. The F1 score was 0.66, sensitivity was 67% (95%CI 54%-78%), and specificity was 92% (95%CI 88%-95%).

Conclusions: The deep learning system could predict if there was malignancy using cytocentrifuged urine cytology images. The process was explainable since the prediction of malignancy was directly based on the abnormal cells selected by the model and can be verified by examining those candidate abnormal cells in each image. Thus, this DLS was not just a tool for pathologists in cytology diagnosis. It simultaneously provided novel histopathologic insights for urologists.