Novel Computational Pipeline Enables Reliable Diagnosis of Inverted Urothelial Papilloma and Distinguishes It From Urothelial Carcinoma.

Purpose: With the aid of ever-increasing computing resources, many deep learning algorithms have been proposed to aid in diagnostic workup for clinicians. However, existing studies usually selected informative patches from whole-slide images for the training of the deep learning model, requiring labor-intensive labeling efforts. This work aimed to improve diagnostic accuracy through the statistic features extracted from hematoxylin and eosin-stained slides.

Methods: We designed a computational pipeline for the diagnosis of inverted urothelial papilloma (IUP) of the bladder from its cancer mimics using statistical features automatically extracted from whole-slide images. Whole-slide images from 225 cases of common and uncommon urothelial lesions (64 IUPs; 69 inverted urothelial carcinomas [UCInvs], and 92 low-grade urothelial carcinoma [UCLG]) were analyzed.

Results: We identified 68 image features in total that were significantly different between IUP and UCInv and 42 image features significantly different between IUP and UCLG. Our method integrated multiple types of image features and achieved high AUCs (the AUCs) of 0.913 and 0.920 for classifying IUP from UCInv and conventional UC, respectively. Moreover, we constructed an ensemble classifier to test the prediction accuracy of IUP from an external validation cohort, which provided a new workflow to diagnose rare cancer subtypes and test the models with limited validation samples.

Conclusion: Our data suggest that the proposed computational pipeline can robustly and accurately capture histopathologic differences between IUP and other UC subtypes. The proposed workflow and related findings have the potential to expand the clinician's armamentarium for accurate diagnosis of urothelial malignancies and other rare tumors.