Background The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid‐based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear‐to‐cytoplasmic (N:C) ratio and produce a qualitative “atypia score.” The authors propose a hybrid deep‐learning and morphometric model that reliably automates the Paris System. Methods Whole‐slide images (WSI) of liquid‐based urine cytology specimens were extracted from 51 negative, 60 atypical, 52 suspicious, and 54 positive cases. Morphometric algorithms were applied to decompose images to their component parts; and statistics, including the NC ratio, were tabulated using segmentation algorithms to create organized data structures, dubbed rich information matrices (RIMs). These RIM objects were enhanced using deep‐learning algorithms to include qualitative measures. The augmented RIM objects were then used to reconstruct WSIs with filtering criteria and to generate pancellular statistical information. Results The described system was used to calculate the N:C ratio for all cells, generate object classifications (atypical urothelial cell, squamous cell, crystal, etc), filter the original WSI to remove unwanted objects, rearrange the WSI to an efficient, condensed‐grid format, and generate pancellular statistics containing quantitative/qualitative data for every cell in a WSI. In addition to developing novel techniques for managing WSIs, a system capable of automatically tabulating the Paris System criteria also was generated. Conclusions A hybrid deep‐learning and morphometric algorithm was developed for the analysis of urine cytology specimens that could reliably automate the Paris System and provide many avenues for increasing the efficiency of digital screening for urine WSIs and other cytology preparations.

Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy and reliability of bladder cancer screening, which has heretofore relied on semi-subjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices, e.g., The Paris System for Reporting Urinary Cytology (TPS), algorithms to emulate semi-autonomous diagnostic decision-making have lagged behind, in part due to the complex and nuanced nature of urine cytology reporting. In this study, we report on a deep learning tool, AutoParis-X, which can facilitate rapid semi-autonomous examination of urine cytology specimens. Through a large-scale retrospective validation study, results indicate that AutoParis-X can accurately determine urothelial cell atypia and aggregate a wide-variety of cell and cluster-related information across a slide to yield an Atypia Burden Score (ABS) that correlates closely with overall specimen atypia, predictive of TPS diagnostic categories. Importantly, this approach accounts for challenges associated with assessment of overlapping cell cluster borders, which improved the ability to predict specimen atypia and accurately estimate the nuclear-to-cytoplasm (NC) ratio for cells in these clusters. We developed an interactive web application that is publicly available and open-source, which features a simple, easy-to-use display for examining urine cytology whole-slide images (WSI) and determining the atypia level of specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis-X (and other semi-automated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms via head-to-head clinical trials.

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Castleman disease is a rare benign lymphoproliferative disorder that includes a spectrum of distinct histopathological entities. The differential diagnosis of Castleman disease is broad and includes lymphomas, HIV-related lymphadenopathy, autoimmune disorders, and inflammatory liposarcoma. When Castleman disease occurs in the retroperitoneum, the distinction from the inflammatory variant of well-differentiated liposarcoma can be very challenging in small biopsies. Herein we report a case of Castleman disease that presented as a retroperitoneal mass and expressed MDM2 and CDK4 by immunohistochemistry. To our knowledge, this is the first report of Castleman disease staining positively for MDM2/CDK4, and it underscores how immunohistochemistry can potentially serve as a pitfall when differentiating this rare entity from retroperitoneal sarcomas.