Diagnosis of Schistosoma haematobium on Voided Urine Cytology

Laser, Alice; Kacher, John E; Amodio, John; Strubel, Naomi; Elgert, Paul; Ph, Levine

doi:10.1159/000325092

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Unlike the previous three scores which focus on individual urothelial cells, identified urothelial cell clusters represent the principal unit of analysis. Using AutoParis-X, RIF-SIF scores were calculated across the slide-level training and validation cohorts . We added the following patient-level characteristics to the RIF-SIF scores: 1) age; 2) sex; 3) history of hematuria; and 4) specimen source 82, 83 . We also noted where slides contained significant blood, high cellularity, acellularity, neobladders (abundant degenerated enterocytes) and scanning artifacts.…”

Section: Methodsmentioning

confidence: 99%

Large-Scale Validation Study of an Improved Semi-Autonomous Urine Cytology Assessment Tool: AutoParis-X

Levy

Chan

Marotti

et al. 2023

Preprint

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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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Section: Methodsmentioning

confidence: 99%