BackgroundAdopting 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 semisubjective 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), algorithms to emulate semiautonomous diagnostic decision‐making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting.MethodsIn this study, the authors report on the development and large‐scale validation of a deep‐learning tool, AutoParis‐X, which can facilitate rapid, semiautonomous examination of urine cytology specimens.ResultsThe results of this large‐scale, retrospective validation study indicate that AutoParis‐X can accurately determine urothelial cell atypia and aggregate a wide variety of cell‐related and cluster‐related information across a slide to yield an atypia burden score, which correlates closely with overall specimen atypia and is predictive of Paris system diagnostic categories. Importantly, this approach accounts for challenges associated with the assessment of overlapping cell cluster borders, which improve the ability to predict specimen atypia and accurately estimate the nuclear‐to‐cytoplasm ratio for cells in these clusters.ConclusionsThe authors developed a publicly available, open‐source, interactive web application that features a simple, easy‐to‐use display for examining urine cytology whole‐slide images and determining the level of atypia in specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis‐X (and other semiautomated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms in head‐to‐head clinical trials.

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.

Urine cytology (UC) is generally considered the primary approach for screening for recurrence of bladder cancer. However, it is currently unclear how best to use cytological exams themselves for the assessment and early detection of recurrence, beyond identifying a positive finding which requires more invasive methods to confirm recurrence and decide on therapeutic options. As screening programs are frequent, and can be burdensome, finding quantitative means to reduce this burden for patients, cytopathologists and urologists is an important endeavor and can improve both the efficiency and reliability of findings. Additionally, identifying ways to risk-stratify patients is crucial for improving quality of life while reducing the risk of future recurrence or progression of the cancer. In this study, we leveraged a computational machine learning tool, AutoParis-X, to extract imaging features from UC exams longitudinally to study the predictive potential of urine cytology for assessing recurrence risk. This study examined how the significance of imaging predictors changes over time before and after surgery to determine which predictors and time periods are most relevant for assessing recurrence risk. Results indicate that imaging predictors extracted using AutoParis-X can predict recurrence as well or better than traditional cytological / histological assessments alone and that the predictiveness of these features is variable across time, with key differences in overall specimen atypia identified immediately before tumor recurrence. Further research will clarify how computational methods can be effectively utilized in high volume screening programs to improve recurrence detection and complement traditional modes of assessment.

BackgroundUrine cytology is generally considered the primary approach for screening for recurrence of bladder cancer. However, it is currently unclear how best to use cytological examinations for assessment and early detection of recurrence, beyond identifying a positive finding that requires more invasive methods to confirm recurrence and decide on therapeutic options. Because screening programs are frequent, and can be burdensome, finding quantitative means to reduce this burden for patients, cytopathologists, and urologists is an important endeavor and can improve both the efficiency and reliability of findings. Additionally, identifying ways to risk‐stratify patients is crucial for improving quality of life while reducing the risk of future recurrence or progression of the cancer.MethodsIn this study, a computational machine learning tool, AutoParis‐X, was leveraged to extract imaging features from urine cytology examinations longitudinally to study the predictive potential of urine cytology for assessing recurrence risk. This study examined how the significance of imaging predictors changes over time before and after surgery to determine which predictors and time periods are most relevant for assessing recurrence risk.ResultsResults indicate that imaging predictors extracted using AutoParis‐X can predict recurrence as well or better than traditional cytological/histological assessments alone and that the predictiveness of these features is variable across time, with key differences in overall specimen atypia identified immediately before tumor recurrence.ConclusionsFurther research will clarify how computational methods can be effectively used in high‐volume screening programs to improve recurrence detection and complement traditional modes of assessment.