Large‐scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis‐X

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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.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Using Autoparis-x To Derive Imaging Predictors Of Recurrencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…50 In this specific work, we investigate the potential of using machine learning from UC in predicting recurrence among a cohort of patients. 40…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Examining longitudinal markers of bladder cancer recurrence through a semiautonomous machine learning system for quantifying specimen atypia from urine cytology

Levy

Chan

Marotti

et al. 2023

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Current role of cytopathology in the molecular and computational era: The perspective of young pathologists

Caputo,

Pisapia,

L'Imperio

2024

Cytopathology represents a well established diagnostic approach because of its limited cost, reliability, and minimal invasiveness with respect to other methodologies. The evolving complexity of the different classifications systems and the implementation of ancillary techniques to refine the diagnosis is progressively helping in the risk of malignancy stratification, and the adoption of next‐generation sequencing techniques contributes to enrich this valuable tool with predictive information, which is always more essential in the tailored medicine era. The recent introduction of digital and computational pathology is further boosting the potentialities of cytopathology, aiding in the interpretation of samples to improve the cost effectiveness of large screening programs and the diagnostic efficiency within intermediate/atypical categories. Moreover, the adoption of artificial intelligence tools is promising to complement molecular investigations, representing a stimulating perspective in the cytopathology field. In this work, the authors tried to summarize the multifaceted nature of this complex and evolving field of pathology, synthesizing the most recent advances and providing the young pathologists’ perspective on this fascinating world.

Evaluating artificial intelligence–enhanced digital urine cytology for bladder cancer diagnosis

Liu,

Yang,

Huang

et al. 2024

BackgroundThis study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence–based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time.MethodsOne cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole‐slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS‐guided and binary diagnosis, inter‐ and intraobserver agreement, and screening time, were compared across all methods and reviewers.ResultsAIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%–30.6% to 63.9%), positive predictive value (PPV; from 21.6%–24.3% to 31.1%), and negative predictive value (NPV; from 91.3%–91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high‐grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%–27.3% to 33.3%), PPV (from 31.3%–47.4% to 61.1%), and NPV (from 91.6%–92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%–82.2% to 90.0%) and NPV (from 91.7%–93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57–0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75–0.88). AIxURO significantly reduced screening time by 52.3%–83.2% from microscopy and 43.6%–86.7% from WSI review across all reviewers. Screening‐positive (AUC+) cases required more time than negative cases across all methods and reviewers.ConclusionsAIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.