Cancer cytopathology · 2026
Enhancing urothelial carcinoma diagnosis with artificial intelligence-integrated urine cytology: Biopsy-validated accuracy and efficiency gain.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Lajara Sigfred, Cuda Jacqueline, Geisler Daniel L, Staniszewski Caroline, Yang Wei-Lei, Atkison Karen, Lin Chih-Yun, Shao Guowei, Liu Tien-Jen, Crothers Barbara, Dhir Rajiv, Khader Samer N
Аннотация
BACKGROUND: Urine cytology is a noninvasive tool for detecting urothelial carcinoma, yet its performance depends heavily on expert cytologists and time-intensive glass slide review. Artificial intelligence (AI)-assisted digital cytology has emerged as a potential solution to improve diagnostic sensitivity and workflow efficiency. This retrospective study aimed to evaluate the diagnostic performance and workflow impact of AIxURO, an AI-assisted digital urine cytology system, in a high-volume US medical center.
METHODS: Two hundred ThinPrep cytology slides were digitized with two distinct customized scanners integrated into the AIxURO imaging system (AIS-1 and AIS-2). Three reviewers independently assessed each case across three diagnostic modalities: microscopy, AIxURO-AIS-1, and AIxURO-AIS-2. Performance for binary bladder cancer detection was compared against expert consensus cytology diagnoses; review time, biopsy correlation, and performance in patients presenting with hematuria were also evaluated.
RESULTS: AIxURO (AIS-1 and AIS-2) improved diagnostic sensitivity (85.0% and 88.3%) and negative predictive value (NPV) (85.1% and 87.6%) relative to microscopy (79.3% and 82.0%), whereas accuracy remained comparable across modalities. These sensitivity gains were accompanied by lower specificity (85.7% and 82.3% vs. 94.3%) and positive predictive value (PPV) (85.6% and 83.3% vs. 93.3%) compared with microscopy. Both AI modalities reduced classifying atypical urothelial cells and above cases as negative for high-grade urothelial carcinoma cases, and shortened median review time by 66%-78% (p < .0001). Among the 200 cases, 98 (49%) had biopsy confirmation, in which AIxURO showed higher sensitivity and NPV than microscopy. In 16 hematuria cases with biopsy correlation, AI-assisted screening achieved superior diagnostic performance.
CONCLUSIONS: AIxURO enhances sensitivity for detecting urothelial carcinoma and markedly improves screening efficiency, with a tradeoff of reduced specificity/PPV compared with microscopy.
METHODS: Two hundred ThinPrep cytology slides were digitized with two distinct customized scanners integrated into the AIxURO imaging system (AIS-1 and AIS-2). Three reviewers independently assessed each case across three diagnostic modalities: microscopy, AIxURO-AIS-1, and AIxURO-AIS-2. Performance for binary bladder cancer detection was compared against expert consensus cytology diagnoses; review time, biopsy correlation, and performance in patients presenting with hematuria were also evaluated.
RESULTS: AIxURO (AIS-1 and AIS-2) improved diagnostic sensitivity (85.0% and 88.3%) and negative predictive value (NPV) (85.1% and 87.6%) relative to microscopy (79.3% and 82.0%), whereas accuracy remained comparable across modalities. These sensitivity gains were accompanied by lower specificity (85.7% and 82.3% vs. 94.3%) and positive predictive value (PPV) (85.6% and 83.3% vs. 93.3%) compared with microscopy. Both AI modalities reduced classifying atypical urothelial cells and above cases as negative for high-grade urothelial carcinoma cases, and shortened median review time by 66%-78% (p < .0001). Among the 200 cases, 98 (49%) had biopsy confirmation, in which AIxURO showed higher sensitivity and NPV than microscopy. In 16 hematuria cases with biopsy correlation, AI-assisted screening achieved superior diagnostic performance.
CONCLUSIONS: AIxURO enhances sensitivity for detecting urothelial carcinoma and markedly improves screening efficiency, with a tradeoff of reduced specificity/PPV compared with microscopy.