Artificial intelligence to improve cytology performance in urothelial carcinoma diagnosis: results from validation phase of the French, multicenter, prospective VISIOCYT1 trial

World journal of urology(2023)

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摘要
Purpose Cytology and cystoscopy, the current gold standard for diagnosing urothelial carcinomas, have limits: cytology has high interobserver variability with moderate or not optimal sensitivity (particularly for low-grade tumors); while cystoscopy is expensive, invasive, and operator dependent. The VISIOCYT1 study assessed the benefit of VisioCyt ® for diagnosing urothelial carcinoma. Methods VISIOCYT1 was a French prospective clinical trial conducted in 14 centers. The trial enrolled adults undergoing endoscopy for suspected bladder cancer or to explore the lower urinary tract. Participants were allocated either Group 1: with bladder cancer, i.e., with positive cystoscopy or with negative cystoscopy but positive cytology, or Group 2: without bladder cancer. Before cystoscopy and histopathology, slides were prepared for cytology and the VisioCyt ® test from urine samples. The diagnostic performance of VisioCyt ® was assessed using sensitivity (primary objective, 70% lower-bound threshold) and specificity (75% lower-bound threshold). Sensitivity was also assessed by tumor grade and T-staging. VisioCyt ® and cytology performance were evaluated relative to the histopathological assessments. Results Between October 2017 and December 2019, 391 participants (170 in Group 1 and 149 in Group 2) were enrolled. VisioCyt ® ’s sensitivity was 80.9% (95% CI 73.9–86.4%) and specificity was 61.8% (95% CI 53.4–69.5%). In high-grade tumors, the sensitivity was 93.7% (95% CI 86.0–97.3%) and in low-grade tumors 66.7% (95% CI 55.2–76.5%). Sensitivity by T-staging, compared to the overall sensitivity, was higher in high-grade tumors and lower in low-grade tumors. Conclusion VisioCyt ® is a promising diagnostic tool for urothelial cancers with improved sensitivities for high-grade tumors and notably for low-grade tumors.
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urothelial carcinoma diagnosis,cytology performance,artificial intelligence
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