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

World J Urol. 2023 Sep;41(9):2381-2388. doi: 10.1007/s00345-023-04519-4. Epub 2023 Jul 22.

Abstract

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.

Keywords: Artificial intelligence; Bladder; Cancer; Deep learning; Markers; Urothelial.

Publication types

  • Multicenter Study

MeSH terms

  • Adult
  • Artificial Intelligence
  • Carcinoma, Transitional Cell* / diagnosis
  • Cytological Techniques
  • Humans
  • Prospective Studies
  • Urinary Bladder Neoplasms* / diagnosis