Deep learning-based mitotic quantification for objective adjunct for grading of urothelial carcinoma: a multi-institutional validation study

Abstract

Histological grading of urothelial carcinoma is subject to interobserver variability. We evaluated whether artificial intelligence (AI)-based whole-slide mitotic quantification provides an objective adjunct to histological grading. We developed and validated a deep learning-based mitotic detection framework trained on 91,183 patches with 318,893 annotations. A multi-reader crossover study was conducted in internal ( n  = 149) and external ( n  = 316) cohorts, where three independent pathologists graded cases in unassisted and model-assisted phases. In the combined cohort ( n  = 465), AI-derived mitotic count showed high diagnostic accuracy for high-grade carcinoma (area under the curve [AUC] 0.91, 95% CI 0.89–0.94) at a cut-off of 17.5 mitoses per 2 mm². AI assistance significantly improved mean reader AUC from 0.90 to 0.98 in the internal cohort ( p  = 0.038). In the external cohort, the mean reader AUC increased from 0.84 to 0.95, although this difference did not reach statistical significance ( p  = 0.11). AI assistance also significantly reduced interobserver variability (Fleiss’s κ: 0.72 to 0.93 internally; 0.62 to 0.85 externally; both, p  < 0.001). AI-based mitotic quantification provides an objective and standardized adjunct to histological grading and reduces interobserver variability. This approach may improve diagnostic accuracy and support clinical decision-making, pending prospective validation.