A WSI-Independent Inference Framework for Cervical and Urinary Cytology Using Hierarchical Labels.

Authors

Yuki Kurita, Shiori Meguro, Yasunori Enomoto, Hideya Kawasaki, Takeji Saitoh, Makoto Suzuki, Junichi Sakane, Daisuke Suzuki, Osamu Ishidou, Kazuya Shinmura, Toshihide Iwashita

Published in

Cytopathology : official journal of the British Society for Clinical Cytology. Oct 20, 2026. Epub Oct 20, 2026.

Abstract

Cytology artificial intelligence (AI) systems often depend on whole-slide images (WSIs) or high-magnification cell images and are commonly optimized for a single specimen type. We developed CYTOLONE-v2, a contrastive language-image pretraining (CLIP)-based hierarchical classification framework for cervical and urinary cytology that is designed for individual-field inference without WSI input.
CYTOLONE-v1.1, previously trained for cervical cytology, was further trained by low-rank adaptation using WSI-derived urinary cytology fields equivalent to ×10 magnification. The 90-case source cohort was divided at the case level into training (72 cases; 360 fields) and validation (18 cases; 90 fields) subsets. Five diagnosis-representative fields were manually selected from each case. Field-level performance was evaluated in an independent urinary test cohort of 40 cases (20 Reactive change and 20 Urothelial carcinoma; 200 fields), and retention was assessed using an independent cervical cytology test set.
In urinary cytology, CYTOLONE-v2 achieved field-level accuracies of 64.0% for Anomaly and Malignancy and 72.5% for Diagnosis. At the Diagnosis level, sensitivity for urothelial carcinoma was 99.0% (95% confidence interval [CI], 96.7%-100%), whereas specificity against Reactive change was 46.0% (95% CI, 30.0%-62.0%). Cervical cytology accuracy was retained at the Anomaly level (94.8% vs. 94.7% with CYTOLONE-v1.1), decreased modestly at the Malignancy level (91.2% vs. 93.5%), and decreased more at the Bethesda and Diagnosis levels.
CYTOLONE-v2 demonstrates the technical feasibility of extending a hierarchical cytology classification framework from cervical to urinary cytology. The findings are limited to selected WSI-derived fields and a Reactive change-versus-Urothelial carcinoma test cohort; external, case-level, direct-microscopy and prospective workflow validation is required before clinical utility can be established.

PMID:
42897965
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.

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