Does artificial intelligence redefine nuclear-to-cytoplasmic ratio threshold for diagnosing high-grade urothelial carcinoma?

Wei-Lei Yang¹, Barbara A Crothers¹, Tien-Jen Liu¹, Shih-Wen Hsu¹, Cheng-Hung Yeh¹, Yi-Siou Liu¹, Guowei Shao¹, Ming-Yu Lin¹, Tang-Yi Tsao², Min-Che Tung³, Pei-Yi Chu⁴, Jen-Fan Hang^5,6

¹AIxMed, Inc, Santa Clara, California, USA.

²Department of Pathology, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan.

³Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan.

⁴Department of Pathology, Show Chwan Memorial Hospital, Changhua, Taiwan.

⁵Division of Cytopathology, Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.

⁶School of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.

PubMed PMC DOI PDF

Abstract

BACKGROUND: The Paris System (TPS) introduced standardized nuclear-to-cytoplasmic (N/C) ratio thresholds for urine cytology to improve high-grade urothelial carcinoma (HGUC) detection, but these criteria remain subjective. This study used AIxURO, an artificial intelligence-based model, to measure N/C ratio and nuclear area to identify abnormal cells in whole slide images (WSIs). METHODS: A total…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.