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Liu TJ, Yang WC, Huang SM, Yang WL, Wu HJ, Ho HW, Hsu SW, Yeh CH, Lin MY, Hwang YT, Chu PY. Evaluating artificial intelligence-enhanced digital urine cytology for bladder cancer diagnosis. Cancer Cytopathol 2024. [PMID: 39003588 DOI: 10.1002/cncy.22884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/11/2024] [Accepted: 06/18/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence-based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time. METHODS One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole-slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS-guided and binary diagnosis, inter- and intraobserver agreement, and screening time, were compared across all methods and reviewers. RESULTS AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%-30.6% to 63.9%), positive predictive value (PPV; from 21.6%-24.3% to 31.1%), and negative predictive value (NPV; from 91.3%-91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high-grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%-27.3% to 33.3%), PPV (from 31.3%-47.4% to 61.1%), and NPV (from 91.6%-92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%-82.2% to 90.0%) and NPV (from 91.7%-93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57-0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75-0.88). AIxURO significantly reduced screening time by 52.3%-83.2% from microscopy and 43.6%-86.7% from WSI review across all reviewers. Screening-positive (AUC+) cases required more time than negative cases across all methods and reviewers. CONCLUSIONS AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.
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Affiliation(s)
| | - Wen-Chi Yang
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Department of Pathology, Show Chwan Memorial Hospital, Changhua, Taiwan
| | - Shin-Min Huang
- Department of Pathology, Show Chwan Memorial Hospital, Changhua, Taiwan
| | | | - Hsing-Ju Wu
- Research Assistant Center, Show Chwan Memorial Hospital, Changhua, Taiwan
| | - Hui Wen Ho
- Research Assistant Center, Show Chwan Memorial Hospital, Changhua, Taiwan
| | | | | | | | - Yi-Ting Hwang
- Department of Statistics, National Taipei University, Taipei, Taiwan
| | - Pei-Yi Chu
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Department of Pathology, Show Chwan Memorial Hospital, Changhua, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- National Institute of Cancer Research, National Health Research Institutes, Tainan, Taiwan
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Hang JF, Ou YC, Yang WL, Tsao TY, Yeh CH, Li CB, Hsu EY, Hung PY, Lin MY, Hwang YT, Liu TJ, Tung MC. Evaluating Urine Cytology Slide Digitization Efficiency: A Comparative Study Using an Artificial Intelligence-Based Heuristic Scanning Simulation and Multiple Z-Plane Scanning. Acta Cytol 2024:1-9. [PMID: 38648759 DOI: 10.1159/000538985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/16/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by prolonged scanning times, increased image file sizes, and the requirement for cytopathologists to review multiple Z-plane images. METHODS This study presents heuristic scan as a novel solution, using an artificial intelligence (AI)-based approach specifically designed for cytology slide scanning as an alternative to the multi-Z-plane scan. Both the 21 Z-plane scan and the heuristic scan simulation methods were used on 52 urine cytology slides from three distinct cytopreparations (Cytospin, ThinPrep, and BD CytoRich™ [SurePath]), generating whole-slide images (WSIs) via the Leica Aperio AT2 digital scanner. The AI algorithm inferred the WSI from 21 Z-planes to quantitate the total number of suspicious for high-grade urothelial carcinoma or more severe cells (SHGUC+) cells. The heuristic scan simulation calculated the total number of SHGUC+ cells from the 21 Z-plane scan data. Performance metrics including SHGUC+ cell coverage rates (calculated by dividing the number of SHGUC+ cells identified in multiple Z-planes or heuristic scan simulation by the total SHGUC+ cells in the 21 Z-planes for each WSI), scanning time, and file size were analyzed to compare the performance of each scanning method. The heuristic scan's metrics were linearly estimated from the 21 Z-plane scan data. Additionally, AI-aided interpretations of WSIs with scant SHGUC+ cells followed The Paris System guidelines and were compared with original diagnoses. RESULTS The heuristic scan achieved median SHGUC+ cell coverage rates similar to 5 Z-plane scans across three cytopreparations (0.78-0.91 vs. 0.75-0.88, p = 0.451-0.578). Notably, it substantially reduced both scanning time (137.2-635.0 s vs. 332.6-1,278.8 s, p < 0.05) and image file size (0.51-2.10 GB vs. 1.16-3.10 GB, p < 0.05). Importantly, the heuristic scan yielded higher rates of accurate AI-aided interpretations compared to the single Z-plane scan (62.5% vs. 37.5%). CONCLUSION We demonstrated that the heuristic scan offers a cost-effective alternative to the conventional multi-Z-plane scan in digital cytopathology. It achieves comparable SHGUC+ cell capture rates while reducing both scanning time and image file size, promising to aid digital urine cytology interpretations with a higher accuracy rate compared to the conventional single (optimal) plane scan. Further studies are needed to assess the integration of this new technology into compatible digital scanners for practical cytology slide scanning.
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Affiliation(s)
- Jen-Fan Hang
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Chuan Ou
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
| | | | - Tang-Yi Tsao
- Department of Pathology, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
| | | | - Chi-Bin Li
- AIxMed, Inc., Santa Clara, California, USA
| | - En-Yu Hsu
- AIxMed, Inc., Santa Clara, California, USA
| | | | | | - Yi-Ting Hwang
- Department of Statistics, National Taipei University, Taipei, Taiwan
| | | | - Min-Che Tung
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
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Levy J, Yao K. The future of digital cytology and artificial intelligence: an editorial commentary for Digital Cytology part 1 and 2. J Am Soc Cytopathol 2024; 13:81-85. [PMID: 38267293 DOI: 10.1016/j.jasc.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/26/2024]
Affiliation(s)
- Joshua Levy
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Keluo Yao
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California; Enterprise Information Services, Cedars-Sinai, Los Angeles, California.
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