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Lacoste-Collin L. [What contribution can make artificial intelligence to urinary cytology?]. Ann Pathol 2024; 44:195-203. [PMID: 38614871 DOI: 10.1016/j.annpat.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/30/2024] [Accepted: 03/24/2024] [Indexed: 04/15/2024]
Abstract
Urinary cytology using the Paris system is still the method of choice for screening high-grade urothelial carcinomas. However, the use of the objective criteria described in this terminology shows a lack of inter- and intra-observer reproducibility. Moreover, if its sensitivity is excellent on instrumented urine, it remains insufficient on voided urine samples. Urinary cytology appears to be an excellent model for the application of artificial intelligence to improve performance, since the objective criteria of the Paris system are defined at cellular level, and the resulting diagnostic approach is presented in a highly "algorithmic" way. Nevertheless, there is no commercially available morphological diagnostic aid, and very few predictive devices are still undergoing clinical validation. The analysis of different systems using artificial intelligence in urinary cytology rises clear prospects for mutual contributions.
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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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Levy JJ, Chan N, Marotti JD, Rodrigues NJ, Ismail AAO, Kerr DA, Gutmann EJ, Glass RE, Dodge CP, Suriawinata AA, Christensen B, Liu X, Vaickus LJ. Examining longitudinal markers of bladder cancer recurrence through a semiautonomous machine learning system for quantifying specimen atypia from urine cytology. Cancer Cytopathol 2023; 131:561-573. [PMID: 37358142 PMCID: PMC10527805 DOI: 10.1002/cncy.22725] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/31/2023] [Accepted: 04/20/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND Urine cytology is generally considered the primary approach for screening for recurrence of bladder cancer. However, it is currently unclear how best to use cytological examinations for assessment and early detection of recurrence, beyond identifying a positive finding that requires more invasive methods to confirm recurrence and decide on therapeutic options. Because screening programs are frequent, and can be burdensome, finding quantitative means to reduce this burden for patients, cytopathologists, and urologists is an important endeavor and can improve both the efficiency and reliability of findings. Additionally, identifying ways to risk-stratify patients is crucial for improving quality of life while reducing the risk of future recurrence or progression of the cancer. METHODS In this study, a computational machine learning tool, AutoParis-X, was leveraged to extract imaging features from urine cytology examinations longitudinally to study the predictive potential of urine cytology for assessing recurrence risk. This study examined how the significance of imaging predictors changes over time before and after surgery to determine which predictors and time periods are most relevant for assessing recurrence risk. RESULTS Results indicate that imaging predictors extracted using AutoParis-X can predict recurrence as well or better than traditional cytological/histological assessments alone and that the predictiveness of these features is variable across time, with key differences in overall specimen atypia identified immediately before tumor recurrence. CONCLUSIONS Further research will clarify how computational methods can be effectively used in high-volume screening programs to improve recurrence detection and complement traditional modes of assessment.
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Affiliation(s)
- Joshua J. Levy
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Department of Dermatology, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
- Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Natt Chan
- Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Jonathan D. Marotti
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Nathalie J. Rodrigues
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
| | - A. Aziz O. Ismail
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- White River Junction VA Medical Center, White River Junction, VT, 05009
| | - Darcy A. Kerr
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Edward J. Gutmann
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | | | | | - Arief A. Suriawinata
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Brock Christensen
- Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
- Department of Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
- Department of Community and Family Medicine, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Xiaoying Liu
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Louis J. Vaickus
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
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