Awan, Ruqayya, Benes, Ksenija, Azam, Ayesha, Song, Tzu‐Hsi, Shaban, Muhammad, Verrill, Clare, Tsang, Yee Wah, Snead, David, Minhas, Fayyaz ul Amir Afsar and Rajpoot, Nasir M. (2021) Deep learning based digital cell profiles for risk stratification of urine cytology images. Cytometry Part A, 99 (7). pp. 732-742. doi:10.1002/cyto.a.24313 ISSN 1552-4922.

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Abstract

Urine cytology is a test for the detection of high-grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells to make a diagnosis. Accurate identification of atypical and malignant cells in urine cytology is a challenging task and is an essential part of identifying different diagnosis with low-risk and high-risk malignancy. Computer-assisted identification of malignancy in urine cytology can be complementary to the clinicians for treatment management and in providing advice for carrying out further tests. In this study, we presented a method for identifying atypical and malignant cells followed by their profiling to predict the risk of diagnosis automatically. For cell detection and classification, we employed two different deep learning-based approaches. Based on the best performing network predictions at the cell level, we identified low-risk and high-risk cases using the count of atypical cells and the total count of atypical and malignant cells. The area under the receiver operating characteristic (ROC) curve shows that a total count of atypical and malignant cells is comparably better at diagnosis as compared to the count of malignant cells only. We obtained area under the ROC curve with the count of malignant cells and the total count of atypical and malignant cells as 0.81 and 0.83, respectively. Our experiments also demonstrate that the digital risk could be a better predictor of the final histopathology-based diagnosis. We also analyzed the variability in annotations at both cell and whole slide image level and also explored the possible inherent rationales behind this variability.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history Q Science > QM Human anatomy
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
SWORD Depositor:	Library Publications Router
Library of Congress Subject Headings (LCSH):	Artificial intelligence -- Biological applications, Machine learning, Cells -- Classification -- Data Processing, Cytology, Urinary organs -- Cytology -- Data Processing, Urinary organs -- Cancer -- Data Processing, Cell separation -- Data Processing
Journal or Publication Title:	Cytometry Part A
Publisher:	John Wiley & Sons Ltd.
ISSN:	1552-4922
Official Date:	1 July 2021
Dates:	Date Event 1 July 2021 Published 20 February 2021 Available 15 December 2020 Accepted
Volume:	99
Number:	7
Page Range:	pp. 732-742
DOI:	10.1002/cyto.a.24313
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	24 August 2021
Date of first compliant Open Access:	25 August 2021
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID UNSPECIFIED University of Warwick http://dx.doi.org/10.13039/501100000741 EP/N510129/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 UNSPECIFIED UK Research and Innovation http://dx.doi.org/10.13039/100014013

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