Existing trends and applications of artificial intelligence in urothelial cancer

A scoping review

Authors

  • Shamir Malik Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
  • Jeremy Wu University of Toronto
  • Nicole Bodnariuc
  • Krishnateja Narayana
  • Naveen Gupta
  • Mikail Malik
  • Jethro C.C. Kwong University of Toronto
  • Adree Khondker
  • Alistair E.W. Johnson
  • Girish S. Kulkarni

DOI:

https://doi.org/10.5489/cuaj.8322

Keywords:

Urothelial cancer, Artificial intelligence

Abstract

INTRODUCTION: The use of artificial intelligence (AI) in urology is gaining significant traction. While previous reviews of AI applications in urology exist, there have been few attempts to synthesize existing literature on urothelial cancer (UC).

METHODS: Comprehensive searches based on the concepts of “AI” and “urothelial cancer” were conducted in MEDLINE, EMBASE, Web of Science, and Scopus. Study selection and data abstraction were conducted by two independent reviewers. Two independent raters assessed study quality in a random sample of 25 studies with the prediction model risk of bias assessment tool (PROBAST) and the standardized reporting of machine learning applications in urology (STREAM-URO) framework.

RESULTS: From a database search of 4581 studies, 227 were included. By area of research, 33% focused on image analysis, 26% on genomics, 16% on radiomics, and 15% on clinicopathology. Thematic content analysis identified qualitative trends in AI models employed and variables for feature extraction. Only 19% of studies compared performance of AI models to non-AI methods. All selected studies demonstrated high risk of bias for analysis and overall concern with Cohen’s kappa (k)=0.68. Selected studies met 66% of STREAM-URO items, with k=0.76.

CONCLUSIONS: The use of AI in UC is a topic of increasing importance; however, there is a need for improved standardized reporting, as evidenced by the high risk of bias and low methodologic quality identified in the included studies.

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Published

2023-08-03

How to Cite

Malik, S., Wu, J., Bodnariuc, N., Narayana, K., Gupta, N., Malik, M., Kwong, J. C., Khondker, A., Johnson, A. E., & Kulkarni, G. S. (2023). Existing trends and applications of artificial intelligence in urothelial cancer: A scoping review. Canadian Urological Association Journal, 17(11), E395–401. https://doi.org/10.5489/cuaj.8322

Issue

Vol. 17 No. 11 (2023): CUAJ November

Section

Review

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