The utilisation of artificial intelligence to enhance the detection rates of renal cancer on cross-sectional imaging. Protocol for a systematic review and meta-analysis
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Published version
Author(s)
Type
Journal Article
Abstract
INTRODUCTION
The incidence of renal cell carcinoma has steadily been on the increase due to the increased use of imaging to identify incidental masses. Although survival has also improved because of early detection, overdiagnosis and overtreatment of benign renal masses is associated with significant morbidity, as patients with a suspected renal malignancy on imaging undergo invasive and risky procedures for a definitive diagnosis. Therefore, accurately characterising a renal mass as benign or malignant on imaging is paramount to improving patient outcomes.
Artificial Intelligence (AI) poses an exciting solution to the problem, augmenting traditional radiological diagnosis to increase detection accuracy. This report aims to investigate and summarise the current evidence about the diagnostic accuracy of AI in characterising renal masses on imaging.
METHODS AND ANALYSIS
This will involve systematically searching PubMed, MEDLINE, Embase, Web of Science, Scopus and Cochrane databases. Publications of research that have evaluated the use of automated AI, fully or to some extent, in cross-sectional imaging for diagnosing or characterising malignant renal tumours will be included if published between July 2016 and June 2025 and in English. The protocol adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) 2015 checklist. The QUADAS-2 score will be used to evaluate the quality and risk of bias across included studies. Furthermore, in line with CLAIM AI (Checklist for Artificial Intelligence in Medical Imaging) recommendations, studies will be evaluated for including the minimum necessary information on AI research reporting.
ETHICS AND DISSEMINATION
Ethical clearance will not be necessary for conducting this systematic review, and results will be disseminated through peer-reviewed publications and presentations at both national and international conferences.
The incidence of renal cell carcinoma has steadily been on the increase due to the increased use of imaging to identify incidental masses. Although survival has also improved because of early detection, overdiagnosis and overtreatment of benign renal masses is associated with significant morbidity, as patients with a suspected renal malignancy on imaging undergo invasive and risky procedures for a definitive diagnosis. Therefore, accurately characterising a renal mass as benign or malignant on imaging is paramount to improving patient outcomes.
Artificial Intelligence (AI) poses an exciting solution to the problem, augmenting traditional radiological diagnosis to increase detection accuracy. This report aims to investigate and summarise the current evidence about the diagnostic accuracy of AI in characterising renal masses on imaging.
METHODS AND ANALYSIS
This will involve systematically searching PubMed, MEDLINE, Embase, Web of Science, Scopus and Cochrane databases. Publications of research that have evaluated the use of automated AI, fully or to some extent, in cross-sectional imaging for diagnosing or characterising malignant renal tumours will be included if published between July 2016 and June 2025 and in English. The protocol adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) 2015 checklist. The QUADAS-2 score will be used to evaluate the quality and risk of bias across included studies. Furthermore, in line with CLAIM AI (Checklist for Artificial Intelligence in Medical Imaging) recommendations, studies will be evaluated for including the minimum necessary information on AI research reporting.
ETHICS AND DISSEMINATION
Ethical clearance will not be necessary for conducting this systematic review, and results will be disseminated through peer-reviewed publications and presentations at both national and international conferences.
Date Issued
2025-08-31
Date Acceptance
2025-07-24
Citation
BMJ Open, 2025, 15 (8)
ISSN
2044-6055
Publisher
BMJ Publishing Group
Journal / Book Title
BMJ Open
Volume
15
Issue
8
Copyright Statement
© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
License URL
Identifier
10.1136/bmjopen-2024-090422
Publication Status
Published
Article Number
090422
Date Publish Online
2025-08-31