Utilisation of artificial intelligence to enhance the detection rates of renal cancer on cross-sectional imaging: protocol for a systematic review and meta-analysis.

Journal: BMJ open
Published Date:

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 are 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.

Authors

  • Ojone Ofagbor
    Department of Urology, Norfolk and Norwich University Hospital, Norwich, UK OJ_OFAGBOR@doctors.org.uk.
  • Gaurika Bhardwaj
    Department of Urology, Imperial College Healthcare NHS Trust, London, UK.
  • Yi Zhao
    Department of Biostatistics and Health Data Science, Indiana University School of Medicine.
  • Mohamed Baana
    Department of Urology, London North West University Healthcare NHS Trust, Harrow, UK.
  • Murtada Arkwazi
    London North West University Healthcare NHS Trust, Harrow, UK.
  • Mariam Lami
    Department of Urology, Imperial College Healthcare NHS Trust, London, UK.
  • Eva Bolton
    Department of Urology, Imperial College Healthcare NHS Trust, London, UK.
  • Rakesh Heer
    Division of Surgery, Imperial College London, London, United Kingdom.