Artificial intelligence and machine learning in cancer imaging

Dow-Mu Koh; Nickolas Papanikolaou; Ulrich Bick; Rowland Illing; Charles E Kahn Jr; Jayshree Kalpathi-Cramer; Celso Matos; Luis Martí-Bonmatí; Anne Miles; Seong Ki Mun; Sandy Napel; Andrea Rockall; Evis Sala; Nicola Strickland; Fred Prior

doi:10.1038/s43856-022-00199-0

Artificial intelligence and machine learning in cancer imaging

Commun Med (Lond). 2022 Oct 27:2:133. doi: 10.1038/s43856-022-00199-0. eCollection 2022.

Authors

Dow-Mu Koh^#¹, Nickolas Papanikolaou^#^{1

2}, Ulrich Bick³, Rowland Illing⁴, Charles E Kahn Jr⁵, Jayshree Kalpathi-Cramer⁶, Celso Matos², Luis Martí-Bonmatí⁷, Anne Miles⁸, Seong Ki Mun⁹, Sandy Napel¹⁰, Andrea Rockall¹¹, Evis Sala¹², Nicola Strickland¹¹, Fred Prior¹³

Affiliations

¹ Department of Radiology, Royal Marsden Hospital, Sutton, UK.
² Champalimaud Foundation, Lisbon, Portugal.
³ Charité - Universitätsmedizin Berlin, Berlin, Germany.
⁴ Department of Surgery & Interventional Science, University College London, London, UK.
⁵ Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, USA.
⁶ Centre for machine learning, Massachusetts General Hospital/Harvard Medical School, Boston, USA.
⁷ Department of Radiology, Hospital Universitari i Politècnic La Fe, Valencia, Spain.
⁸ Department of Psychological Sciences, Birkbeck University, London, UK.
⁹ Arlington Innovation Center for Health Research, Virginia Tech, Arlington, USA.
¹⁰ Department of Radiology, Stanford University, Stanford, USA.
¹¹ Department of Radiology, Imperial College Healthcare NHS Trust, London, UK.
¹² Department of Radiology, Cambridge University, Cambridge, UK.
¹³ Department of Biomedical Informatics and Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, USA.

^# Contributed equally.

Abstract

An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.

Keywords: Biomarkers; Cancer imaging.

Publication types

Review