Despite technological and medical advances, the detection, interpretation,
and treatment of cancer based on imaging data continue to pose significant
challenges. These include high inter-observer variability, difficulty of
small-sized lesion detection, nodule interpretation and malignancy
determination, inter- and intra-tumour heterogeneity, class imbalance,
segmentation inaccuracies, and treatment effect uncertainty. The recent
advancements in Generative Adversarial Networks (GANs) in computer vision as
well as in medical imaging may provide a basis for enhanced capabilities in
cancer detection and analysis. In this review, we assess the potential of GANs
to address a number of key challenges of cancer imaging, including data
scarcity and imbalance, domain and dataset shifts, data access and privacy,
data annotation and quantification, as well as cancer detection, tumour
profiling and treatment planning. We provide a critical appraisal of the
existing literature of GANs applied to cancer imagery, together with
suggestions on future research directions to address these challenges. We
analyse and discuss 163 papers that apply adversarial training techniques in
the context of cancer imaging and elaborate their methodologies, advantages and
limitations. With this work, we strive to bridge the gap between the needs of
the clinical cancer imaging community and the current and prospective research
on GANs in the artificial intelligence community.