The roadmap to the adoption of computational intelligence in cancer diagnosis: The clinical-radiological perspective

Abstract

In the last decade, important breakthroughs took place in the clinical application of computational intelligence in oncology. Accurate diagnosis of cancer, personalized therapeutic approaches for cancer, and outcome prediction remain a crucial clinical demand in oncologic patients. Many radiomics and artificial intelligence models have been constructed and tested so far and have the potential to assist oncologic multidisciplinary teams in for cancers diagnosis, staging, and assessment of treatment response. However, the clinical implementation of these computational models is still limited due to the fact that most radiomics, radiogenomics, and artificial intelligence studies are deemed of insufficient quality. An international effort and further prospective large-scale studies are needed to fill-in the gap between research study setting and clinical application.