From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML. Machine learning offers exciting potential for improved cancer detection, prognosis, and the identification of optimized therapies for patients. This review discusses advances and applications in machine learning models and techniques for the rich imaging and molecular data from the clinical oncology workflow, reviews the regulatory process for approving machine learning methods for cancer diagnostics, and outlines how to improve model design and evaluation to further adoption of machine learning in clinical oncology.