Machine learning and modeling: Data, validation, communication challenges

TLDR

Machine learning is increasingly applied in radiation oncology for tasks such as treatment planning, contouring, and motion tracking, yet its routine clinical adoption remains limited. The paper seeks to advance machine learning in radiation oncology by highlighting its benefits, outlining current challenges, and proposing future research directions. Targeted at newcomers and practitioners, the paper offers recommendations to avoid common pitfalls and guidelines for transparent, informative reporting of machine learning results.

Abstract

With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.