No abstract

The CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses.

Pre-treatment MR-IBMs were associated to radiation-induced xerostomia, which supported the hypothesis that the amount of predisposed fat within the parotid glands is associated with Xer. In addition, xerostomia prediction was improved with MR-IBMs compared to the reference model.

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.