Images Are Data: Challenges and Opportunities in the Clinical Translation of Radiomics

Radiomics provides an opportunity to uncover image-based biomarkers through the conversion and analysis of standard-of-care medical images into high-dimensional mineable data. In the last decade, thousands of studies have been published on different clinical applications, novel analysis algorithms, and the stability and reproducibility of radiomics. Despite this, interstudy comparisons are challenging because there is not a generally accepted analytic and reporting standard. The ability to compare and combine results from multiple studies using interoperative platforms is an essential component on the path toward clinical application. The NCI supported study from van Griethuysen and colleagues published in Cancer Research in 2017 proposed PyRadiomics: an open-source radiomics quantification platform for standardized image processing. Since released, it has become a frequently utilized analytic tool in the radiomics literature and has accelerated the capability of combining data from different studies. The subsequent challenge will be the design of multicenter trials with a fixed and immutable version of software, which is currently open-source, readily modified and freely distributed. Generally, this is accomplished with a commercial partner to navigate the regulatory processes.

Automated summary

Radiomics provides an opportunity to uncover image-based biomarkers by converting and analyzing medical images into high-dimensional mineable data. However, the lack of a generally accepted analytic and reporting standard makes interstudy comparisons challenging. Comparing and combining results from multiple studies is essential for clinical application.