Radiomics Research: Current Status, Limitations, and Guide for Strategic Changes

Abstract

Radiomics uses machine learning to capture detailed interpixel or intervoxel relationships within medical images, beyond the limits of human perception and current radiology reporting. This potential to more deeply explore the content of medical images has fueled a growing interest in radiomics by the radiology research community, as investigators have sought to use radiomics to provide novel insights and biomarkers to improve patient outcomes. However, further integration of radiomics into clinical practice has been hindered by a range of challenges and barriers, precluding full clinical adoption of any single radiomics application. This article describes current limitations in the existing body of radiomics research and highlights the strategic changes needed for radiomics research to yield more meaningful translational evidence in this space. Though setting a high bar, the guidance could lead to the evidence required for radiomics to move into clinical arenas—or indicate if radiomics best stays a research technique.

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