Radiomics: Images Are More than Pictures, They Are Data
2015/11/18 by Robert J. Gillies, Paul E. Kinahan, Hedvig Hricak · 8,407 citations
Computer Science · Medicine · #AI in cancer detection #Artificial intelligence #Clinical Practice #Computer science #Data extraction #Data mining #Data science #Data set #Field (mathematics) #MEDLINE #Machine learning #Medical imaging #Medical physics #Medicine #Pancreatic and Hepatic Oncology Research #Process (computing) #Radiology #Radiomics #Radiomics and Machine Learning in Medical Imaging #Set (abstract data type)
paper · doi:10.1148/radiol.2015151169
published in Radiology 278(2), 563-577 (Radiological Society of North America)
openalex publication_date 2015/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Abstract
In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Citations
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