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• W2174661749 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." @default.
• W2174661749 created "2016-06-24" @default.
• W2174661749 creator A5021013894 @default.
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• W2174661749 date "2016-02-01" @default.
• W2174661749 modified "2023-10-14" @default.
• W2174661749 title "Radiomics: Images Are More than Pictures, They Are Data" @default.
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• W2174661749 doi "https://doi.org/10.1148/radiol.2015151169" @default.
• W2174661749 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4734157" @default.
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• W2174661749 hasPublicationYear "2016" @default.
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