Abstract
Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.
Highlights
Radiomics approaches provide quantitative image features computed from medical images and hold promise for improved computer-aided diagnosis, treatment selection, and response prediction [1,2,3,4,5,6]
This paper presents a toolkit for the creation of digital reference object (DRO) and a sample collection of DROs made using it for radiomics experiments and illustration
We present the calculation of several theoretical radiomics values for some DROs in our sample collection and compare them to the corresponding features extracted using a particular radiomics pipeline
Summary
Radiomics approaches provide quantitative image features computed from medical images and hold promise for improved computer-aided diagnosis, treatment selection, and response prediction [1,2,3,4,5,6]. When different pipelines are run on the same imaging data, features may vary significantly across institutions and pipelines owing to differences in feature definition, software implementations, and/or parameter settings [13]. This raises concerns about the reproducibility and repeatability of both the feature computation itself and the subsequent model building [2, 4]. Phantoms with known characteristics should prove helpful for standardization across institutions
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