Abstract
Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examples. Potential clinical applications were tested on CT and MR images for the differentiation of pulmonary lesions. In these sample applications, maps of Skewness enhanced the differentiation of non-malignant lesions and non-small lung carcinoma manifestations on CT images and maps of Variance enhanced the differentiation of pulmonary lymphoma manifestations and fungal infiltrates on MR images. This new and simple method for creating parametric maps makes radiomics features visually perceivable, allows direct feature quantification by placing a region of interest, can improve the assessment of radiological images and, furthermore, can increase the use of radiomics in clinical routine.
Highlights
Radiomics are an emerging means in image analysis [1,2,3,4] that allow quantitative image assessment beyond morphologic and macroscopic characteristics [5]
A second NRRD-file of the same dimensions is created to contain a grid of volumes of interest (VOI), i.e., a grid that divides the image into small blocks
As the intention was to design a tool for the easy creation of parametric maps, we provide options to adjust basic settings
Summary
Radiomics are an emerging means in image analysis [1,2,3,4] that allow quantitative image assessment beyond morphologic and macroscopic characteristics [5]. Statistics of the grey level composition in a region of interest (ROI) are calculated, resulting in many different quantitative texture features that can be statistically analyzed and linked to an outcome [5]. Numerous studies have shown the potential of radiomics in the differentiation of various pathological entities [6,7,8,9]. A very specific application, for example, is the differentiation of pulmonary lymphoma manifestations and non-lymphoma infiltrates in suspected fungal pneumonia in hematooncologic patients [10]. In this collective, the first-order feature Variance has shown to be a useful parameter [11]. The process usually results in exclusively abstract numerical values
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