ResearchRadiographic Image Radiomics Feature Reproducibility: A Preliminary Study on the Impact of Field Size
Introduction
Medical imaging plays a critical role in the management of different patients in terms of disease detection, diagnosis, treatment planning, and follow-up [1]. New advances in medical imaging have given medical specialists the ability to manage diseases using both qualitative and quantitative approaches [2]. In the field of oncology, imaging has been found in a wide range of applications and utilized more in cancer management as diagnostic, prognostic, and predictive markers [[3], [4], [5]]. Recently, quantitative radiomics analysis, as a newly accepted branch of advanced image processing, has opened a new horizon of biomarker discovery in the field of cancer and many different types of disorders [6,7].
As defined by previous studies, “radiomics is the high-throughput extraction of large amounts of image features from radiographic images creating a high-dimensional data set followed by data mining for potentially improved decision support” [8]. Radiomics is a multistep process involving the acquisition of high-quality diagnostic or planning medical images, definition of the tumor with manual or automated segmentation methods, extraction of quantitative radiomics features from the segmented regions, selection of most informative features, feature analysis for their relationship with clinical or biological data, and finally building diagnostic, prognostic, and predictive models by using these radiomics features [8].
Radiomics features are mathematical issues that are a measure of spatial relationship and distribution of intensity, heterogeneity patterns, shape, size, and relations of segmented regions (tumor) with surrounding regions (other tissues) [9]. These quantitative data, which are newly accepted biomarkers, are well-defined descriptors of tumor characteristics, biology, and behaviors against therapeutic approaches [10]. Although these imaging features have been issued as cost-beneficial, easy-to-use, and noninvasive, several challenges remain regarding the use of these markers [[11], [12], [13], [14]]. Based on imaging biomarker recommendations, radiomics features have remained unchanged and robust against any changes in each radiomics process [15]. Previous studies have indicated that many image features are vulnerable against different image acquisition, reconstruction, and segmentation in all imaging modalities including radiology, computed tomography, magnetic resonance imaging, and positron emission tomography [14,[16], [17], [18], [19]].
Although radiomics is used more for oncological data and in three dimensional (3D) features, studies have demonstrated the wide application of imaging features in 2D and for conventional radiographic imaging. Radiographic radiomics feature analysis is an applicable approach to quantify several diseases and also for computer-aided detection or diagnosis. Computer-aided detection or diagnosis has been proven to be an effective approach to assist radiologists in improving diagnostic accuracy [20,21].
To the best of our knowledge, there are few studies on the radiographic radiomics feature reproducibility. In our previous study, we showed the effect of radiographic parameters including kVp, mAs, source to film distance, filtration, and tube angle variations on feature values [12]. In the present study, we aimed to assess how radiomics features change with radiographic field sizes and positions in the field size. In this work, we assessed these changes on phantoms made with different materials and also imaged with different mAs.
Section snippets
Materials and Methods
Figure 1 shows the flowchart of the study.
Field Size Change
Our results on COV, ICC, and BA for radiomics feature reproducibility against changes in field size for all phantoms, CO and big and small woods, are depicted in Figure 3.
In regard to COV for all phantoms, six features including DifEntrp and InvDfMom from gray-level co-occurrence matrix (GLCM) feature set and Fraction, LngREmph, RLNonUni, and ShrtREmp from gray-level run length matrix (GLRLM) feature set are found as highly reproducible features. For CO and big and small woods, there was a
Discussion
Radiomics is an emerging field of medical imaging that has the potential to improve personalized medicine by using mineable imaging data, noninvasively [12,14,[22], [23], [24], [25], [26], [27]]. Based on previous research, radiomics has played a significant role in the development of new feasible biomarkers in radiation oncology. These biomarkers will be used clinically, if they have the main requirements such as repeatability and reproducibility [17].
In the present study, the impact of
Conclusion
In conclusion, we demonstrated that radiomics features are strongly vulnerable against radiographic field size, positions in the radiation field, mAs, and phantom materials. The results of this study could be taken into account when a quantitative imaging is needed for patient outcome analysis and disease detection and management.
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