Abstract
We studied the reliability of radiomic features on abdominal computed tomography (CT) images reconstructed with multiple CT image acquisition settings using the ACR (American College of Radiology) CT Phantom. Twenty-four sets of CT images of the ACR CT phantom were attained from a GE Discovery 750HD scanner using 24 different image acquisition settings, combinations of 4 tube currents (25, 50, 100, 200 Effective mAs), 3 slice thicknesses (1.25, 2.5, 5 mm), and 2 convolution kernels (STANDARD and SOFT). Polyethylene (−95 HU) and acrylic (120 HU) of the phantom model were selected for calculating real feature value; a noise-free, computer-generated phantom image series that reproduced the 2 objects and the background was used for calculating reference feature value. Feature reliability was defined as the degree of predicting reference feature value from real feature value. Radiomic features mean, std, skewness, kurtosis, gray-level co-occurrence matrix (GLCM)-energy, GLCM-contrast, GLCM-correlation, GLCM-homogeneity were investigated. The value of R2 ≥ 0.85 was considered to be of high reliability. The reliability of mean and std were high across all image acquisition settings. At 200 Effective mAs, all features except GLCM-homogeneity showed high reliability, whereas at 25 Effective mAs, most features (except mean and std) showed low reliability. From high to low, reliability was ranked in the following order: mean, std, skewness, kurtosis, GLCM-energy, correlation, contrast and homogeneity. CT image acquisition settings affected the reliability of radiomic features. High reliable features were attained from images reconstructed at high tube current and thick slice thickness.
Highlights
Medical imaging plays an ever greater role in disease diagnosis and patient care
The true feature value in our study was defined as the feature value that was calculated on computed tomography (CT) image within which the CT number of each tissue composition was equal to its theoretical CT number at 120 kVp, for example, air equals to Ϫ1000 Hounsfield unit (HU), and water equals to 0 HU
We were able to observe that feature reliability decreased with a decrease in tube current, features were more reliable on 5-mm CT images than on 1.25- and 2.5-mm CT images, there was little difference in feature reliability between CT images of STANDARD and SOFT convolution kernels, and histogram-based radiomic features (RFs) are more reliable than textural RFs
Summary
Medical imaging plays an ever greater role in disease diagnosis and patient care. One of the most exciting new areas related to cancer diagnosis, treatment planning, and response assessment is the field of radiomics, which involves the extraction and analysis of a large number of quantitative imaging features from medical images for characterization of tumor and tissue phenotypes [1, 2].Owing to the associations between tumor phenotypes and underlying biological processes, radiomic features (RFs) or RFderived phenotypes can act as biomarkers that convey information about disease to help with the management of therapies. One of the most exciting new areas related to cancer diagnosis, treatment planning, and response assessment is the field of radiomics, which involves the extraction and analysis of a large number of quantitative imaging features from medical images for characterization of tumor and tissue phenotypes [1, 2]. Numerous studies have been conducted on the “reproducibility” of RFs [17,18,19,20], which refers to whether feature values could remain the same when reimaged using different equipment and different image acquisition settings. “Reliability” refers to whether true feature value could be maintained when imaged using different scanners and image acquisition settings. True feature value was called as reference value in our study
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