Evaluation of Radiomics Features Stability for Prediction Modeling using MR Image

Abstract

Radiomics has been established to support treatmentdecision making in precision medicine. The radiomic features (RFs) used for prediction must be stable with respect to thevariety of imaging conditions. The purpose was to evaluate the stability of RFsextracted from T1-weighted MR images (T1WIs) using different imaging conditions. Two types of stabilityevaluation (SE) phantoms that canbe used for contrast (C-SE) andresolution (R-SE) assessment werecreated. The T1WIs of each phantom were acquired. Regarding the imagingparameters, the number of excitations (NEX), matrix size (MS),and repetition time (TR) werevaried. A total of 837 RFs were extracted from each T1WI acquired withdifferent parameters. Stability was evaluated using the coefficient ofvariation (CV). The criterion ofstability was employed as the CV < 0.05. The percentage of stable featuresin the C- and R-SE phantoms against individually changes in imaging conditionswere 30.8% and 31.1% for the TR, 34.8% and 39.1% for the NEX, and 35.6% and 38.5% for the MS respectively. Moreover, the percentage of stable features forchanging all conditions were 21.0% for the C-SE phantom and 22.1% for the R-SEphantom. Stable features were found in MR images against changes in imagingconditions.