Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability.

ABSTRACT: Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test-retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test-retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliabili

SUBMITTER: Zhang J 

PROVIDER: S-EPMC10600245 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature