Radiomics feature reproducibility under inter-rater variability in segmentations of CT images.

ABSTRACT: Identifying image features that are robust with respect to segmentation variability is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyse radiomics feature reproducibility in two phases: first with manual segmentations provided by four expert readers and second with probabilistic automated segmentations using a recently developed neural network (PHiseg). We test feature reproducibility on three publicly available datasets of lung, kidney and liver lesions. We find consistent results both over manual and automated segmentations in all three datasets and show that there are subsets of radiomic features which are robust against segmentation variability and other radiomic features which are prone to poor reproducibility

SUBMITTER: Haarburger C 

PROVIDER: S-EPMC7391354 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature