<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>9(1)</volume><submitter>Zwanenburg A</submitter><pubmed_abstract>Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can be used to analyse new images, whereas models with non-robust features may fail to predict the outcome of interest accurately. Test-retest imaging is recommended to assess robustness, but may not be available for the phenotype of interest. We therefore investigated 18 combinations of image perturbations to determine feature robustness, based on noise addition (N), translation (T), rotation (R), volume growth/shrinkage (V) and supervoxel-based contour randomisation (C). Test-retest and perturbation robustness were compared for combined total of 4032 morphological, statistical and texture features that were computed</pubmed_abstract><journal>Scientific reports</journal><pagination>614</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC6345842</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Assessing robustness of radiomic features by image perturbation.</pubmed_title><pmcid>PMC6345842</pmcid><pubmed_authors>Leger S</pubmed_authors><pubmed_authors>Agolli L</pubmed_authors><pubmed_authors>Richter C</pubmed_authors><pubmed_authors>Lock S</pubmed_authors><pubmed_authors>Troost EGC</pubmed_authors><pubmed_authors>Pilz K</pubmed_authors><pubmed_authors>Zwanenburg A</pubmed_authors></additional><is_claimable>false</is_claimable><name>Assessing robustness of radiomic features by image perturbation.</name><description>Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can be used to analyse new images, whereas models with non-robust features may fail to predict the outcome of interest accurately. Test-retest imaging is recommended to assess robustness, but may not be available for the phenotype of interest. We therefore investigated 18 combinations of image perturbations to determine feature robustness, based on noise addition (N), translation (T), rotation (R), volume growth/shrinkage (V) and supervoxel-based contour randomisation (C). Test-retest and perturbation robustness were compared for combined total of 4032 morphological, statistical and texture features that were computed</description><dates><release>2019-01-01T00:00:00Z</release><publication>2019 Jan</publication><modification>2025-04-26T08:54:50.539Z</modification><creation>2019-03-26T22:41:53Z</creation></dates><accession>S-EPMC6345842</accession><cross_references><pubmed>30679599</pubmed><doi>10.1038/s41598-018-36938-4</doi></cross_references></HashMap>