Radiomic Feature Stability Analysis based on Probabilistic Segmentations
This work addresses the problem of improving radiomics reliability for medical imaging researchers, but it is incremental as it builds on existing methods with a new analysis approach.
The study tackled the challenge of identifying robust radiomic features against segmentation variability by analyzing feature stability using probabilistic segmentations from a lung cancer dataset, finding that some feature groups are more robust than others and demonstrating that segmentation variance affects survival model performance.
Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyze radiomics feature stability based on probabilistic segmentations. Based on a public lung cancer dataset, we generate an arbitrary number of plausible segmentations using a Probabilistic U-Net. From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations. Our results suggest that there are groups of radiomic features that are more (e.g. statistics features) and less (e.g. gray-level size zone matrix features) robust against segmentation variability. Finally, we demonstrate that segmentation variance impacts the performance of a prognostic lung cancer survival model and propose a new and potentially more robust radiomics feature selection workflow.