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

Scientific Reports

Christiane Kuhl¹  Leon Weninger²  Gustav Müller-Franzes²  Christoph Haarburger²  Daniel Truhn³  Dorit Merhof⁴ 

[1]Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany

[2]Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

[3]Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

[4]Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany

[5]Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

[6]Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany

DOI  :  10.1038/s41598-020-69534-6

来源: Springer

【 摘 要 】

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 under differing segmentations. By providing a detailed analysis of robustness of the most common radiomics features across several datasets, we envision that more reliable and reproducible radiomic models can be built in the future based on this work.

【 授权许可】

CC BY