Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle

Radiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and furt...

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Bibliographic Details
Published in:Scientific reports 2021-04, Vol.11 (1), p.8262-8262, Article 8262
Main Authors: Escudero Sanchez, Lorena, Rundo, Leonardo, Gill, Andrew B., Hoare, Matthew, Mendes Serrao, Eva, Sala, Evis
Format: Article
Language:English
Subjects:
631/67/1504/1610
631/67/1857
631/67/2321
631/67/2329
639/166/985
639/705/1042
692/4028/67/1504/1610
692/4028/67/1857
692/4028/67/2321
692/4028/67/2329
Biomarkers
Carcinoma, Hepatocellular - diagnostic imaging
Carcinoma, Hepatocellular - pathology
Computed tomography
Hepatocellular carcinoma
Humanities and Social Sciences
Humans
Liver - diagnostic imaging
Liver - pathology
Liver cancer
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - pathology
Magnetic resonance imaging
Medical imaging
Medical research
multidisciplinary
Muscles - diagnostic imaging
Muscles - pathology
Patients
Radiometry - methods
Radiomics
Retrospective Studies
Science
Science (multidisciplinary)
Software
Tomography
Tomography, X-Ray Computed - methods
Tumors
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Summary:Radiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75–90%) was found to be highly robust (
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-87598-w