posted on 2021-10-15, 01:07authored byJC Korte, C Cardenas, N Hardcastle, T Kron, J Wang, H Bahig, B Elgohari, R Ger, L Court, CD Fuller, Sweet NgSweet Ng
Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
Funding
This project is supported by funding from the Peter MacCallum Cancer Foundation, RSNA Fellow Grant, Mike Hogg Fund, and RANZCR research grants.
History
Publication Date
2021-09-03
Journal
Scientific Reports
Volume
11
Issue
1
Article Number
17633
Pagination
11p.
Publisher
Nature Portfolio
ISSN
2045-2322
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