Journal
SCIENTIFIC REPORTS
Volume 9, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-019-41344-5
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Funding
- ERC advanced grant (ERC-ADG-2015) [694812]
- QuIC-ConCePT project - EFPI A companies
- Innovative Medicine Initiative Joint Undertaking (IMI JU) [115151]
- Dutch technology Foundation STW [10696 DuCAT, P14-19]
- Technology Programme of the Ministry of Economic Affairs
- EU [257144, 601826]
- SME Phase 2 (RAIL) [673780]
- EUROSTARS (SeDI)
- EUROSTARS (CloudAtlas)
- EUROSTARS (DART)
- EUROSTARS (DECIDE)
- EUROSTARS (COMPACT)
- European Program H2020-2015-17 [PHC30-689715, 733008, 766276]
- Interreg V-A Euregio Meuse-Rhine (Euradiomics)
- Cancer Research UK BIDD grant [C1353/A12762, C8742/A18097]
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Quantitative radiomics features, extracted from medical images, characterize tumour-phenotypes and have been shown to provide prognostic value in predicting clinical outcomes. Stability of radiomics features extracted from apparent diffusion coefficient (ADC)-maps is essential for reliable correlation with the underlying pathology and its clinical applications. Within a multicentre, multi-vendor trial we established a method to analyse radiomics features from ADC-maps of ovarian (n = 12), lung (n = 19), and colorectal liver metastasis (n = 30) cancer patients who underwent repeated (< 7 days) diffusion-weighted imaging at 1.5 T and 3 T. From these ADC-maps, 1322 features describing tumour shape, texture and intensity were retrospectively extracted and stable features were selected using the concordance correlation coefficient (CCC > 0.85). Although some features were tissue-and/or respiratory motion-specific, 122 features were stable for all tumour-entities. A large proportion of features were stable across different vendors and field strengths. By extracting stable phenotypic features, fitting-dimensionality is reduced and reliable prognostic models can be created, paving the way for clinical implementation of ADC-based radiomics.
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