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Sci Rep. 2019 Mar 18;9(1):4800. doi: 10.1038/s41598-019-41344-5.

Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial.

Author information

1
The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
2
Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
3
The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands. h.woodruff@maastrichtuniversity.nl.
4
Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands. h.woodruff@maastrichtuniversity.nl.
5
Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Hospital, Sutton, UK.
6
Laboratory of Imaging Biomarkers, UMR 1149 Inserm - University Paris Diderot, Paris; Department of Radiology, Beaujon University Hospital Paris Nord, Clichy, France.
7
Department of Radiology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands.
8
Wolfson Imaging Centre, Wolfson Molecular Imaging Centre, University of Manchester, 23 Palatine Rd, Withington, Greater Manchester, UK.
9
Department of Nuclear Medicine, University Hospital RWTH Aachen University, Aachen, Germany.

Abstract

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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