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

Jurgen Peerlings, Henry C. Woodruff*, Jessica M. Winfield, Abdalla Ibrahim, Bernard E. Van Beers, Arend Heerschap, Alan Jackson, Joachim E. Wildberger, Felix M. Mottaghy, Nandita M. DeSouza, Philippe Lambin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

57 Citations (Web of Science)

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.

Original languageEnglish
Article number4800
Pages (from-to)1-10
Number of pages10
JournalScientific Reports
Volume9
DOIs
Publication statusPublished - 18 Mar 2019

Keywords

  • CELL LUNG-CANCER
  • WEIGHTED MRI
  • TEXTURAL FEATURES
  • DW-MRI
  • IMAGES
  • BODY
  • REPRODUCIBILITY
  • VARIABILITY

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