Investigation of the Inter- and Intrascanner Reproducibility and Repeatability of Radiomics Features in T1-Weighted Brain MRI

Rosalind Nina Mitchell-Hay, Trevor S. Ahearn, Alison D. Murray, Gordon D. Waiter

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
18 Downloads (Pure)

Abstract

Background
Radiomics is the high throughput analysis of medical images using computer algorithms, which specifically assess textural features. It has increasingly been proposed as a tool for the development of imaging biomarkers. However, an important acknowledged limitation of radiomics is the lack of reproducibility of features produced.

Purpose
To assess reproducibility and repeatability of radiomics variables in brain MRI through a multivisit, multicenter study.

Study Type
Retrospective.

Population
Fourteen individuals visiting three institutions twice, 10 males with the mean age of 36.3 years and age range 25–51.

Field Strength
3D T1W inversion recovery on three 1.5-T General Electric scanners.

Assessment
Radiomics analysis by a consultant radiologist performed on the T1W images of the whole brain on all visits. All possible radiomics features were generated.

Statistical Test
Concordance correlation coefficient (CCC) and dynamic range (DR) for all variables were calculated to assess the test–retest repeatability. Intraclass correlation coefficients (ICCs) were calculated to investigate the reproducibility of features across centers.

Results
Of 1596 features generated, 57 from center 1, 15 from center 2, and 22 from center 3 had a CCC > 0.9 and DR > 0.9. Eight variables had CCC > 0.9 and DR > 0.9 in all centers. Forty-one variables had an ICC of >0.9. No variables had CCC > 0.9, DR > 0.9, and ICC > 0.9.

Data Conclusion
Repeatability and reproducibility of variables is a significant limitation of radiomics analysis in 3DT1W brain MRI. Careful selection of radiomic features is required.
Original languageEnglish
Pages (from-to)1559-1568
Number of pages10
JournalJournal of Magnetic Resonance Imaging
Volume56
Issue number5
Early online date9 Apr 2022
DOIs
Publication statusPublished - Nov 2022

Bibliographical note

Open Access via the Wiley Agreement
Acknowledgments
This work was supported by the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which was funded by Innovate UK on behalf of UK Research and Innovation (UKRI) (project number: 104690) and the Roland Sutton Academic Trust (RSAT).

Keywords

  • radiomics
  • repeatability
  • reproducibility
  • neuro-imaging
  • CANCER

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