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Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer.
Rai, Robba; Barton, Michael B; Chlap, Phillip; Liney, Gary; Brink, Carsten; Vinod, Shalini; Heinke, Monique; Trada, Yuvnik; Holloway, Lois C.
Afiliação
  • Rai R; University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia.
  • Barton MB; Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia.
  • Chlap P; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia.
  • Liney G; University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia.
  • Brink C; Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia.
  • Vinod S; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia.
  • Heinke M; University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia.
  • Trada Y; Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia.
  • Holloway LC; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia.
J Med Imaging (Bellingham) ; 9(4): 044005, 2022 Jul.
Article em En | MEDLINE | ID: mdl-35992729
ABSTRACT

Purpose:

Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer.

Approach:

An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers.

Results:

We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans.

Conclusions:

The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article