Rectal MRI radiomics inter- and intra-reader reliability: should we worry about that?

Abdom Radiol (NY). 2022 Jun;47(6):2004-2013. doi: 10.1007/s00261-022-03503-7. Epub 2022 Apr 2.

Abstract

Purpose: The aim of this review paper is to summarize the current literature regarding inter- and intra-reader reliability of radiomics on rectal MRI.

Methods: Original studies examining treatment response prediction in patients with rectal cancer following neoadjuvant therapy using rectal MRI-based radiomics between January 2010 and December 2021 were identified via a PubMed/Medline search. Studies in which intra- and/or inter-reader reliability had been reported were included in this review.

Results: Thirteen studies were selected, with an average number of patients of 145 (range, 20-649). All included studies evaluated T2-weighted imaging (T2WI) and/or diffusion-weighted imaging (DWI) sequences, while 3/13 (23%) also evaluated the contrast-enhanced T1-weighted imaging (T1WI) sequence. Most of the selected studies involved two readers (10/13, 77%), 6/13 (46%) studies used baseline MRI only, 1/13 (8%) study used restaging MRI only, and 6/13 (46%) used both. Segmentation was performed manually in 10/13 (77%) studies, and in a slight majority of studies (7/13, 54%), the entire tumor volume (3D VOI) was segmented, while 4/13 (31%) studies segmented the 2D ROI and 2/13 (15%) segmented both. Intraclass correlation coefficient (ICC) on intra-reader agreement varied from 0.73 to 0.93. ICC to assess inter-reader varied from 0.60 to 0.99. Overall, features obtained from baseline rectal MRI, using 3D VOI and first-order features, had higher agreement.

Conclusion: Based on our qualitative assessment of a small number of non-dedicated studies, there seems to be good reliability, particularly among low-order features extracted from the entire tumor volume using baseline MRI; however, direct evidence remains scarce. More targeted research in this area is required to quantitatively verify reliability, and before these novel radiomic techniques can be clinically adopted.

Keywords: Agreement; Machine learning; Magnetic resonance imaging; Rectal cancer; Reliability.

Publication types

  • Review
  • Research Support, N.I.H., Extramural

MeSH terms

  • Diffusion Magnetic Resonance Imaging / methods
  • Humans
  • Magnetic Resonance Imaging* / methods
  • Rectal Neoplasms* / pathology
  • Reproducibility of Results
  • Retrospective Studies