Brett, A, Bowes, MA and Conaghan, PG orcid.org/0000-0002-3478-5665 (2023) Comparison of 3D quantitative osteoarthritis imaging biomarkers from paired CT and MR images: data from the IMI-APPROACH study. BMC Musculoskeletal Disorders, 24. 76. ISSN 1471-2474
Abstract
Introduction
MRI bone surface area and femoral bone shape (B-score) measures have been employed as quantitative endpoints in DMOAD clinical trials. Computerized Tomography (CT) imaging is more commonly used for 3D visualization of bony anatomy due to its high bone-soft tissue contrast. We aimed to compare CT and MRI assessments of 3D imaging biomarkers.
Methods
We used baseline and 24-month image data from the IMI-APPROACH 2-year prospective cohort study. Femur and tibia were automatically segmented using active appearance models, a machine-learning method, to measure 3D bone shape, area and 3D joint space width (3DJSW). Linear regression was used to test for correlation between measures. Limits of agreement and bias were tested using Bland-Altman analysis.
Results
CT-MR pairs of the same knee were available from 434 participants (78% female). B-scores from CT and MR were strongly correlated (CCC = 0.967) with minimal bias of 0.1 (SDD = 0.227). Area measures were also correlated but showed a consistent bias (MR smaller). 3DJSW showed different biases (MR larger) in both lateral and medial compartments.
Discussion
The strong correlation and small B-score bias suggests that B-score may be measured reliably using either modality. It is likely that the bone surface identified using MR and CT will be at slightly different positions within the bone/cartilage boundary. The negative bone area bias suggests the MR bone boundary is inside the CT boundary producing smaller areas for MR, consistent with the positive 3DJSW bias. The lateral-medial 3DJSW difference is possibly due to a difference in knee pose during acquisition (extended for CT, flexed for MR).
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
Keywords: | Osteoarthritis; B-score; Biomarker; Machine learning; MRI; CT |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Institute of Rheumatology & Musculoskeletal Medicine (LIRMM) (Leeds) > Musculoskeletal Medicine & Imaging (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Mar 2023 19:02 |
Last Modified: | 25 Jun 2023 23:15 |
Status: | Published |
Publisher: | BMC |
Identification Number: | 10.1186/s12891-023-06187-2 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196089 |