Evidence map›Paper›PMID 35152476›Full record

ArticleJournal of orthopaedic research : official publication of the Orthopaedic Research Society2022

Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative.

Ari Väärälä, Victor Casula, Arttu Peuna, Egor Panfilov, Ali Mobasheri, Marianne Haapea, Eveliina Lammentausta, Miika T Nieminen

Abstract read
In one paragraph

Article in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ari VääräläResearch Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.ORCID 0000-0002-9163-5356
Victor CasulaResearch Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.ORCID 0000-0003-0447-2796
Arttu PeunaResearch Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.ORCID 0000-0002-2623-7458
Egor PanfilovResearch Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.ORCID 0000-0002-2500-6375
Ali MobasheriResearch Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.ORCID 0000-0001-6261-1286
Marianne HaapeaMedical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.ORCID 0000-0002-3989-9354
Eveliina LammentaustaResearch Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
Miika T NieminenResearch Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.ORCID 0000-0002-2300-2848

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.

Indexed as

Cartilage, ArticularOsteoarthritis, KneeDisease ProgressionHumansKnee JointMagnetic Resonance ImagingTibiacartilageDESSmagnetic resonance imaging (MRI)osteoarthritis (OA)texture analysis

Identifiers

PMID35152476
PMCPMC9790756

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.