Evidence map›Paper›PMID 40568184›Full record

ArticleOsteoarthritis and cartilage open2025

Analysis of multiple MRI-based quantitative structural measurements of knee osteoarthritis in a case control study - association with pain and structural progression and comparison to semi-quantitative scoring.

Stacy E Smith, Lawrence Lo, Meera Sury, Sara M Bahouth, Ming Yin, Lena F Schaefer, Jamie E Collins, Jeffrey Duryea

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Osteoarthritis and cartilage open, 2025. 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. Review
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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.

Stacy E SmithDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital. Harvard Medical School, Boston, MA, USA.
Lawrence LoDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital. Harvard Medical School, Boston, MA, USA.
Meera SuryDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital. Harvard Medical School, Boston, MA, USA.
Sara M BahouthDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital. Harvard Medical School, Boston, MA, USA.
Ming YinDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital. Harvard Medical School, Boston, MA, USA.
Lena F SchaeferDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital. Harvard Medical School, Boston, MA, USA.
Jamie E CollinsDepartment of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Jeffrey DuryeaDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital. Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To use software-based magnetic resonance imaging (MRI) measures of multiple features of knee osteoarthritis (KOA) to predict radiographic and pain progression in persons with KOA, and compare to a study that used primarily semi-quantitative (SQ) scoring. Design: Data from the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium (FNIH) nested case-control study (600 subjects divided into case and control groups based on knee pain and/or radiographic progression) were used. The MRI Osteoarthritis Software Scoring (MOSS) was used to quantitatively assess medial femoral cartilage, bone marrow lesions, osteophyte volume, effusion-synovitis volume, and a measure of Hoffa's synovitis at baseline and 24-months using readers with diverse levels of expertise. Association between baseline and baseline to 24-month change with progressor status was examined and discriminative ability assessed using the c-statistic (AUC) computed under 10-fold cross validation. Results: AUC values ranged from 0.690 to 0.726 to predict combined pain/radiographic progression and from 0.709 to 0.804 to predict radiographic progression alone. Bone marrow lesions and osteophyte volume played a role in all analyses. Medial femoral cartilage was significant for all but the cross-sectional analysis involving pain progression. Comparison to results from a separate publication showed that MOSS offered similar discrimination to a published model that primarily used SQ scoring. Conclusions: We found a high level of discrimination particularly for radiographic progression analysis. Use of fast automated software and readers with varied prior experience make MOSS a useful tool for enriching future clinical trials and for other large studies of KOA.

Indexed as

KneeMagnetic resonance imagingOsteoarthritisQuantitative imagingSoftwareSynovitis

Identifiers

PMID40568184
PMCPMC12192608

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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.