Evidence map›Paper›PMID 38747030›Full record

ArticleJournal of orthopaedic research : official publication of the Orthopaedic Research Society2024

Radiomic features of the medial meniscus predicts incident destabilizing meniscal tears: Data from the osteoarthritis initiative.

Michelle Villagran, Jeffrey B Driban, Bing Lu, James W MacKay, Timothy E McAlindon, Matthew S Harkey

Abstract read
In one paragraph

Article in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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  5. Review
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

6 authors.

Michelle VillagranDepartment of Chemistry, Wellesley College, Wellesley, Massachusetts, USA.ORCID 0000-0002-2374-4913
Jeffrey B DribanDepartment of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, Massachusetts, USA.
Bing LuDepartment of Public Health Sciences, University of Connecticut Health Center, Farmington, Connecticut, USA.
James W MacKayDepartment of Radiology, University of Cambridge, Cambridge, UK.
Timothy E McAlindonDivision of Rheumatology, Allergy and Immunology, Tufts Medical Center, Boston, Massachusetts, USA.
Matthew S HarkeyDepartment of Kinesiology, Michigan State University, East Lansing, Michigan, USA.ORCID 0000-0002-3480-3173

Funding

DATA COORDINATING CENTER FOR THE OSTEOARTHRITIS INITIATIVEN01AR22258 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NEVITT, MICHAEL · 2007 to 2011
$13.5M
CLINICAL CENTERS FOR THE OSTEOARTHRITIS INITIATIVEN01AR22259 · NIDCD · UNIVERSITY OF MARYLAND BALTIMORE · PI HOCHBERG, MARC C. · 2007 to 2011
$7.8M
Clinical Centers for the Osteoarthritis Initiative^Rhode IslandN01AR22262 · NIAMS · MEMORIAL HOSPITAL OF RHODE ISLAND · PI EATON, CHARLES B. · 2007 to 2010
$6.8M
CLINICAL CENTERS FOR THE OSTEOARTHRITIS INITIATIVEN01AR22261 · NIAMS · OHIO STATE UNIVERSITY RESEARCH FDN · PI JACKSON, REBECCA · 2007 to 2011
$6.2M
CLINICAL CENTERS FOR THE OSTEOARTHRITIS INITIATIVEN01AR22260 · NIDCD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2007 to 2010
$6.1M
Metabolites of Dietary Intake and Risk of Knee OsteoarthritisR01AR074447 · NIAMS · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI LU, BING · 2019 to 2022
$2.6M
Characterization of Rapidly Progressive Knee OsteoarthritisR01AR065977 · NIAMS · TUFTS MEDICAL CENTER · PI DRIBAN, JEFFREY B · 2015 to 2018
$1.5M
Walk this Way: Physical Activity and Walking Biomechanics Lead to Early Knee OA Symptoms and Ultrasound-Detected Structural Pathology after ACL ReconstructionK01AR081389 · NIAMS · MICHIGAN STATE UNIVERSITY · PI Matthew Harkey · 2022 to 2026
$689k
NHLBI NIH HHS N01AR22258NIAMS NIH HHS K01 AR081389NIAMS NIH HHS N01AR22259NIAMS NIH HHS N01AR22260NIAMS NIH HHS N01AR22261NIAMS NIH HHS N01AR22262NIAMS NIH HHS R01 AR065977NIAMS NIH HHS R01 AR074447
6 · The paper itself

Abstract

The objective of this study was to determine the optimal meniscal radiomic features to classify people who will develop an incident destabilizing medial meniscal tear. We used magnetic resonance (MR) images from an existing case-control study that includes images from the first 4 years of the Osteoarthritis Initiative (OAI). For this exploratory analysis (n = 215), we limited our study sample to people with (1) intact menisci at the OAI baseline visit, (2) 4-year meniscal status data, and (3) complete meniscal data from each region of interest. Incident destabilizing meniscal tear was defined as progressing from an intact meniscus to a destabilizing tear by the 48-month visit using intermediate-weighted fat-suppressed MR images. One reader manually segmented each participant's anterior and posterior horn of the medial menisci at the OAI baseline visit. Next, 61 different radiomic features were extracted from each medial meniscus horn. We performed a classification and regression tree (CART) analysis to determine the classification rules and important variables that predict incident destabilizing meniscal tear. The CART correctly classified 24 of the 34 cases and 172 out of 181 controls with a sensitivity of 70.6% and a specificity of 95.0%. The CART identified large zone high gray level emphasis (i.e., more coarse texture) from the posterior horn as the most important variable to classify who would develop an incident destabilizing medial meniscal tear. The use of radiomic features provides sensitive and quantitative measures of meniscal alterations, allowing us to intervene and prevent destabilizing meniscal tears.

Indexed as

Magnetic Resonance ImagingMenisci, TibialTibial Meniscus InjuriesAgedCase-Control StudiesFemaleHumansMaleMiddle AgedOsteoarthritis, KneeRadiomicsdiagnostic imagingkneemeniscusosteoarthritis

Identifiers

PMID38747030
PMCPMC11336561

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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.