Dataset Information

The relationship between meniscal pathology and osteoarthritis depends on the type of meniscal damage visible on magnetic resonance images: data from the Osteoarthritis Initiative.

ABSTRACT: To determine the association of different types of meniscal pathology with knee pain, bone marrow lesion (BML) volume, and end-stage knee osteoarthritis (esKOA).Participants were selected from an ancillary project to the Osteoarthritis Initiative (OAI) who had at least one knee with symptomatic osteoarthritis. Baseline magnetic resonance images (MRI) were evaluated for meniscal pathology using a modified International Society of Arthroscopy, Knee Surgery, and Orthopaedic Sports Medicine (ISAKOS) classification system. We collapsed 10 types of meniscal pathology into five categories: normal, intrameniscal signal, morphological deformity/extrusion (altered meniscal shape and/or extrusion but no apparent substance loss), tear, and maceration. Outcomes included Western Ontario and McMaster Universities osteoarthritis index (WOMAC) knee pain and BML volume at baseline and after 2 years. We defined the prevalence of esKOA based on a validated algorithm. We performed logistic regression and adjusted for age, sex, and body mass index (BMI).The 463 participants (53% male) included in the analysis had mean age 63 (9.2) years, BMI 29.6 (4.6) kg/m2, and 71% had Kellgren-Lawrence grade ?2. Morphological deformity/extrusion and maceration, but no other types of meniscal pathology, were associated with BML volume (morphological deformity/extrusion odds ratio [OR] = 2.47, 95% CI: 1.49, 4.09, maceration OR = 5.85, 95% CI: 3.40, 10.06) and change in BML volume (morphological deformity/extrusion OR = 2.17, 95% CI: 1.37, 3.45, maceration OR = 3.12, 95% CI: 1.87, 5.19). Only maceration was associated with baseline WOMAC knee pain (OR = 2.82, 95% CI: 1.79, 4.43) and prevalence of esKOA (OR = 7.53, 95% CI: 4.25, 13.31).Based on MRI, morphologic deformity/extrusion and maceration rather than intrameniscal signal or tear were associated with osteoarthritis severity and progression, which highlights the importance of differentiating distinct types of meniscal pathology.

SUBMITTER: Antony B

PROVIDER: S-EPMC5310282 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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The relationship between meniscal pathology and osteoarthritis depends on the type of meniscal damage visible on magnetic resonance images: data from the Osteoarthritis Initiative.

Antony B B Driban J B JB Price L L LL Lo G H GH Ward R J RJ Nevitt M M Lynch J J Eaton C B CB Ding C C McAlindon T E TE

Osteoarthritis and cartilage 20160815 1

<h4>Objective</h4>To determine the association of different types of meniscal pathology with knee pain, bone marrow lesion (BML) volume, and end-stage knee osteoarthritis (esKOA).<h4>Design</h4>Participants were selected from an ancillary project to the Osteoarthritis Initiative (OAI) who had at least one knee with symptomatic osteoarthritis. Baseline magnetic resonance images (MRI) were evaluated for meniscal pathology using a modified International Society of Arthroscopy, Knee Surgery, and Ort ...[more]

PMID: 27539889

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Project description:PurposeQuantitative assessment of white matter hyperintensities (WMH) on structural Magnetic Resonance Imaging (MRI) is challenging. It is important to harmonise results from different software tools considering not only the volume but also the signal intensity. Here we propose and evaluate a metric of white matter (WM) damage that addresses this need.MethodsWe obtained WMH and normal-appearing white matter (NAWM) volumes from brain structural MRI from community dwelling older individuals and stroke patients enrolled in three different studies, using two automatic methods followed by manual editing by two to four observers blind to each other. We calculated the average intensity values on brain structural fluid-attenuation inversion recovery (FLAIR) MRI for the NAWM and WMH. The white matter damage metric is calculated as the proportion of WMH in brain tissue weighted by the relative image contrast of the WMH-to-NAWM. The new metric was evaluated using tissue microstructure parameters and visual ratings of small vessel disease burden and WMH: Fazekas score for WMH burden and Prins scale for WMH change.ResultsThe correlation between the WM damage metric and the visual rating scores (Spearman ρ > =0.74, p < 0.0001) was slightly stronger than between the latter and WMH volumes (Spearman ρ > =0.72, p < 0.0001). The repeatability of the WM damage metric was better than WM volume (average median difference between measurements 3.26% (IQR 2.76%) and 5.88% (IQR 5.32%) respectively). The follow-up WM damage was highly related to total Prins score even when adjusted for baseline WM damage (ANCOVA, p < 0.0001), which was not always the case for WMH volume, as total Prins was highly associated with the change in the intense WMH volume (p = 0.0079, increase of 4.42 ml per unit change in total Prins, 95%CI [1.17 7.67]), but not with the change in less-intense, subtle WMH, which determined the volumetric change.ConclusionThe new metric is practical and simple to calculate. It is robust to variations in image processing methods and scanning protocols, and sensitive to subtle and severe white matter damage.

Project description:BackgroundCartilage morphometry based on magnetic resonance images (MRIs) is an emerging outcome measure for clinical trials among patients with knee osteoarthritis (KOA). However, current methods for cartilage morphometry take many hours per knee and require extensive training on the use of the associated software. In this study we tested the feasibility, reliability, and construct validity of a novel osteoarthritis cartilage damage quantification method (Cartilage Damage Index [CDI]) that utilizes informative locations on knee MRIs.MethodsWe selected 102 knee MRIs from the Osteoarthritis Initiative that represented a range of KOA structural severity (Kellgren Lawrence [KL] Grade 0 - 4). We tested the intra- and inter-tester reliability of the CDI and compared the CDI scores against different measures of severity (radiographic joint space narrowing [JSN] grade, KL score, joint space width [JSW]) and static knee alignment, both cross-sectionally and longitudinally.ResultsDetermination of the CDI took on average14.4 minutes (s.d. 2.1) per knee pair (baseline and follow-up of one knee). Repeatability was good (intra- and inter-tester reliability: intraclass correlation coefficient >0.86). The mean CDI scores related to all four measures of osteoarthritis severity (JSN grade, KL score, JSW, and knee alignment; all p values < 0.05). Baseline JSN grade and knee alignment also predicted subsequent 24-month longitudinal change in the CDI (p trends <0.05). During 24 months, knees with worsening in JSN or KL grade (i.e. progressors) had greater change in CDI score.ConclusionsThe CDI is a novel knee cartilage quantification method that is rapid, reliable, and has construct validity for assessment of medial tibiofemoral osteoarthritis structural severity and its progression. It has the potential to addresses the barriers inherent to studies requiring assessment of cartilage damage on large numbers of knees, and as a biomarker for knee osteoarthritis progression.

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Project description:Quantitative analysis of craniofacial morphology is of interest to scholars working in a wide variety of disciplines, such as anthropology, developmental biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI) provide excellent contrast between soft tissues. Given its three-dimensional nature, MRI represents an ideal imaging modality for the analysis of craniofacial structure in living individuals. Here we describe how T1-weighted MR images, acquired to examine brain anatomy, can also be used to analyze facial features. Using a sample of typically developing adolescents from the Saguenay Youth Study (N?=?597; 292 male, 305 female, ages: 12 to 18 years), we quantified inter-individual variations in craniofacial structure in two ways. First, we adapted existing nonlinear registration-based morphological techniques to generate iteratively a group-wise population average of craniofacial features. The nonlinear transformations were used to map the craniofacial structure of each individual to the population average. Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features. Second, we employed a landmark-based approach to quantify variations in face surfaces. This approach involves: (a) placing 56 landmarks (forehead, nose, lips, jaw-line, cheekbones, and eyes) on a surface representation of the MRI-based group average; (b) warping the landmarks to the individual faces using the inverse nonlinear transformation estimated for each person; and (3) using a principal components analysis (PCA) of the warped landmarks to identify facial features (i.e. clusters of landmarks) that vary in our sample in a correlated fashion. As with the voxel-wise analysis of the deformation fields, we examined the effects of sex and age on the PCA-derived spatial relationships between facial features. Both methods demonstrated significant sexual dimorphism in craniofacial structure in areas such as the chin, mandible, lips, and nose.

Dataset Information

The relationship between meniscal pathology and osteoarthritis depends on the type of meniscal damage visible on magnetic resonance images: data from the Osteoarthritis Initiative.

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The relationship between meniscal pathology and osteoarthritis depends on the type of meniscal damage visible on magnetic resonance images: data from the Osteoarthritis Initiative.

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