Dataset Information

Role of Magnetic Resonance Imaging in Classifying Individuals Who Will Develop Accelerated Radiographic Knee Osteoarthritis.

ABSTRACT: We assessed whether adding magnetic resonance (MR)-based features to a base model of clinically accessible participant characteristics (i.e., serological, radiographic, demographic, symptoms, and physical function) improved classification of adults who developed accelerated radiographic knee osteoarthritis (AKOA) or not over the subsequent 4 years. We conducted a case-control study using radiographs from baseline and the first four annual visits of the osteoarthritis initiative to define groups. Eligible individuals had no radiographic KOA in either knee at baseline (Kellgren-Lawrence [KL] grade?<2). We classified two groups matched on sex (i) AKOA: at least one knee developed advanced-stage KOA (KL?=?3 or 4) within 48 months and (ii) did not develop AKOA within 48 months. The MR-based features were assessments of bone, effusion/synovitis, tendons, ligaments, cartilage, and menisci. All characteristics and MR-based features were from the baseline visit. Classification and regression tree analyses were performed to determine classification rules and identify statistically important variables. The CART models with and without MR features each explained approximately 40% of the variability. Adding MR-based features to the model yielded modest improvements in specificity (0.90 vs. 0.82) but lower sensitivity (0.62 vs. 0.70) than the base model. There was consistent evidence that serum glucose, effusion-synovitis volume, and cruciate ligament degeneration are statistically important variables in classifying individuals who will develop AKOA. We found common MR-based measures failed to dramatically improve classification. These findings also show a complex interplay among participant characteristics and a need to identify novel characteristics to improve classification. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2420-2428, 2019.

SUBMITTER: Price LL

PROVIDER: S-EPMC6778707 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Role of Magnetic Resonance Imaging in Classifying Individuals Who Will Develop Accelerated Radiographic Knee Osteoarthritis.

Price Lori Lyn LL Harkey Matthew S MS Ward Robert J RJ MacKay James W JW Zhang Ming M Pang Jincheng J Davis Julie E JE McAlindon Timothy E TE Lo Grace H GH Amin Mamta M Eaton Charles B CB Lu Bing B Duryea Jeffrey J Barbe Mary F MF Driban Jeffrey B JB

Journal of orthopaedic research : official publication of the Orthopaedic Research Society 20190729 11

We assessed whether adding magnetic resonance (MR)-based features to a base model of clinically accessible participant characteristics (i.e., serological, radiographic, demographic, symptoms, and physical function) improved classification of adults who developed accelerated radiographic knee osteoarthritis (AKOA) or not over the subsequent 4 years. We conducted a case-control study using radiographs from baseline and the first four annual visits of the osteoarthritis initiative to define groups. ...[more]

PMID: 31297900

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Project description:OBJECTIVES:To assess whether initial or 12-18-month change in magnetic resonance imaging (MRI) subchondral bone texture is predictive of radiographic knee osteoarthritis (OA) progression over 36 months. METHODS:This was a nested case-control study including 122 knees/122 participants in the Osteoarthritis Initiative (OAI) Bone Ancillary Study, who underwent MRI optimised for subchondral bone assessment at either the 30- or 36-month and 48-month OAI visits. Case knees (n = 61) had radiographic OA progression between the 36- and 72-month OAI visits, defined as ? 0.7 mm minimum medial tibiofemoral radiographic joint space (minJSW) loss. Control knees (n = 61) without radiographic OA progression were matched (1:1) to cases for age, sex, body mass index and initial medial minJSW. Texture analysis was performed on the medial femoral and tibial subchondral bone. We assessed the association of texture features with radiographic progression by creating a composite texture score using penalised logistic regression and calculating odds ratios. We evaluated the predictive performance of texture features for predicting radiographic progression using c-statistics. RESULTS:Initial (odds ratio [95% confidence interval] = 2.13 [1.41-3.40]) and 12- 18-month change (3.76 [2.04-7.82]) texture scores were significantly associated with radiographic OA progression. Combinations of texture features were significant predictors of radiographic progression using initial (c-statistic [95% confidence interval] = 0.65 [0.64-0.65], p = 0.003) and 12-18-month change (0.68 [0.68-0.68], p < 0.001) data. CONCLUSIONS:Initial and 12-18-month changes in MRI subchondral bone texture score were significantly associated with radiographic progression at 36 months, with better predictive performance for 12-18-month change in texture. These results suggest that texture analysis may be a useful biomarker of subchondral bone in OA. KEY POINTS:• Subchondral bone MRI texture analysis is a promising knee osteoarthritis imaging biomarker. • In this study, subchondral bone texture was associated with knee osteoarthritis progression. • This demonstrates predictive and concurrent validity of MRI subchondral bone texture analysis. • This method may be useful in clinical trials with interventions targeting bone.

Project description:Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.

Dataset Information

Role of Magnetic Resonance Imaging in Classifying Individuals Who Will Develop Accelerated Radiographic Knee Osteoarthritis.

Publications

Role of Magnetic Resonance Imaging in Classifying Individuals Who Will Develop Accelerated Radiographic Knee Osteoarthritis.

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