Dataset Information

Magnetic Resonance Image Sequence Influences the Relationship between Bone Marrow Lesions Volume and Pain: Data from the Osteoarthritis Initiative.

ABSTRACT: Subchondral bone marrow lesions (BMLs) are related to structural and symptomatic osteoarthritis progression. However, it is unclear how sequence selection influences a quantitative BML measurement and its construct validity. We compared quantitative assessment of BMLs on intermediate-weighted fat suppressed (IW FS) turbo spin echo and 3-dimensional dual echo steady state (3D DESS) sequences. We used a customized software to measure 30 knees' (24- and 48-month MR images) BMLs on both sequences. The results showed that the IW FS sequences have much larger BML volumes (median: IW FS = 1840?mm(3); DESS = 191?mm(3)) and BML volume change (between 24 and 48 months) than DESS sequence and demonstrate more BML volume change. The 24-month BML volume on IW FS is correlated with BML volume on DESS (r s = 0.83). BML volume change on IW FS is not significantly correlated with change on DESS. The 24-month WOMAC pain is correlated with the 24-month BMLs on IW FS (r s = 0.39) but not DESS. The change in WOMAC pain is correlated with BML volume change on IW FS (r s = 0.37) but not DESS. Overall, BML quantification on IW FS offers better validity and statistical power than BML quantification on a 3D DESS sequence.

SUBMITTER: Zhang M

PROVIDER: S-EPMC4644821 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Magnetic Resonance Image Sequence Influences the Relationship between Bone Marrow Lesions Volume and Pain: Data from the Osteoarthritis Initiative.

Zhang Ming M Driban Jeffrey B JB Price Lori Lyn LL Lo Grace H GH McAlindon Timothy E TE

BioMed research international 20151102

Subchondral bone marrow lesions (BMLs) are related to structural and symptomatic osteoarthritis progression. However, it is unclear how sequence selection influences a quantitative BML measurement and its construct validity. We compared quantitative assessment of BMLs on intermediate-weighted fat suppressed (IW FS) turbo spin echo and 3-dimensional dual echo steady state (3D DESS) sequences. We used a customized software to measure 30 knees' (24- and 48-month MR images) BMLs on both sequences. T ...[more]

PMID: 26609533

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Project description:ObjectiveTo determine the association of different types of meniscal pathology with knee pain, bone marrow lesion (BML) volume, and end-stage knee osteoarthritis (esKOA).DesignParticipants 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).ResultsThe 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).ConclusionsBased 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.

Project description:Background No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting. Methods A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24–48 months] and matched controls (182 OA knees not meeting the case definition). DeepKOA was developed through 3-dimensional (3D) DenseNet169 to predict KOA progression over 24–48 months based on sagittal intermediate-weighted turbo-spin echo sequences with fat-suppression (SAG-IW-TSE-FS), sagittal 3D dual-echo steady-state water excitation (SAG-3D-DESS-WE) and its axial and coronal multiplanar reformation, and their combined MR images with patient-level labels at baseline, 12, and 24 months to eventually determine the probability of progression. The classification performance of the DeepKOA was evaluated using 5-fold cross-validation. An X-ray-based model and traditional models that used clinical variables via multilayer perceptron were built. Combined models were also constructed, which integrated clinical variables with DeepKOA. The area under the curve (AUC) was used as the evaluation metric. Results The performance of SAG-IW-TSE-FS in predicting OA progression was similar or higher to that of other single and combined sequences. The DeepKOA based on SAG-IW-TSE-FS achieved an AUC of 0.664 (95% CI: 0.585–0.743) at baseline, 0.739 (95% CI: 0.703–0.775) at 12 months, and 0.775 (95% CI: 0.686–0.865) at 24 months. The X-ray-based model achieved an AUC ranging from 0.573 to 0.613 at 3 time points. However, adding clinical variables to DeepKOA did not improve performance (P>0.05). Initial visualizations from gradient-weighted class activation mapping (Grad-CAM) indicated that the frequency with which the patellofemoral joint was highlighted increased as time progressed, which contrasted the trend observed in the tibiofemoral joint. The meniscus, the infrapatellar fat pad, and muscles posterior to the knee were highlighted to varying degrees. Conclusions This study initially demonstrated the feasibility of DeepKOA in the prediction of KOA progression and identified the potential responsible structures which may enlighten the future development of more clinically practical methods.

Project description:IntroductionBone marrow lesion (BML) size may be an important imaging biomarker for osteoarthritis-related clinical trials and reducing BML size may be an important therapeutic goal. However, data on the interrelationships between BML size, pain, and structural progression are inconsistent and rarely examined in the same cohort. Therefore, we evaluated the cross-sectional and longitudinal associations of BML volume with knee pain and joint space narrowing (JSN).MethodsA BML volume assessment was performed on magnetic resonance images of the knee collected at the 24- and 48-month Osteoarthritis Initiative visits from a convenience sample of 404 participants in the progression cohort. During the same visits, knee pain was assessed with WOMAC pain scores and knee radiographs were acquired and scored for JSN. BML volume was summed to generate a total knee volume and an index tibiofemoral compartment volume (compartment with greater baseline JSN). Primary analyses included multiple linear regressions (outcome = pain, predictor = total knee BML volume) and logistic regressions (outcome = JSN, predictor = index tibiofemoral compartment BML volume).ResultsThis sample was 49% female with a mean age of 63 (9.2 standard deviation (SD)) years, and 71% had radiographic osteoarthritis in the study knee. Larger baseline BMLs were associated with greater baseline knee pain (P = 0.01), the presence of JSN at baseline (odds ratio (OR) = 1.50, 95% confidence interval (CI) = 1.23 to 1.83), and JSN progression (OR = 1.27, 95%CI = 1.11 to 1.46). Changes in total knee BML volume had a positive association with changes in knee pain severity (P = 0.004) and this association may be driven by knees that were progressing from no or small baseline BMLs to larger BMLs. In contrast, we found no linear positive relationship between BML volume change and JSN progression. Instead, regression of medial tibiofemoral BML volume was associated with JSN progression compared to knees with no or minimal changes in BML volume (OR = 3.36, 95%CI = 1.55 to 7.28). However, follow-up analyses indicated that the association between JSN progression and BML volume change may primarily be influenced by baseline BML volume.ConclusionLarge baseline BMLs are associated with greater baseline knee pain, the presence of JSN at baseline, and disease progression. Additionally, BML regression is associated with decreased knee pain but not a reduced risk of concurrent JSN progression.

Project description:BackgroundThe infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). Magnetic resonance (MR) signal heterogeneity of the IPFP is related to pathologic changes. In this study, we aimed to investigate whether the IPFP radiomic features have predictive value for incident radiographic knee OA (iROA) 1 year prior to iROA diagnosis.MethodsData used in this work were obtained from the osteoarthritis initiative (OAI). In this study, iROA was defined as a knee with a baseline Kellgren-Lawrence grade (KLG) of 0 or 1 that further progressed to KLG ≥2 during the follow-up visit. Intermediate-weighted turbo spin-echo knee MR images at the time of iROA diagnosis and 1 year prior were obtained. Five clinical characteristics-age, sex, body mass index, knee injury history, and knee surgery history-were obtained. A total of 604 knees were selected and matched (302 cases and 302 controls). A U-Net segmentation model was independently trained to automatically segment the IPFP. The prediction models were established in the training set (60%). Three main models were generated using (I) clinical characteristics; (II) radiomic features; (III) combined (clinical plus radiomic) features. Model performance was evaluated in an independent testing set (remaining 40%) using the area under the curve (AUC). Two secondary models were also generated using Hoffa-synovitis scores and clinical characteristics.ResultsThe comparison between the automated and manual segmentations of the IPFP achieved a Dice coefficient of 0.900 (95% CI: 0.891-0.908), which was comparable to that of experienced radiologists. The radiomic features model and the combined model yielded superior AUCs of 0.700 (95% CI: 0.630-0.763) and 0.702 (95% CI: 0.635-0.763), respectively. The DeLong test found no statistically significant difference between the receiver operating curves of the radiomic and combined models (P=0.831); however, both models outperformed the clinical model (P=0.014 and 0.004, respectively).ConclusionsOur results demonstrated that radiomic features of the IPFP are predictive of iROA 1 year prior to the diagnosis, suggesting that IPFP radiomic features can serve as an early quantitative prediction biomarker of iROA.

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.

Dataset Information

Magnetic Resonance Image Sequence Influences the Relationship between Bone Marrow Lesions Volume and Pain: Data from the Osteoarthritis Initiative.

Publications

Magnetic Resonance Image Sequence Influences the Relationship between Bone Marrow Lesions Volume and Pain: Data from the Osteoarthritis Initiative.

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