Pub Date : 2024-01-01DOI: 10.1016/j.ostima.2024.100192
C.T. Nielsen , M. Boesen , H.R. Gudbergsen , P. Hansen , J.U. Nybing , M. Henriksen , H. Bliddal , K.E.S. Poole , T.D. Turmezei
INTRODUCTION
Change in shape of the distal femur demonstrated with MRI is an established biomarker for structural OA progression in clinical trials. This “B-score” has been ported across to CT with minimal bias, which brings the opportunity to include 3-D evaluation of periarticular bone distribution in shape analysis by combining statistical shape modelling (SSM) and cortical bone mapping (CBM).
OBJECTIVE
To look for significant relationships between 3-D knee shape and bone distribution with CT.
METHODS
This exploratory analysis was performed ancillary to the LOSEIT trial evaluating the efficacy of liraglutide in inducing and maintaining weight loss and pain relief in overweight patients with knee OA. After exclusions, 133 participants were included, 65 from the placebo group, 68 from the liraglutide group. All had baseline CT (140kV) and weight-bearing radiographs of both knees. Both knees were segmented from the CT data for CBM using Stradview followed by registration of canonical objects to femurs and tibias using wxRegSurf. SSM was performed on combined femur and tibia registrations using MATLAB 2024a. Index knee data were taken from each participant. Generalized estimating equation (GEE) analysis looked for associations of the first 10 shape modes with KLG controlling for age, sex and mass using Bonferroni correction. 3-D cortical thickness (CTh) and subcortical trabecular attenuation (TA) maps were transferred to the canonical objects. SPM analysis was performed using the MATLAB Surfstat toolbox to establish dependence of CTh and TA distribution on shape controlling for age, sex, mass and KLG.
RESULTS
Study participants were 89 females and 44 males with mean +/- SD age of 59.6 +/- 9.2 yrs, mass 93.3 +/- 16.7 kg and an index knee breakdown of KLG1 = 19, KLG2 = 57, KLG3 = 57. GEE showed shape mode 2 (SM2) was the only mode significantly associated with KLG with an odds ratio of 1.43 (1.28-1.59 95% CI, P<<0.05) for each SD of the mode (Fig. 1, * = P<0.05). Subjective visualization showed substantial similarities of SM2 to the B-score, namely increased femoral articular surface area with marginal articular prominence and narrowing of the intercondylar distance (Fig. 2, +/- 3xSD of the mode). SPM showed subchondral TA was significantly dependent on SM2 across nearly all the femoral articular surface (P<0.05), showing up to 40 HU drop for each increase in SD (Fig 2). Small zones of marginal articular bone at the lateral tibiofemoral compartment showed significant CTh dependence on the shape mode (P<0.05) with an increase of up to 0.2 mm for each SD increase (Fig. 2), but the association was limited to this compartment. In the tibia, this combined shape mode represented peaked widening of the tibial plateau rim, with significant dependence of TA in the posterior lateral tibial plateau (-20 HU per SD increase) and CTh around the medial plateau
{"title":"KNEE B-SCORE SHAPE FROM COMPUTED TOMOGRAPHY IS ASSOCIATED WITH SUBCHONDRAL BONE ATTENUATION AND MARGINAL CORTICAL BONE THICKNESS","authors":"C.T. Nielsen , M. Boesen , H.R. Gudbergsen , P. Hansen , J.U. Nybing , M. Henriksen , H. Bliddal , K.E.S. Poole , T.D. Turmezei","doi":"10.1016/j.ostima.2024.100192","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100192","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Change in shape of the distal femur demonstrated with MRI is an established biomarker for structural OA progression in clinical trials. This “B-score” has been ported across to CT with minimal bias, which brings the opportunity to include 3-D evaluation of periarticular bone distribution in shape analysis by combining statistical shape modelling (SSM) and cortical bone mapping (CBM).</p></div><div><h3>OBJECTIVE</h3><p>To look for significant relationships between 3-D knee shape and bone distribution with CT.</p></div><div><h3>METHODS</h3><p>This exploratory analysis was performed ancillary to the LOSEIT trial evaluating the efficacy of liraglutide in inducing and maintaining weight loss and pain relief in overweight patients with knee OA. After exclusions, 133 participants were included, 65 from the placebo group, 68 from the liraglutide group. All had baseline CT (140kV) and weight-bearing radiographs of both knees. Both knees were segmented from the CT data for CBM using Stradview followed by registration of canonical objects to femurs and tibias using wxRegSurf. SSM was performed on combined femur and tibia registrations using MATLAB 2024a. Index knee data were taken from each participant. Generalized estimating equation (GEE) analysis looked for associations of the first 10 shape modes with KLG controlling for age, sex and mass using Bonferroni correction. 3-D cortical thickness (CTh) and subcortical trabecular attenuation (TA) maps were transferred to the canonical objects. SPM analysis was performed using the MATLAB Surfstat toolbox to establish dependence of CTh and TA distribution on shape controlling for age, sex, mass and KLG.</p></div><div><h3>RESULTS</h3><p>Study participants were 89 females and 44 males with mean +/- SD age of 59.6 +/- 9.2 yrs, mass 93.3 +/- 16.7 kg and an index knee breakdown of KLG1 = 19, KLG2 = 57, KLG3 = 57. GEE showed shape mode 2 (SM2) was the only mode significantly associated with KLG with an odds ratio of 1.43 (1.28-1.59 95% CI, P<<0.05) for each SD of the mode (Fig. 1, * = P<0.05). Subjective visualization showed substantial similarities of SM2 to the B-score, namely increased femoral articular surface area with marginal articular prominence and narrowing of the intercondylar distance (Fig. 2, +/- 3xSD of the mode). SPM showed subchondral TA was significantly dependent on SM2 across nearly all the femoral articular surface (P<0.05), showing up to 40 HU drop for each increase in SD (Fig 2). Small zones of marginal articular bone at the lateral tibiofemoral compartment showed significant CTh dependence on the shape mode (P<0.05) with an increase of up to 0.2 mm for each SD increase (Fig. 2), but the association was limited to this compartment. In the tibia, this combined shape mode represented peaked widening of the tibial plateau rim, with significant dependence of TA in the posterior lateral tibial plateau (-20 HU per SD increase) and CTh around the medial plateau","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000205/pdfft?md5=457b8d0685a0d3e8df4867c769e60496&pid=1-s2.0-S2772654124000205-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1016/j.ostima.2024.100196
C.T. Nielsen , M Henriksen , C.L. Daugaard , J.U. Nybing , P. Hansen , F.C. Müller , H. Bliddal , M. Boesen , H. Gudbergsen
INTRODUCTION
Calcium crystal (CaC) depositions in hyaline cartilage, meniscus and the joint capsule are seen in some patients with knee OA. Whether or not these crystals exacerbate the symptoms and progression of OA is not well understood. Composed primarily of calcium pyrophosphate and basic calcium phosphate crystals, CaC has been shown to activate pro-inflammatory pathways in vitro. The pro-inflammatory effect of these crystals in vivo is more uncertain.
OBJECTIVE
In this exploratory cross-sectional analysis we aimed to investigate if overweight individuals with knee OA and CaC deposits experience more knee joint inflammation compared with matched individuals without CaC deposits.
METHODS
We used pre-randomization imagining data from an RCT, the LOSEIT trial. Participants were included if they were between 18 and 75 years old; had clinical knee OA, according to the ACR criteria; showed KLG 1-3 on weight-bearing x-ray; and had a BMI ≥ 27 kg/m2. Participants had CT (Somatom Definition Edge®, Siemens, Germany) and 3T MRI (Verio®, Siemens, Germany) of the index knee. Intraarticular CaCs were assessed on CT (in-plane resolution: 0.6 × 0.6mm, slice thickness: 1mm, tube voltage: 140 kV) using a modified version of the Boston University Calcium Knee Score (BUCKS), classifying participants as OA with CaC if they had a BUCKS ≥ 1 in any sub-region. To estimate joint inflammation, we used both static and dynamic contrast-enhanced (DCE) MRI. The following static MRI variables were analyzed: MRI in OA Knee Score (MOAKS) with Hoffa-synovitis and effusion-synovitis scores summed to one MOAKS-synovitis score (0–6). The Boston-Leeds Osteoarthritis Knee Score (BLOKS) effusion sub-score (0–3) and the 11-point whole-knee synovitis score (CE-synovitis) as proposed by Guermazi et al. (0–22). Heuristic DCE-MRI analysis was carried out using the software Dynamika® v. 5.2.2 (Image Analysis Group). We included five DCE-MRI variables; Initial Rate of Enhancement (IRE), Maximum Enhancement (ME), Most Perfused Voxels (Nvoxel) and the two composite scores; IRE x Nvoxel and ME x Nvoxel. We only included participants with complete CT and MRI data, i.e., no imputation for missing data. To test if there was a difference in the MRI variables between participants with and without CaC deposits, we used an Analysis of Covariance (ANCOVA) model adjusted for age and KLG. We did not adjust for multiple testing, acknowledging the exploratory nature of this study and interpreting the results accordingly.
RESULTS
Of the 168 participants included in the LOSEIT trial 115 had MRI available; 13 (11.3 %) had CaC deposits, 8 in the cartilage, 5 in the meniscus and 2 in the joint capsule. Mean (SD) static and DCE-MRI variables are presented in Table 1 along with the results from the ANCOVA analyses. None of the MRI variables were associated with the presence of CaC deposits (Figure 1). The betw
{"title":"CALCIUM CRYSTAL DEPOSITION AND KNEE OSTEOARTHRITIS, ASSESSMENT OF JOINT INFLAMMATION BY DCE-MRI: A CROSS-SECTIONAL STUDY","authors":"C.T. Nielsen , M Henriksen , C.L. Daugaard , J.U. Nybing , P. Hansen , F.C. Müller , H. Bliddal , M. Boesen , H. Gudbergsen","doi":"10.1016/j.ostima.2024.100196","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100196","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Calcium crystal (CaC) depositions in hyaline cartilage, meniscus and the joint capsule are seen in some patients with knee OA. Whether or not these crystals exacerbate the symptoms and progression of OA is not well understood. Composed primarily of calcium pyrophosphate and basic calcium phosphate crystals, CaC has been shown to activate pro-inflammatory pathways in vitro. The pro-inflammatory effect of these crystals in vivo is more uncertain.</p></div><div><h3>OBJECTIVE</h3><p>In this exploratory cross-sectional analysis we aimed to investigate if overweight individuals with knee OA and CaC deposits experience more knee joint inflammation compared with matched individuals without CaC deposits.</p></div><div><h3>METHODS</h3><p>We used pre-randomization imagining data from an RCT, the LOSEIT trial. Participants were included if they were between 18 and 75 years old; had clinical knee OA, according to the ACR criteria; showed KLG 1-3 on weight-bearing x-ray; and had a BMI ≥ 27 kg/m<sup>2</sup>. Participants had CT (Somatom Definition Edge®, Siemens, Germany) and 3T MRI (Verio®, Siemens, Germany) of the index knee. Intraarticular CaCs were assessed on CT (in-plane resolution: 0.6 × 0.6mm, slice thickness: 1mm, tube voltage: 140 kV) using a modified version of the Boston University Calcium Knee Score (BUCKS), classifying participants as OA with CaC if they had a BUCKS ≥ 1 in any sub-region. To estimate joint inflammation, we used both static and dynamic contrast-enhanced (DCE) MRI. The following static MRI variables were analyzed: MRI in OA Knee Score (MOAKS) with Hoffa-synovitis and effusion-synovitis scores summed to one MOAKS-synovitis score (0–6). The Boston-Leeds Osteoarthritis Knee Score (BLOKS) effusion sub-score (0–3) and the 11-point whole-knee synovitis score (CE-synovitis) as proposed by Guermazi et al. (0–22). Heuristic DCE-MRI analysis was carried out using the software Dynamika® v. 5.2.2 (Image Analysis Group). We included five DCE-MRI variables; Initial Rate of Enhancement (IRE), Maximum Enhancement (ME), Most Perfused Voxels (Nvoxel) and the two composite scores; IRE x Nvoxel and ME x Nvoxel. We only included participants with complete CT and MRI data, i.e., no imputation for missing data. To test if there was a difference in the MRI variables between participants with and without CaC deposits, we used an Analysis of Covariance (ANCOVA) model adjusted for age and KLG. We did not adjust for multiple testing, acknowledging the exploratory nature of this study and interpreting the results accordingly.</p></div><div><h3>RESULTS</h3><p>Of the 168 participants included in the LOSEIT trial 115 had MRI available; 13 (11.3 %) had CaC deposits, 8 in the cartilage, 5 in the meniscus and 2 in the joint capsule. Mean (SD) static and DCE-MRI variables are presented in Table 1 along with the results from the ANCOVA analyses. None of the MRI variables were associated with the presence of CaC deposits (Figure 1). The betw","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000242/pdfft?md5=2e21b2e3b69ee87217d38f07ad30d13d&pid=1-s2.0-S2772654124000242-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rock climbers have an elevated prevalence of foot pathology. However, the factors that may explain the association between rock climbing or climbing shoes and foot joint pathology have not been elucidated. Imaging of feet in climbing shoes is limited, and no imaging of feet engaged in rock climbing exists.
OBJECTIVE
To compare the foot joint positions of rock climbers’ feet while in climbing shoes in a weight-bearing standing position with the foot joint position when climbing on a wall.
METHODS
Recreational rock climbers (n=24; 66.7% men) from the Kansas City area were recruited. Survey data were collected on climbing habits and street/climbing shoe usage. Participants feet were imaged while in climbing shoes using a Planmed XFI weight-bearing CT (WBCT) in a standing position and again while engaged on a gym-style rock climbing wall inside the WBCT gantry. Joint angles were measured for hallux valgus angle (HVA), interphalangeal angle (IMA), and first intermetatarsal angle (IPA). HVA and IMA were selected due to a clinical correlation with hallux valgus deformity which is common among rock climbers. Statistical testing on the join angulation data was performed using a linear mixed effects regression where the position (standing or climbing) was a fixed effect, and the participant ID and participant-foot interaction were random effects.
RESULTS
Participants’ mean±SD age was 36.0±10.8 years, BMI was 24.8±4.2 kg/m2, and reported climbing 2.8±1.1 times per week for 7.1±4.9 hours per week. Duration of climbing experience was 6.1±4.1 years (range: 1–15 years). Participants were comfortable climbing mean indoor bouldering V4.3±1.5 on the vermillion scale and climbing mean indoor sport 5.1±1.0 on the Yosemite decimal system. Participants indicated the hardest indoor bouldering route accomplished was V5.9±2 on the vermillion scale. The hardest indoor sport climb was 5.11±0.99 on Yosemite decimal scale. Median measured climbing shoe size was smaller than reported street shoe size (EU 41 vs 42.5, p<0.001). Compared with when standing (20.2±6.9°), there was no difference in hallux valgus angle (HVA) when climbing (HVA 20.5±7.8°; p = 0.7665). There was greater intermetatarsal angle (IMA) when climbing compared to standing (11.6±2.2° vs 9.9±1.6°; p < 0.0001). The interphalangeal angle (IPA) was greater when climbing, compared to when standing (18.7° vs 15.3°; p = 0.0009).
CONCLUSION
Using WBCT allowed a 3D weight-bearing examination of the foot structural anatomy while standing and engaged on rock-climbing footholds. Climbing shoes induce excessive angulation of the joints, more so when engaged in climbing. Additional research is needed to evaluate the effect of rotatory changes in the first ray on the development of hallux valgus and changes in sesamoid posture.
{"title":"FUNCTIONAL EVALUATION USING ENHANCED TECHNIQUES FOR PRECISION IMAGING IN CLIMBING SHOES (FEETPICS)","authors":"Q.M. Krause , N.A. Segal , O. Burroughs , B.L. Burns , D.G. Naylor","doi":"10.1016/j.ostima.2024.100204","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100204","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Rock climbers have an elevated prevalence of foot pathology. However, the factors that may explain the association between rock climbing or climbing shoes and foot joint pathology have not been elucidated. Imaging of feet in climbing shoes is limited, and no imaging of feet engaged in rock climbing exists.</p></div><div><h3>OBJECTIVE</h3><p>To compare the foot joint positions of rock climbers’ feet while in climbing shoes in a weight-bearing standing position with the foot joint position when climbing on a wall.</p></div><div><h3>METHODS</h3><p>Recreational rock climbers (n=24; 66.7% men) from the Kansas City area were recruited. Survey data were collected on climbing habits and street/climbing shoe usage. Participants feet were imaged while in climbing shoes using a Planmed XFI weight-bearing CT (WBCT) in a standing position and again while engaged on a gym-style rock climbing wall inside the WBCT gantry. Joint angles were measured for hallux valgus angle (HVA), interphalangeal angle (IMA), and first intermetatarsal angle (IPA). HVA and IMA were selected due to a clinical correlation with hallux valgus deformity which is common among rock climbers. Statistical testing on the join angulation data was performed using a linear mixed effects regression where the position (standing or climbing) was a fixed effect, and the participant ID and participant-foot interaction were random effects.</p></div><div><h3>RESULTS</h3><p>Participants’ mean±SD age was 36.0±10.8 years, BMI was 24.8±4.2 kg/m<sup>2</sup>, and reported climbing 2.8±1.1 times per week for 7.1±4.9 hours per week. Duration of climbing experience was 6.1±4.1 years (range: 1–15 years). Participants were comfortable climbing mean indoor bouldering V4.3±1.5 on the vermillion scale and climbing mean indoor sport 5.1±1.0 on the Yosemite decimal system. Participants indicated the hardest indoor bouldering route accomplished was V5.9±2 on the vermillion scale. The hardest indoor sport climb was 5.11±0.99 on Yosemite decimal scale. Median measured climbing shoe size was smaller than reported street shoe size (EU 41 vs 42.5, p<0.001). Compared with when standing (20.2±6.9°), there was no difference in hallux valgus angle (HVA) when climbing (HVA 20.5±7.8°; p = 0.7665). There was greater intermetatarsal angle (IMA) when climbing compared to standing (11.6±2.2° vs 9.9±1.6°; p < 0.0001). The interphalangeal angle (IPA) was greater when climbing, compared to when standing (18.7° vs 15.3°; p = 0.0009).</p></div><div><h3>CONCLUSION</h3><p>Using WBCT allowed a 3D weight-bearing examination of the foot structural anatomy while standing and engaged on rock-climbing footholds. Climbing shoes induce excessive angulation of the joints, more so when engaged in climbing. Additional research is needed to evaluate the effect of rotatory changes in the first ray on the development of hallux valgus and changes in sesamoid posture.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100204"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000321/pdfft?md5=26ad2f7d5f1360aa678400ea7e3c7236&pid=1-s2.0-S2772654124000321-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1016/j.ostima.2024.100190
K. Moradi , S. Mohammadi , B. Mohajer , R. Hadidchi , F.W. Roemer , A. Guermazi , S. Demehri
INTRODUCTION
Subchondral bone marrow lesions (BMLs) are a risk factor for knee OA outcomes and deep-learning (DL) methods can help in automated segmentation and risk prediction.
OBJECTIVE
To determine the association between statin use and longitudinal changes in knee MRI-detected BML volume.
METHODS
Using the Osteoarthritis Initiative (OAI) cohort, we classified participants’ knees into two categories: statin users (those who used statins from baseline to the fourth year of the cohort) and non-users. We employed a 1:1 ratio propensity score (PS) matching method, adjusting for factors including age, sex, race, BMI, smoking, alcohol use, physical activity, KL grade, abdominal obesity, diabetes mellitus, and cardiovascular diseases. We measured quantitative BML volume using a validated deep learning (DL) algorithm, applied to baseline, year-2, and year-4 intermediate-weighted knee MRIs. The outcome was determined by the differences in the 4-year BML volume change between statin users and non-users.
RESULTS
After adjusting for potential confounders, 3206 knees were included (1603 statin users:1603 non-user; 64.1 ± 8.5 years old, female/male ratio: 1.1). Multilevel linear mixed-effect regression model showed that statin use is associated a less degree of increase in BML volume over 4 years (time-treatment interaction estimate, 95% confidence interval (CI): -4.24 mm3/year, -7.26 to -1.22, P = 0.005).
CONCLUSION
Continues statin use is linked to a reduction in the worsening of BML, a known risk factor for the onset and progression of knee OA.
{"title":"STATIN USE AND DECREASED BONE MARROW LESION BURDEN: A LONGITUDINAL DEEP-LEARNING QUANTITATIVE ANALYSIS FROM OSTEOARTHRITIS INITIATIVE","authors":"K. Moradi , S. Mohammadi , B. Mohajer , R. Hadidchi , F.W. Roemer , A. Guermazi , S. Demehri","doi":"10.1016/j.ostima.2024.100190","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100190","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Subchondral bone marrow lesions (BMLs) are a risk factor for knee OA outcomes and deep-learning (DL) methods can help in automated segmentation and risk prediction.</p></div><div><h3>OBJECTIVE</h3><p>To determine the association between statin use and longitudinal changes in knee MRI-detected BML volume.</p></div><div><h3>METHODS</h3><p>Using the Osteoarthritis Initiative (OAI) cohort, we classified participants’ knees into two categories: statin users (those who used statins from baseline to the fourth year of the cohort) and non-users. We employed a 1:1 ratio propensity score (PS) matching method, adjusting for factors including age, sex, race, BMI, smoking, alcohol use, physical activity, KL grade, abdominal obesity, diabetes mellitus, and cardiovascular diseases. We measured quantitative BML volume using a validated deep learning (DL) algorithm, applied to baseline, year-2, and year-4 intermediate-weighted knee MRIs. The outcome was determined by the differences in the 4-year BML volume change between statin users and non-users.</p></div><div><h3>RESULTS</h3><p>After adjusting for potential confounders, 3206 knees were included (1603 statin users:1603 non-user; 64.1 ± 8.5 years old, female/male ratio: 1.1). Multilevel linear mixed-effect regression model showed that statin use is associated a less degree of increase in BML volume over 4 years (time-treatment interaction estimate, 95% confidence interval (CI): -4.24 mm<sup>3</sup>/year, -7.26 to -1.22, P = 0.005).</p></div><div><h3>CONCLUSION</h3><p>Continues statin use is linked to a reduction in the worsening of BML, a known risk factor for the onset and progression of knee OA.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000187/pdfft?md5=f7ce064276b2dd78f7f5e54add4536aa&pid=1-s2.0-S2772654124000187-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1016/j.ostima.2024.100221
A. Wisser , T.C. Walter-Rittel , A. Chaudhari , N.M. Brisson , T. Maleitzke , G.N. Duda , W. Wirth , T. Winkler , F. Eckstein
INTRODUCTION
Assessing the structure and properties of articular tissues using MRI-based approaches is highly relevant to OA studies, as MRI enables direct visualization of all joint structures. These can be evaluated using semi-quantitative (sq) or quantitative (q) morphometric methods. Insights into the biochemical composition of specific tissues can be obtained with MRI T2 relaxometry. A crucial basis for such OA analysis is the choice of a suitable, and time-efficient MRI acquisition protocol that assures high image quality while lowering patient burden and costs through short scan time. Moreover, standardization of MRI protocols and analysis techniques across studies is helpful to ensure comparability between studies.
OBJECTIVE
To propose - as an expert opinion - a state-of-the-art MRI acquisition protocol for clinical trials on both early and advanced stages of knee OA. This protocol is designed to support a multitude of semi-quantitative and quantitative image assessments (including synovitis), relevant to the study and management of knee OA, and ideally suitable for automated analysis.
METHODS
A PubMed literature search of articles published in the last 20 years was performed (focus on the past 5 years) and several OA imaging experts provided input. Specific MRI sequences (including orientations, spatial resolutions, and parameters) were identified that support the above purpose. The implementation of the protocol had to be feasible on standard clinical MRI scanners, with a net acquisition time of <30 minutes.
RESULTS
The proposed protocol is shown in Tables 1 & 2, and example images in Figure 1. MRIs should be obtained at ≥1.5T, ideally without hardware (or major software) changes during longitudinal studies. Localizer images should be used to spatially align the sequences with the knee anatomy and position. We recommend clinical 2D proton density (PD) turbo spin echo sequences (TSE) with fat suppression (FS) in two planes, and a coronal T1-weighted TSE (without FS) to support sq assessment of all articular tissues and pathologies, and q assessment of Hoffa and effusion synovitis. A high-resolution 3D quantitative double echo steady state (qDESS) sequence [1] is proposed (coronal, or sagittal, or sagittal near-isotropic) for quantitative cartilage morphometry and T2, for bone (shape) and for q meniscus analysis. Inversion recovery spin echo (FLAIR [2]) is included for potential non-contrast-enhanced depiction of synovitis. All images should be checked for quality and protocol adherence as soon as possible (best immediately) after image acquisition. Acquiring repeated scans (re-test) in a few patients per site at baseline and follow-up can provide information on study-specific test-retest errors and the smallest detectable change (SDC).
CONCLUSION
Here, we propose a state-of-the-art image acquisition protocol for tria
引言 使用基于核磁共振成像的方法评估关节组织的结构和特性与 OA 研究高度相关，因为核磁共振成像可直接观察所有关节结构。可使用半定量（sq）或定量（q）形态计量学方法对其进行评估。通过磁共振成像 T2 驰豫测量法可以深入了解特定组织的生化成分。此类 OA 分析的一个重要基础是选择合适且省时的磁共振成像采集方案，在确保高质量图像的同时，通过缩短扫描时间降低患者负担和成本。此外，不同研究中磁共振成像方案和分析技术的标准化也有助于确保不同研究之间的可比性。目的作为专家意见，为膝关节 OA 早期和晚期临床试验提出最先进的磁共振成像采集方案。该方案旨在支持多种半定量和定量图像评估（包括滑膜炎），与膝关节 OA 的研究和管理相关，最好适合自动分析。方法对过去 20 年（重点是过去 5 年）发表的文章进行 PubMed 文献检索，多位 OA 成像专家提供了意见。确定了支持上述目的的特定 MRI 序列（包括方向、空间分辨率和参数）。结果拟议方案见表 1 和表 2，示例图像见图 1。磁共振成像应在≥1.5T下获得，最好在纵向研究期间不对硬件（或主要软件）进行更改。应使用定位器图像将序列与膝关节解剖结构和位置进行空间对齐。我们推荐临床二维质子密度（PD）涡轮自旋回波序列（TSE）和两个平面的脂肪抑制（FS），以及冠状T1加权TSE（无FS），以支持所有关节组织和病变的平扫评估，以及Hoffa和渗出性滑膜炎的q评估。建议采用高分辨率三维定量双回波稳态（qDESS）序列[1]（冠状、或矢状、或矢状近各向同性）进行软骨形态和 T2 定量、骨（形状）和半月板 q 分析。反转恢复自旋回波（FLAIR[2]）用于滑膜炎的潜在非对比度增强描述。获取图像后，应尽快（最好立即）检查所有图像的质量和是否符合方案要求。在基线和随访时，对每个部位的少数患者进行重复扫描（再测试），可提供有关特定研究的测试-再测试误差和最小可检测变化（SDC）的信息。在确保临床研究技术可行性的同时，我们还提出了图像采集效率（时间）、安全性和技术/方法多样性之间的平衡。重要的是，建议的方法为膝关节 OA 疾病改变临床试验中组织结构、组成和病理（自动化）分析的科学创新提供了潜力。
{"title":"A SAMPLE RAPID MRI ACQUISITION PROTOCOL SUPPORTING ASSESSMENT OF MULTIPLE ARTICULAR TISSUES AND PATHOLOGIES IN EARLY AND ADVANCED KNEE OSTEOARTHRITIS","authors":"A. Wisser , T.C. Walter-Rittel , A. Chaudhari , N.M. Brisson , T. Maleitzke , G.N. Duda , W. Wirth , T. Winkler , F. Eckstein","doi":"10.1016/j.ostima.2024.100221","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100221","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Assessing the structure and properties of articular tissues using MRI-based approaches is highly relevant to OA studies, as MRI enables direct visualization of all joint structures. These can be evaluated using semi-quantitative (sq) or quantitative (q) morphometric methods. Insights into the biochemical composition of specific tissues can be obtained with MRI T2 relaxometry. A crucial basis for such OA analysis is the choice of a suitable, and time-efficient MRI acquisition protocol that assures high image quality while lowering patient burden and costs through short scan time. Moreover, standardization of MRI protocols and analysis techniques across studies is helpful to ensure comparability between studies.</p></div><div><h3>OBJECTIVE</h3><p>To propose - as an expert opinion - a state-of-the-art MRI acquisition protocol for clinical trials on both early and advanced stages of knee OA. This protocol is designed to support a multitude of semi-quantitative and quantitative image assessments (including synovitis), relevant to the study and management of knee OA, and ideally suitable for automated analysis.</p></div><div><h3>METHODS</h3><p>A PubMed literature search of articles published in the last 20 years was performed (focus on the past 5 years) and several OA imaging experts provided input. Specific MRI sequences (including orientations, spatial resolutions, and parameters) were identified that support the above purpose. The implementation of the protocol had to be feasible on standard clinical MRI scanners, with a net acquisition time of <30 minutes.</p></div><div><h3>RESULTS</h3><p>The proposed protocol is shown in Tables 1 & 2, and example images in Figure 1. MRIs should be obtained at ≥1.5T, ideally without hardware (or major software) changes during longitudinal studies. Localizer images should be used to spatially align the sequences with the knee anatomy and position. We recommend clinical 2D proton density (PD) turbo spin echo sequences (TSE) with fat suppression (FS) in two planes, and a coronal T1-weighted TSE (without FS) to support sq assessment of all articular tissues and pathologies, and q assessment of Hoffa and effusion synovitis. A high-resolution 3D quantitative double echo steady state (qDESS) sequence [1] is proposed (coronal, or sagittal, or sagittal near-isotropic) for quantitative cartilage morphometry and T2, for bone (shape) and for q meniscus analysis. Inversion recovery spin echo (FLAIR [2]) is included for potential non-contrast-enhanced depiction of synovitis. All images should be checked for quality and protocol adherence as soon as possible (best immediately) after image acquisition. Acquiring repeated scans (re-test) in a few patients per site at baseline and follow-up can provide information on study-specific test-retest errors and the smallest detectable change (SDC).</p></div><div><h3>CONCLUSION</h3><p>Here, we propose a state-of-the-art image acquisition protocol for tria","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100221"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000497/pdfft?md5=cc64f9ab06672d9afe660b4bfa00aa03&pid=1-s2.0-S2772654124000497-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1016/j.ostima.2024.100211
J.E. Collins , F.W. Roemer , A. Guermazi
INTRODUCTION
Knee OA is a disease of the whole joint involving multiple tissues. MRI-based semi-quantitative (SQ) scoring of knee OA is a method based on ordinal grading to perform multi-tissue joint assessment. SQ scoring is used to measure severity of structural disease on a tissue level and allows evaluation of disease progression. While guidance is available to describe how SQ scoring may be applied and can be used for clinical trial enrichment, less information is available on how these parameters should be used to assess outcomes in research and clinical trial contexts.
OBJECTIVE
Here we describe how SQ scoring can optimally be used to quantify longitudinal change in knee OA and highlight its potential as an outcome measure in research and clinical trials.
METHODS
The two most widely used SQ scoring systems for knee OA, MOAKS and WORMS, rely on standard MRI acquisitions (usually intermediate-weighted fat-suppressed sequences in three orthogonal planes) and ordinal ratings of knee features by expert readers. Key pathoanatomic features of the joint are assessed, including cartilage damage (both in surface area extent and in full-thickness loss), meniscus damage, osteophytes, BM lesions, synovitis, and others. The knee joint is divided into subregions (SRs) (e.g., MOAKS scores cartilage damage across 14 SRs) or locations (e.g., osteophytes) and each SR or location is scored for a given feature.
RESULTS
The following approaches may be considered to assess longitudinal change. Worsening across SRs is quantified by the count of the number of SRs with a worse (higher) score at follow-up vs. baseline and by the change in the number of SRs affected (score=0 at baseline and >0 at follow-up). Improvement across SRs is quantified as the number of SRs with improvement from baseline to follow-up (i.e., lower score at follow-up vs. baseline). The delta-SR approach considers worsening and improvement simultaneously and is calculated as the number of SRs with worsening minus the number of SRs with improvement (particularly relevant for fluctuating features such as BM lesions). The delta-sum approach considers the ordinal score in each SR: the sum of ordinal scores across all SRs is computed and change is quantified by the difference in total score. Finally, maximum grade change is the maximum change across all SRs. Within-grade changes are changes that do not fulfill the definition of a full-grade change but do represent definite SQ visual change. Including such changes in SQ assessment of longitudinal change increases sensitivity to change. Examples are shown in Table 1.
The various approaches to quantifying longitudinal change may result in variables that are counts, ordered categories, binary categories, or continuous parameters. Count data may be analyzed with Poisson regression, binary data with log-binomial or logistic regression, and continuous data wi
简介：膝关节 OA 是一种涉及多个组织的全关节疾病。基于核磁共振成像的膝关节 OA 半定量（SQ）评分是一种基于顺序分级的方法，用于进行多组织关节评估。SQ 评分用于测量组织层面结构性疾病的严重程度，并可评估疾病的进展情况。虽然已有指南描述了如何应用 SQ 评分并将其用于临床试验强化，但关于如何将这些参数用于评估研究和临床试验结果的信息却较少。目的在此，我们将描述如何以最佳方式使用 SQ 评分来量化膝关节 OA 的纵向变化，并强调其作为研究和临床试验结果测量指标的潜力。方法膝关节 OA 最广泛使用的两种 SQ 评分系统 MOAKS 和 WORMS 依赖于标准 MRI 采集（通常是三个正交平面的中间加权脂肪抑制序列）和专家读者对膝关节特征的顺序评分。对关节的主要病理解剖特征进行评估，包括软骨损伤（包括表面积范围和全厚度损失）、半月板损伤、骨质增生、BM 病变、滑膜炎等。膝关节被划分为亚区域（SR）（例如，MOAKS 对 14 个 SR 的软骨损伤进行评分）或位置（例如，骨质增生），每个 SR 或位置都根据特定特征进行评分。通过计算随访时与基线相比得分较差（较高）的 SR 数量以及受影响 SR 数量的变化（基线时得分=0，随访时得分为 0）来量化各 SR 的恶化情况。各 SR 的改善情况量化为从基线到随访期间有所改善（即随访时得分低于基线）的 SR 数量。delta-SR 法同时考虑恶化和改善，计算方法是恶化的 SR 数减去改善的 SR 数（特别适用于 BM 病变等波动特征）。delta-sum 法考虑了每个 SR 的序数得分：计算所有 SR 的序数得分之和，并以总分之差量化变化。最后，最大等级变化是所有 SR 的最大变化。等内变化是指不符合全等级变化定义但确实代表明确的 SQ 视觉变化的变化。将此类变化纳入 SQ 纵向变化评估可提高对变化的敏感度。量化纵向变化的各种方法可能会产生计数变量、有序类别、二元类别或连续参数。计数数据可用泊松回归分析，二元数据可用对数二项式或逻辑回归分析，连续数据可用线性回归分析。在分析重复测量或聚类数据时必须特别注意，在分析聚类数据时可考虑使用随机效应、混合效应或边际模型，例如，在进行分区与全膝水平的分析时，或在每个参与者包括一个膝关节与两个膝关节时。根据数据的性质和阅读者的数量（如加权卡帕、ICC 等），需要使用不同的方法确定可靠性。纵向变化的 SQ 成像评估为更好地了解多种组织类型的疾病进展提供了机会，这可能使未来的试验结果与患者表型或治疗作用机制相匹配。此外，还可以评估安全性信号，而这些信号不一定能通过专用（如软骨聚焦）成像方案观察到。
{"title":"APPROACHES TO OPTIMIZE ANALYSES OF MULTIDIMENSIONAL ORDINAL MRI DATA IN OSTEOARTHRITIS RESEARCH AND CLINICAL TRIALS","authors":"J.E. Collins , F.W. Roemer , A. Guermazi","doi":"10.1016/j.ostima.2024.100211","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100211","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Knee OA is a disease of the whole joint involving multiple tissues. MRI-based semi-quantitative (SQ) scoring of knee OA is a method based on ordinal grading to perform multi-tissue joint assessment. SQ scoring is used to measure severity of structural disease on a tissue level and allows evaluation of disease progression. While guidance is available to describe how SQ scoring may be applied and can be used for clinical trial enrichment, less information is available on how these parameters should be used to assess outcomes in research and clinical trial contexts.</p></div><div><h3>OBJECTIVE</h3><p>Here we describe how SQ scoring can optimally be used to quantify longitudinal change in knee OA and highlight its potential as an outcome measure in research and clinical trials.</p></div><div><h3>METHODS</h3><p>The two most widely used SQ scoring systems for knee OA, MOAKS and WORMS, rely on standard MRI acquisitions (usually intermediate-weighted fat-suppressed sequences in three orthogonal planes) and ordinal ratings of knee features by expert readers. Key pathoanatomic features of the joint are assessed, including cartilage damage (both in surface area extent and in full-thickness loss), meniscus damage, osteophytes, BM lesions, synovitis, and others. The knee joint is divided into subregions (SRs) (e.g., MOAKS scores cartilage damage across 14 SRs) or locations (e.g., osteophytes) and each SR or location is scored for a given feature.</p></div><div><h3>RESULTS</h3><p>The following approaches may be considered to assess longitudinal change. Worsening across SRs is quantified by the count of the number of SRs with a worse (higher) score at follow-up vs. baseline and by the change in the number of SRs affected (score=0 at baseline and >0 at follow-up). Improvement across SRs is quantified as the number of SRs with improvement from baseline to follow-up (i.e., lower score at follow-up vs. baseline). The delta-SR approach considers worsening and improvement simultaneously and is calculated as the number of SRs with worsening minus the number of SRs with improvement (particularly relevant for fluctuating features such as BM lesions). The delta-sum approach considers the ordinal score in each SR: the sum of ordinal scores across all SRs is computed and change is quantified by the difference in total score. Finally, maximum grade change is the maximum change across all SRs. Within-grade changes are changes that do not fulfill the definition of a full-grade change but do represent definite SQ visual change. Including such changes in SQ assessment of longitudinal change increases sensitivity to change. Examples are shown in Table 1.</p><p>The various approaches to quantifying longitudinal change may result in variables that are counts, ordered categories, binary categories, or continuous parameters. Count data may be analyzed with Poisson regression, binary data with log-binomial or logistic regression, and continuous data wi","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000394/pdfft?md5=ff7cca06a3ff754fe4a845a4ad9e2481&pid=1-s2.0-S2772654124000394-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1016/j.ostima.2024.100205
V. Suryadevara , L. Baratto , R. von Kruechten , N. Malik , S.B. Singh , A.M. Dreisbach , Z. Shokri Varniab , Y. Tanyildizi , T. Liang , J. Cotton , N. Bézière , B. Pichler , S. Goodman , H.E. Daldrup-Link
INTRODUCTION
Cellular senescence, a hallmark of aging, plays a key role in the development of osteoarthritis (OA). Several senolytic therapies have been developed to clear senescent cells in the joint resulting in delayed cartilage degradation and improved clinical symptoms of patients with OA. However, a critical challenge remains: Developing reliable imaging techniques to detect senescence in patients. This will be essential to effectively monitor the efficacy of senolytic therapies and personalize treatment for OA.
OBJECTIVE
Senescent cells overexpress β-galactosidase (β-gal). We have demonstrated in vitro (primary chondrocytes) and in vivo (small animal model-mice and a large animal model-pigs) that [18F]FPyGal, a β-gal targeted PET tracer can detect senescent cells (Figure 1). The objective of our study was to evaluate if [18F]FPyGal could detect senescent cells in human joint specimen from patients with OA. We hypothesized that [18F]FPyGal retention in human specimen, as measured by positron emission tomography (PET), would correlate with the Outerbridge score, determined on simultaneously acquired MRI scans.
METHODS
This study was approved by the Institutional Review Board of our Institution (IRB-62254). Written informed consent was obtained from five patients (one male and four females with an age of 63-86 years (mean 72.8 ± 8.98) to donate their knee specimens after total knee replacement with a joint endoprosthesis. The ten freshly obtained specimens were incubated with 200μCi of the radiotracer for an hour at room temperature. The specimens were washed trice with PBS and imaged in a clinical PET/MRI scanner (Signa GE Healthcare, Chicago, IL). The MRI protocol consisted of a fat-saturated proton density-weighted fast spin-echo sequence (TR = 3,345 ms, TE = 33 ms, FA = 111°, matrix size = 192 × 192 pixels, slice thickness (SL) = 1.5 mm, FOV = 8 cm, and TA = 5 min along and a LAVA sequence (TR = 3.802 ms, TE=1.674, FA=3, Matrix=192 × 192 pixels) for attenuation correction. PET images were acquired simultaneously and reconstructed using the Ordered-Subset Expectation Maximization (OSEM) algorithm with 2 iterations and 28 subsets. The PET/MRI scans were independently analyzed by one nuclear medicine physician and one radiologist. The radiologist assigned a modified Outerbridge score (1-4) of the cartilage damage of these areas, while the Nuclear Medicine physician measured the standardized uptake values (SUV) of the same areas. The SUV and Outerbridge score were correlated with Jonckheere-Terpstra test.
RESULTS
PET/MRI images of human osteoarthritic specimens demonstrated focal retention of [18F]FPyGal radiotracer in some cartilage areas and not others at 1 hour after incubation with 200μCi [18F]FPyGal radiotracer. A significantly higher radiotracer uptake was observed in cartilage areas with an Outerbridge score of
{"title":"[18F]FPyGal PET TRACER DETECTS SENESCENCE IN HUMAN OSTEOARTHRITIC SPECIMENS","authors":"V. Suryadevara , L. Baratto , R. von Kruechten , N. Malik , S.B. Singh , A.M. Dreisbach , Z. Shokri Varniab , Y. Tanyildizi , T. Liang , J. Cotton , N. Bézière , B. Pichler , S. Goodman , H.E. Daldrup-Link","doi":"10.1016/j.ostima.2024.100205","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100205","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Cellular senescence, a hallmark of aging, plays a key role in the development of osteoarthritis (OA). Several senolytic therapies have been developed to clear senescent cells in the joint resulting in delayed cartilage degradation and improved clinical symptoms of patients with OA. However, a critical challenge remains: Developing reliable imaging techniques to detect senescence in patients. This will be essential to effectively monitor the efficacy of senolytic therapies and personalize treatment for OA.</p></div><div><h3>OBJECTIVE</h3><p>Senescent cells overexpress β-galactosidase (β-gal). We have demonstrated <em>in vitro</em> (primary chondrocytes) and <em>in vivo</em> (small animal model-mice and a large animal model-pigs) that [18F]FPyGal, a β-gal targeted PET tracer can detect senescent cells (<strong>Figure 1)</strong>. The objective of our study was to evaluate if [18F]FPyGal could detect senescent cells in human joint specimen from patients with OA. We hypothesized that [18F]FPyGal retention in human specimen, as measured by positron emission tomography (PET), would correlate with the Outerbridge score, determined on simultaneously acquired MRI scans.</p></div><div><h3>METHODS</h3><p>This study was approved by the Institutional Review Board of our Institution (IRB-62254). Written informed consent was obtained from five patients (one male and four females with an age of 63-86 years (mean 72.8 ± 8.98) to donate their knee specimens after total knee replacement with a joint endoprosthesis. The ten freshly obtained specimens were incubated with 200μCi of the radiotracer for an hour at room temperature. The specimens were washed trice with PBS and imaged in a clinical PET/MRI scanner (Signa GE Healthcare, Chicago, IL). The MRI protocol consisted of a fat-saturated proton density-weighted fast spin-echo sequence (TR = 3,345 ms, TE = 33 ms, FA = 111°, matrix size = 192 × 192 pixels, slice thickness (SL) = 1.5 mm, FOV = 8 cm, and TA = 5 min along and a LAVA sequence (TR = 3.802 ms, TE=1.674, FA=3, Matrix=192 × 192 pixels) for attenuation correction. PET images were acquired simultaneously and reconstructed using the Ordered-Subset Expectation Maximization (OSEM) algorithm with 2 iterations and 28 subsets. The PET/MRI scans were independently analyzed by one nuclear medicine physician and one radiologist. The radiologist assigned a modified Outerbridge score (1-4) of the cartilage damage of these areas, while the Nuclear Medicine physician measured the standardized uptake values (SUV) of the same areas. The SUV and Outerbridge score were correlated with Jonckheere-Terpstra test.</p></div><div><h3>RESULTS</h3><p>PET/MRI images of human osteoarthritic specimens demonstrated focal retention of [18F]FPyGal radiotracer in some cartilage areas and not others at 1 hour after incubation with 200μCi [18F]FPyGal radiotracer. A significantly higher radiotracer uptake was observed in cartilage areas with an Outerbridge score of ","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000333/pdfft?md5=134cd2543c2fa426b5c38312094208e7&pid=1-s2.0-S2772654124000333-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1016/j.ostima.2024.100198
F. Eckstein , A. Chaudhari , D.H. Hunter , W. Wirth
INTRODUCTION
Observational studies applying MRI-based quantitative cartilage morphometry, as well as validation studies testing fully automated cartilage segmentation, often rely on the (sagittal) double echo steady state (DESS) sequence from the OAI. However, almost all current multicenter trials evaluating putative disease-modifying OA drugs (DMOADs) use conventional spoiled gradient echo MRI (e.g. FLASH), given its broader availability across worldwide vendor and MRI scanner platforms. A comparison of cartilage loss between coronal FLASH and sagittal DESS using manual segmentation was evaluated in a small sample (n=80) [1], but it is unclear which of both protocols is more sensitive in detecting differences between knees with and without structural progression, particularly in consideration of potential bias from subjective reader preferences.
OBJECTIVE
i) To directly compare the sensitivity to longitudinal change of cartilage morphometry between both MRI protocols in the FNIH-1 OA Biomarkers Consortium [2,3]; and ii) to compare the discrimination of change between progressor and non-progressor knees, both using a convolutional neural network (CNNs) deep learning (DL) algorithm [3,4] for fully automated cartilage segmentation.
METHODS
Coronal FLASH and sagittal DESS CNNs were trained (2D U-Net) using 86 OAI knees with radiographic OA that had manual cartilage segmentations from both MRI sequences [4]. Both models displayed high agreement (Dice Similarity Coefficient) and good accuracy of cartilage thickness metrics in a ROA validation/test set (n=18/18), compared with manual segmentation [2]. FLASH MRI had been acquired in one of both knees. Therefore, the current analysis focused on 309 (304 right and 5 left) knees from the FNIH-1 sample [3]: the CNN models were applied to baseline & 2-year follow-up MRIs [3] of: 100 combined progressor knees (both radiographic [>0.7mm JSW loss] & pain progression between baseline and year >2), 104 non-progressor knees, 53 knees with isolated radiographic, and 52 with isolated pain progression. Medial femorotibial (MFTC) cartilage thickness change was compared i) between all knees with (n=153) vs. without (n=156) radiographic progression and ii) between knees with vs. without combined progression (original OAI FNIH-1 analytic design [2]). The standardized response mean (SRM) was used as a measure of sensitivity to change, and Cohen's D as a measure of effect size for discriminating longitudinal change between both groups.
RESULTS
The MFTC cartilage thickness change using CNN segmentation in all knees with radiographic progression was –211µm (SRM=-0.78) for coronal FLASH and –133µm (SRM=-0.76) for sagittal DESS; it was –37µm (SRM=-0.25) and –13µm (SRM=-0.11) in knees without radiographic progression respectively (Fig. 1). Cohen's D for progressors vs. non-progressors was 0.80 for coronal FLASH and 0.81 for
{"title":"GENTLE(WO)MAN'S DUEL!: SENSITIVITY TO CARTILAGE THICKNESS CHANGE OF CORONAL FLASH VS SAGITTAL DESS WITH FULLY AUTOMATED SEGMENTATION","authors":"F. Eckstein , A. Chaudhari , D.H. Hunter , W. Wirth","doi":"10.1016/j.ostima.2024.100198","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100198","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Observational studies applying MRI-based quantitative cartilage morphometry, as well as validation studies testing fully automated cartilage segmentation, often rely on the (sagittal) double echo steady state (DESS) sequence from the OAI. However, almost all current multicenter trials evaluating putative disease-modifying OA drugs (DMOADs) use conventional spoiled gradient echo MRI (e.g. FLASH), given its broader availability across worldwide vendor and MRI scanner platforms. A comparison of cartilage loss between coronal FLASH and sagittal DESS using manual segmentation was evaluated in a small sample (n=80) [1], but it is unclear which of both protocols is more sensitive in detecting differences between knees with and without structural progression, particularly in consideration of potential bias from subjective reader preferences.</p></div><div><h3>OBJECTIVE</h3><p>i) To directly compare the sensitivity to longitudinal change of cartilage morphometry between both MRI protocols in the FNIH-1 OA Biomarkers Consortium [2,3]; and ii) to compare the discrimination of change between progressor and non-progressor knees, both using a convolutional neural network (CNNs) deep learning (DL) algorithm [3,4] for fully automated cartilage segmentation.</p></div><div><h3>METHODS</h3><p>Coronal FLASH and sagittal DESS CNNs were trained (2D U-Net) using 86 OAI knees with radiographic OA that had manual cartilage segmentations from both MRI sequences [4]. Both models displayed high agreement (Dice Similarity Coefficient) and good accuracy of cartilage thickness metrics in a ROA validation/test set (n=18/18), compared with manual segmentation [2]. FLASH MRI had been acquired in one of both knees. Therefore, the current analysis focused on 309 (304 right and 5 left) knees from the FNIH-1 sample [3]: the CNN models were applied to baseline & 2-year follow-up MRIs [3] of: 100 combined progressor knees (both radiographic [>0.7mm JSW loss] & pain progression between baseline and year >2), 104 non-progressor knees, 53 knees with isolated radiographic, and 52 with isolated pain progression. Medial femorotibial (MFTC) cartilage thickness change was compared i) between all knees with (n=153) vs. without (n=156) radiographic progression and ii) between knees with vs. without combined progression (original OAI FNIH-1 analytic design [2]). The standardized response mean (SRM) was used as a measure of sensitivity to change, and Cohen's D as a measure of effect size for discriminating longitudinal change between both groups.</p></div><div><h3>RESULTS</h3><p>The MFTC cartilage thickness change using CNN segmentation in all knees with radiographic progression was –211µm (SRM=-0.78) for coronal FLASH and –133µm (SRM=-0.76) for sagittal DESS; it was –37µm (SRM=-0.25) and –13µm (SRM=-0.11) in knees without radiographic progression respectively (Fig. 1). Cohen's D for progressors vs. non-progressors was 0.80 for coronal FLASH and 0.81 for ","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000266/pdfft?md5=a3acef671520a9aa5d4b0b70ee3763fd&pid=1-s2.0-S2772654124000266-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1016/j.ostima.2024.100202
K. Kim , W. Zaylor , S. Khan , R. Lartey , B.L. Eck , M. Li , S. Gaj , J. Kim , C.S. Winalski , F. Altahawi , M.H. Jones , L.J. Huston , K.D. Harkins , M.V. Knopp , C.C. Kaeding , K.P. Spindler , X. Li
INTRODUCTION
Patients after ACL have a high risk of developing post-traumatic osteoarthritis (PTOA) regardless surgical reconstruction (ACLR). However, long-term soft tissue degeneration after ACLR and their relationship with patient pain development are largely unknown.
OBJECTIVE
To investigate radiomic features of cartilage, menisci and thigh muscle that associates with radiographic PTOA and pain development in patients 10+ years after ACLR using qMRI.
METHODS
169 patients from the Multicenter Orthopedic Outcomes Network (MOON) on-site cohort were studied. Patient-reported outcome measures were collected using knee injury and osteoarthritis outcome scores (KOOS) survey from patients before surgery and at 10 years follow-up (13.1 ± 1.8 years after ACLR). Radiographs and knee and mid-thigh qMRI were also collected at 10 years follow-up. Radiographic PTOA was defined from radiographs based on a KLG ≥ 2. From KOOS, pain score with threshold of ≤ 85.0 were utilized (one SD below the mean KOOS Pain score of healthy subjects). All qMRI data were acquired at three sites using four different 3T MRI scanners and centrally processed, using an established workflow with rigorous quality control. T1rho and T2 maps of knee cartilage and menisci were acquired using MAPSS, along with DESS for registration and segmentation. For the mid-thigh, fat fraction maps and anatomical images were acquired using 6-point Dixon and T1-weighted TSE scans (Figure 1). A total of 17698 radiomic features were extracted from qMRI maps. A Boruta based feature selection was employed to select 20 features associated with radiographic PTOA and KOOS pain. The selected radiomic features with clinical features such as age, graft-type and BMI were trained using gradient boost machine learning model with five-fold cross-validation. The model's performance was evaluated using mean and SD of the area under the receiver-operating curves (AUROC), sensitivity, and specificity on test data.
RESULTS
27% of the patients exhibited radiographic PTOA based on KL grade, while 14% of the patients exhibited KOOS pain ≤ 85 (Table 1). Out of 20 selected features, ten features from cartilage and menisci regions, and muscle fat fraction were relevant to radiographic PTOA, while nine selected features from cartilage and muscle were relevant to KOOS-pain. The selected features alone resulted in high predictability performance for radiographic (0.81 ± 0.09) PTOA and KOOS-pain (0.68 ± 0.13) compared to clinical features (Figure 2).
CONCLUSION
The radiomic analysis show that features from both articular cartilage and thigh muscle were associated with radiographic PTOA and pain development 10 years after ACLR. Radiomic analysis with qMRI may serve as a powerful tool for improving our understanding of PTOA and pain development after ACLR. Future works may involve inclusion of tissue and joint lesions
{"title":"SOFT TISSUE DEGENRATION 10+ YEARS AFTER ACLR AND THEIR ASSOCIATION WITH RADIOGRAPHIC PTOA AND PAIN DEVELOPMENT: RADIOMIC ANALYSIS USING qMRI APPROACH IN MOON COHORT","authors":"K. Kim , W. Zaylor , S. Khan , R. Lartey , B.L. Eck , M. Li , S. Gaj , J. Kim , C.S. Winalski , F. Altahawi , M.H. Jones , L.J. Huston , K.D. Harkins , M.V. Knopp , C.C. Kaeding , K.P. Spindler , X. Li","doi":"10.1016/j.ostima.2024.100202","DOIUrl":"https://doi.org/10.1016/j.ostima.2024.100202","url":null,"abstract":"<div><h3>INTRODUCTION</h3><p>Patients after ACL have a high risk of developing post-traumatic osteoarthritis (PTOA) regardless surgical reconstruction (ACLR). However, long-term soft tissue degeneration after ACLR and their relationship with patient pain development are largely unknown.</p></div><div><h3>OBJECTIVE</h3><p>To investigate radiomic features of cartilage, menisci and thigh muscle that associates with radiographic PTOA and pain development in patients 10+ years after ACLR using qMRI.</p></div><div><h3>METHODS</h3><p>169 patients from the Multicenter Orthopedic Outcomes Network (MOON) on-site cohort were studied. Patient-reported outcome measures were collected using knee injury and osteoarthritis outcome scores (KOOS) survey from patients before surgery and at 10 years follow-up (13.1 ± 1.8 years after ACLR). Radiographs and knee and mid-thigh qMRI were also collected at 10 years follow-up. Radiographic PTOA was defined from radiographs based on a KLG ≥ 2. From KOOS, pain score with threshold of ≤ 85.0 were utilized (one SD below the mean KOOS Pain score of healthy subjects). All qMRI data were acquired at three sites using four different 3T MRI scanners and centrally processed, using an established workflow with rigorous quality control. T1rho and T2 maps of knee cartilage and menisci were acquired using MAPSS, along with DESS for registration and segmentation. For the mid-thigh, fat fraction maps and anatomical images were acquired using 6-point Dixon and T1-weighted TSE scans (Figure 1). A total of 17698 radiomic features were extracted from qMRI maps. A Boruta based feature selection was employed to select 20 features associated with radiographic PTOA and KOOS pain. The selected radiomic features with clinical features such as age, graft-type and BMI were trained using gradient boost machine learning model with five-fold cross-validation. The model's performance was evaluated using mean and SD of the area under the receiver-operating curves (AUROC), sensitivity, and specificity on test data.</p></div><div><h3>RESULTS</h3><p>27% of the patients exhibited radiographic PTOA based on KL grade, while 14% of the patients exhibited KOOS pain ≤ 85 (Table 1). Out of 20 selected features, ten features from cartilage and menisci regions, and muscle fat fraction were relevant to radiographic PTOA, while nine selected features from cartilage and muscle were relevant to KOOS-pain. The selected features alone resulted in high predictability performance for radiographic (0.81 ± 0.09) PTOA and KOOS-pain (0.68 ± 0.13) compared to clinical features (Figure 2).</p></div><div><h3>CONCLUSION</h3><p>The radiomic analysis show that features from both articular cartilage and thigh muscle were associated with radiographic PTOA and pain development 10 years after ACLR. Radiomic analysis with qMRI may serve as a powerful tool for improving our understanding of PTOA and pain development after ACLR. Future works may involve inclusion of tissue and joint lesions","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"4 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772654124000308/pdfft?md5=c6ac1171b79c6756d9486d456471807a&pid=1-s2.0-S2772654124000308-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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