Pub Date : 2025-01-01DOI: 10.1016/j.ostima.2025.100300
H. Liu, J.L. Gregory, M.O. Silva, C.E. Davey, K.S. Stok
<div><h3>INTRODUCTION</h3><div>Longitudinal assessment of knee joint structure holds promise for providing invaluable spatial-temporal information on the progression of degenerative musculoskeletal (MSK) diseases involving the knee joint.</div></div><div><h3>OBJECTIVE</h3><div>This proof-of-concept study aims to establish a time-lapse <em>in vivo</em> imaging protocol with high temporal resolution to longitudinally track multi-scale structural changes, including mechanical alteration to whole joint structure, sensitive microstructural changes to subchondral bone, and abnormal bone remodeling activity, in a mouse collagenase-induced osteoarthritis (OA) model.</div></div><div><h3>METHODS</h3><div>Eight male C57BL/10 mice aged nine weeks were recruited and assigned to two longitudinal groups, control (CT) and OA. Of these, four ten-week-old mice assigned to the OA group received intra-articular injection of collagenase on the right knee to destabilize the right tibiofemoral joint. Longitudinal <em>in vivo</em> micro-computed tomography (microCT) scans were performed one day before collagenase injection and then weekly for eight weeks in total, resulting in nine scans for each animal. <em>In vivo</em> microCT (Scanco Medical) was performed with a source voltage of 70 kVp, an integration time of 350 <em>ms</em>, a current of 114 μ<em>A</em>, and an isotropic nominal resolution of 10.4 μ<em>m</em> with 1000 projections, with each scanning taking around 30 minutes. Quantitative morphometric analysis (QMA) was performed to measure longitudinal changes to structure of whole joint and subchondral bone, including joint space width (mm), and trabecular thickness (mm). Visualization of dynamic bone remodeling was performed by registering serial microCT scans. Bone resorption rate, BRR (%/day), and bone formation rate, BFR (%/day) were measured to quantify bone remodeling activity. To test the differences between CT and OA group at each time point from week 1 to week 8, a one-way analysis of covariance was used.</div></div><div><h3>RESULTS</h3><div>Three weeks post OA-induction, a significantly smaller joint space width was observed in medial osteoarthritic joint (202 μm), when compared to CT joint (228 μm) (p < 0.01). Regarding trabecular thickness, significant differences were observed at multiple time points between CT and OA groups, specifically in the first three weeks at the early stage of OA progression at lateral side (p < 0.01). Representative 3D visualization of bone formation and bone resorption is shown in <strong>Figure 1 A-B</strong>. Abnormal bone remodeling activities were observed in osteoarthritic femur. When compared to control femur, significantly larger bone resorption rate was observed in the first week post collagenase injection in both the lateral (p < 0.01) and medial femur (p < 0.01), as shown in <strong>Figure 1 C-D</strong>.</div></div><div><h3>CONCLUSION</h3><div>This proof-of-concept study, for the first time, demonstr
{"title":"IN VIVO MICRO COMPUTED TOMOGRAPHY IMAGING ALLOWS LONGITUDINAL ASSESSMENT OF MULTI-SCALE CHANGES TO WHOLE JOINT WITH PROGRESSION OF OA","authors":"H. Liu, J.L. Gregory, M.O. Silva, C.E. Davey, K.S. Stok","doi":"10.1016/j.ostima.2025.100300","DOIUrl":"10.1016/j.ostima.2025.100300","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Longitudinal assessment of knee joint structure holds promise for providing invaluable spatial-temporal information on the progression of degenerative musculoskeletal (MSK) diseases involving the knee joint.</div></div><div><h3>OBJECTIVE</h3><div>This proof-of-concept study aims to establish a time-lapse <em>in vivo</em> imaging protocol with high temporal resolution to longitudinally track multi-scale structural changes, including mechanical alteration to whole joint structure, sensitive microstructural changes to subchondral bone, and abnormal bone remodeling activity, in a mouse collagenase-induced osteoarthritis (OA) model.</div></div><div><h3>METHODS</h3><div>Eight male C57BL/10 mice aged nine weeks were recruited and assigned to two longitudinal groups, control (CT) and OA. Of these, four ten-week-old mice assigned to the OA group received intra-articular injection of collagenase on the right knee to destabilize the right tibiofemoral joint. Longitudinal <em>in vivo</em> micro-computed tomography (microCT) scans were performed one day before collagenase injection and then weekly for eight weeks in total, resulting in nine scans for each animal. <em>In vivo</em> microCT (Scanco Medical) was performed with a source voltage of 70 kVp, an integration time of 350 <em>ms</em>, a current of 114 μ<em>A</em>, and an isotropic nominal resolution of 10.4 μ<em>m</em> with 1000 projections, with each scanning taking around 30 minutes. Quantitative morphometric analysis (QMA) was performed to measure longitudinal changes to structure of whole joint and subchondral bone, including joint space width (mm), and trabecular thickness (mm). Visualization of dynamic bone remodeling was performed by registering serial microCT scans. Bone resorption rate, BRR (%/day), and bone formation rate, BFR (%/day) were measured to quantify bone remodeling activity. To test the differences between CT and OA group at each time point from week 1 to week 8, a one-way analysis of covariance was used.</div></div><div><h3>RESULTS</h3><div>Three weeks post OA-induction, a significantly smaller joint space width was observed in medial osteoarthritic joint (202 μm), when compared to CT joint (228 μm) (p < 0.01). Regarding trabecular thickness, significant differences were observed at multiple time points between CT and OA groups, specifically in the first three weeks at the early stage of OA progression at lateral side (p < 0.01). Representative 3D visualization of bone formation and bone resorption is shown in <strong>Figure 1 A-B</strong>. Abnormal bone remodeling activities were observed in osteoarthritic femur. When compared to control femur, significantly larger bone resorption rate was observed in the first week post collagenase injection in both the lateral (p < 0.01) and medial femur (p < 0.01), as shown in <strong>Figure 1 C-D</strong>.</div></div><div><h3>CONCLUSION</h3><div>This proof-of-concept study, for the first time, demonstr","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ostima.2025.100349
W. Wirth , F. Eckstein
<div><h3>INTRODUCTION</h3><div>Automated cartilage segmentation using convolutional neural networks (CNN) has been shown to provide moderate to high accuracy in comparison with gold-standard manual approaches. It also displays similar sensitivity to longitudinal change and to between-group differences in change as has been reported for manual analysis [1-3]. Denuded areas of subchondral bone (dAB) provide challenges and impair the accuracy of automated cartilage segmentation in knees with severe radiographic OA (KLG 4). The reason is that CNNs are trained to detect cartilage, but encounter “difficulties” to properly segment areas where cartilage is lost entirely. CNNs therefore often segment cartilage cover in some areas of actual full thickness loss or ignore dABs entirely. This was observed to result in an overestimation of cartilage thickness and an underestimation of dABs in knees with severe OA [4].</div></div><div><h3>OBJECTIVE</h3><div>To improve CNN-based automated segmentation in severely osteoarthritic knee cartilage by using an automated post-processing algorithm that relies on a multi-atlas registration for reconstructing the total area of subchondral bone (tAB). We evaluate the agreement, accuracy and longitudinal sensitivity to cartilage change of this new methodology.</div></div><div><h3>METHODS</h3><div>Sagittal DESS and coronal FLASH MRIs were acquired by the Osteoarthritis Initiative (OAI). 2D U-Net models were trained for both MRI protocols using manual cartilage segmentations of knees with radiographic OA (KLG2-4, n training / validation set: 86/18 knees, baseline scans only) or severe radiographic OA (KLG4, n training/ validation set: 29/6 knees. These were trained either from baseline scans only [KLG4<sub>BL</sub>] or from baseline and follow-up scans [KLG4<sub>BL+FU</sub>]. The trained models were then applied to the test set comprising 10 KLG4 knees with manual cartilage segmentations from both DESS and FLASH MRI available and to n=125/14 knees with manual cartilage segmentations from either DESS or FLASH MRI available. Automated, registration-based post-processing was applied to reconstruct missing parts of the tAB and to refine the segmentations (Fig. 1), particularly in areas of denuded bone. The agreement and accuracy of automated cartilage analysis were evaluated in the test set for individual cartilages using Dice Similarity coefficients (DSC), correlation analysis, and by determining systematic offsets between manual and automated analysis. The sensitivity to one-year change was assessed using the standardized response mean (SRM) across the entire femorotibial joint in 104/24 (DESS/FLASH) knees with manual baseline and follow-up segmentations.</div></div><div><h3>RESULTS</h3><div>The strongest agreement (DSC 0.80±0.07 to 0.89±0.05) and lowest systematic offsets for cartilage thickness (1.2% to 8.5%) were observed for CNNs trained on KLG2-4 rather than KLG4 knees. Similar observations were made for dABs (-40.6% to 3.
{"title":"A FULLY-AUTOMATED TECHNIQUE FOR KNEE CARTILAGE AND DENUDED BONE AREA MORPHOMETRY IN SEVERE RADIOGRAPHIC KNEE OA – METHOD DEVELOPMENT AND VALIDATION","authors":"W. Wirth , F. Eckstein","doi":"10.1016/j.ostima.2025.100349","DOIUrl":"10.1016/j.ostima.2025.100349","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Automated cartilage segmentation using convolutional neural networks (CNN) has been shown to provide moderate to high accuracy in comparison with gold-standard manual approaches. It also displays similar sensitivity to longitudinal change and to between-group differences in change as has been reported for manual analysis [1-3]. Denuded areas of subchondral bone (dAB) provide challenges and impair the accuracy of automated cartilage segmentation in knees with severe radiographic OA (KLG 4). The reason is that CNNs are trained to detect cartilage, but encounter “difficulties” to properly segment areas where cartilage is lost entirely. CNNs therefore often segment cartilage cover in some areas of actual full thickness loss or ignore dABs entirely. This was observed to result in an overestimation of cartilage thickness and an underestimation of dABs in knees with severe OA [4].</div></div><div><h3>OBJECTIVE</h3><div>To improve CNN-based automated segmentation in severely osteoarthritic knee cartilage by using an automated post-processing algorithm that relies on a multi-atlas registration for reconstructing the total area of subchondral bone (tAB). We evaluate the agreement, accuracy and longitudinal sensitivity to cartilage change of this new methodology.</div></div><div><h3>METHODS</h3><div>Sagittal DESS and coronal FLASH MRIs were acquired by the Osteoarthritis Initiative (OAI). 2D U-Net models were trained for both MRI protocols using manual cartilage segmentations of knees with radiographic OA (KLG2-4, n training / validation set: 86/18 knees, baseline scans only) or severe radiographic OA (KLG4, n training/ validation set: 29/6 knees. These were trained either from baseline scans only [KLG4<sub>BL</sub>] or from baseline and follow-up scans [KLG4<sub>BL+FU</sub>]. The trained models were then applied to the test set comprising 10 KLG4 knees with manual cartilage segmentations from both DESS and FLASH MRI available and to n=125/14 knees with manual cartilage segmentations from either DESS or FLASH MRI available. Automated, registration-based post-processing was applied to reconstruct missing parts of the tAB and to refine the segmentations (Fig. 1), particularly in areas of denuded bone. The agreement and accuracy of automated cartilage analysis were evaluated in the test set for individual cartilages using Dice Similarity coefficients (DSC), correlation analysis, and by determining systematic offsets between manual and automated analysis. The sensitivity to one-year change was assessed using the standardized response mean (SRM) across the entire femorotibial joint in 104/24 (DESS/FLASH) knees with manual baseline and follow-up segmentations.</div></div><div><h3>RESULTS</h3><div>The strongest agreement (DSC 0.80±0.07 to 0.89±0.05) and lowest systematic offsets for cartilage thickness (1.2% to 8.5%) were observed for CNNs trained on KLG2-4 rather than KLG4 knees. Similar observations were made for dABs (-40.6% to 3.","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100349"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ostima.2025.100341
F. Boel , M.A. van den Berg , N.S. Riedstra , M.M.A. van Buuren , J. Tang , H. Ahedi , N. Arden , S.M.A. Bierma-Zeinstra , C.G. Boer , F.M. Cicuttini , T.F. Cootes , K.M. Crossley , D.T. Felson , W.P. Gielis , J.J. Heerey , G. Jones , S. Kluzek , N.E. Lane , C. Lindner , J.A. Lynch , R. Agricola
<div><h3>INTRODUCTION</h3><div>Hip morphology has been recognized as an important risk factor for the development of hip OA. In previous studies within the Worldwide Collaboration on OsteoArthritis prediCtion for the Hip consortium (World COACH), both acetabular dysplasia (AD) and pincer morphology–characterized by acetabular under- and overcoverage of the femoral head–were associated with the development of radiographic hip OA (RHOA) within 4-8 years, with an odds ratio (OR) of 1.80 (95% confidence interval (CI) 1.40-2.34) and 1.50 (95% CI 1.05-2.15), respectively. However, we know that not everyone with AD or pincer morphology will develop RHOA. Specific baseline characteristics or variations in hip shape among individuals with AD and pincer morphology may influence their risk of developing RHOA. Statistical shape models (SSM), describing the mean hip shape of a population and a range of independent shape variations, can be utilized to study these variations in hip shape.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate whether specific hip shape variations or baseline characteristics within individuals with either AD or pincer morphology are associated with the development of RHOA within 4-8 years.</div></div><div><h3>METHODS</h3><div>We pooled individual participant data from seven prospective cohort studies within the World COACH consortium. Standardized anteroposterior (AP) pelvic radiographs were obtained at baseline and within 4-8 years follow-up. RHOA was scored by KLG or (modified) Croft grade. We harmonized the RHOA scores into “No OA” (KLG/Croft = 0), “doubtful OA” (KLG/Croft = 1), or “definite OA” (KLG/Croft ≥ 2 or total hip replacement). The Wiberg center edge angle (WCEA), measuring the weight-bearing femoral head coverage, and the lateral center edge angle (LCEA), measuring the bony femoral head coverage, were automatically determined using a validated method. Hips were included if they had baseline and follow-up RHOA scores, no RHOA at baseline, and either AD defined by a WCEA ≤ 25° or pincer morphology defined by a LCEA ≥45°. For both populations, an SSM was created of the acetabular roof, posterior wall, femoral head and neck, and teardrop (Fig 1). We analyzed the first 13 shape modes that explained around 90% of total shape variation in the population. The association between each shape mode, sex, baseline age, BMI, diabetes and smoking habits, and the development of RHOA was estimated using univariate generalized linear mixed-effects models. The mixed effects were added to account for the potential clustering within cohorts and participants. The results were expressed as ORs with 95% CIs.</div></div><div><h3>RESULTS</h3><div>The AD population consisted of 4,737 hips, of which 2.6% developed incident RHOA (Table 1). Four of the 13 shape modes (Fig 1) were associated with the development of RHOA. Additionally, in hips with AD, females had higher odds of incident RHOA than males (OR 2.85, 95% CI 1.46 – 5.58), and each year inc
髋关节形态已被认为是髋关节骨关节炎发生的重要危险因素。在全球髋关节骨关节炎预测合作联盟（World COACH）之前的研究中，髋臼发育不良（AD）和钳形（以髋臼股骨头覆盖不足和过度为特征）与4-8年内髋关节骨性关节炎（RHOA）的发生相关，比值比（OR）分别为1.80（95%可信区间（CI） 1.40-2.34）和1.50 （95% CI 1.05-2.15）。然而，我们知道不是每个患有AD或钳形形态的人都会发展RHOA。特定的基线特征或AD和钳形形态个体的臀部形状变化可能影响他们发展RHOA的风险。统计形状模型（SSM）描述了一个群体的平均臀部形状和一系列独立的形状变化，可以用来研究臀部形状的这些变化。目的评估AD或钳形形态患者的特定髋关节形状变化或基线特征是否与4-8年内RHOA的发生有关。方法：我们汇集了来自世界COACH联盟的7项前瞻性队列研究的个体参与者数据。在基线和4-8年随访期间获得标准化骨盆正位（AP） x线片。RHOA采用KLG或（改良的）Croft评分。我们将RHOA评分统一为“无OA”（KLG/Croft = 0）、“可疑OA”（KLG/Croft = 1）或“明确OA”（KLG/Croft≥2或全髋关节置换术）。采用经过验证的方法自动确定Wiberg中心边缘角（WCEA）和外侧中心边缘角（LCEA），分别用于测量负重股骨头覆盖率和骨股骨头覆盖率。如果髋关节有基线和随访的RHOA评分，基线时无RHOA，且WCEA≤25°定义的AD或LCEA≥45°定义的钳形形态，则纳入髋部。对于这两组患者，对髋臼顶、后壁、股骨头、颈和泪滴进行SSM（图1）。我们分析了前13种形状模式，它们解释了种群中约90%的总形状变化。使用单变量广义线性混合效应模型估计每种体型模式、性别、基线年龄、BMI、糖尿病和吸烟习惯与RHOA发展之间的关系。加入混合效应是为了解释在队列和参与者中潜在的聚类。结果以or表示，ci为95%。结果AD人群包括4737例髋关节，其中2.6%发生了RHOA（表1）。13种形状模式中的4种（图1）与RHOA的发展有关。此外，在患有AD的髋关节中，女性发生RHOA的几率高于男性（OR 2.85, 95% CI 1.46 - 5.58），并且基线年龄的逐年增加与RHOA发生的几率升高相关（OR 1.05, 95% CI 1.02 - 1.09）。基线BMI、糖尿病和吸烟习惯都与AD患者的RHOA无关。钳形人群包括1118髋，其中2.8%发生偶发RHOA。只有一种形状模式与入射RHOA相关（图1）。性别、基线年龄、BMI、糖尿病和吸烟习惯与钳形形态患者的RHOA无关。结论AD患者的形状和钳形形态的差异与RHOA的发生几率有关。在AD患者中，性别和基线年龄也与RHOA的发生有关。然而，在钳形形态的患者中没有观察到这种情况。这些发现可能为髋关节骨关节炎的个性化风险评估工具和预防策略的发展提供信息。
{"title":"BEYOND ACETABULAR DYSPLASIA AND PINCER MORPHOLOGY: REFINING HIP OSTEOARTHRITIS RISK ASSESSMENT THROUGH STATISTICAL SHAPE MODELING","authors":"F. Boel , M.A. van den Berg , N.S. Riedstra , M.M.A. van Buuren , J. Tang , H. Ahedi , N. Arden , S.M.A. Bierma-Zeinstra , C.G. Boer , F.M. Cicuttini , T.F. Cootes , K.M. Crossley , D.T. Felson , W.P. Gielis , J.J. Heerey , G. Jones , S. Kluzek , N.E. Lane , C. Lindner , J.A. Lynch , R. Agricola","doi":"10.1016/j.ostima.2025.100341","DOIUrl":"10.1016/j.ostima.2025.100341","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Hip morphology has been recognized as an important risk factor for the development of hip OA. In previous studies within the Worldwide Collaboration on OsteoArthritis prediCtion for the Hip consortium (World COACH), both acetabular dysplasia (AD) and pincer morphology–characterized by acetabular under- and overcoverage of the femoral head–were associated with the development of radiographic hip OA (RHOA) within 4-8 years, with an odds ratio (OR) of 1.80 (95% confidence interval (CI) 1.40-2.34) and 1.50 (95% CI 1.05-2.15), respectively. However, we know that not everyone with AD or pincer morphology will develop RHOA. Specific baseline characteristics or variations in hip shape among individuals with AD and pincer morphology may influence their risk of developing RHOA. Statistical shape models (SSM), describing the mean hip shape of a population and a range of independent shape variations, can be utilized to study these variations in hip shape.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate whether specific hip shape variations or baseline characteristics within individuals with either AD or pincer morphology are associated with the development of RHOA within 4-8 years.</div></div><div><h3>METHODS</h3><div>We pooled individual participant data from seven prospective cohort studies within the World COACH consortium. Standardized anteroposterior (AP) pelvic radiographs were obtained at baseline and within 4-8 years follow-up. RHOA was scored by KLG or (modified) Croft grade. We harmonized the RHOA scores into “No OA” (KLG/Croft = 0), “doubtful OA” (KLG/Croft = 1), or “definite OA” (KLG/Croft ≥ 2 or total hip replacement). The Wiberg center edge angle (WCEA), measuring the weight-bearing femoral head coverage, and the lateral center edge angle (LCEA), measuring the bony femoral head coverage, were automatically determined using a validated method. Hips were included if they had baseline and follow-up RHOA scores, no RHOA at baseline, and either AD defined by a WCEA ≤ 25° or pincer morphology defined by a LCEA ≥45°. For both populations, an SSM was created of the acetabular roof, posterior wall, femoral head and neck, and teardrop (Fig 1). We analyzed the first 13 shape modes that explained around 90% of total shape variation in the population. The association between each shape mode, sex, baseline age, BMI, diabetes and smoking habits, and the development of RHOA was estimated using univariate generalized linear mixed-effects models. The mixed effects were added to account for the potential clustering within cohorts and participants. The results were expressed as ORs with 95% CIs.</div></div><div><h3>RESULTS</h3><div>The AD population consisted of 4,737 hips, of which 2.6% developed incident RHOA (Table 1). Four of the 13 shape modes (Fig 1) were associated with the development of RHOA. Additionally, in hips with AD, females had higher odds of incident RHOA than males (OR 2.85, 95% CI 1.46 – 5.58), and each year inc","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100341"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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