<div><h3>INTRODUCTION</h3><div>Stemless humeral head components have emerged as a popular choice for patients undergoing shoulder arthroplasty for end-stage OA since they preserve non-diseased bone for future surgical revisions. Current pre-operative clinical measures are limited in assessing volumetric bone mineral density (vBMD) and mechanical properties in the region of bone directly supporting the component. Gold-standard phantom calibration, used to determine vBMD in CT images, is seldom utilized in clinical practice requiring alternative density measures for accurate vBMD. Internal density calibration using internal tissues as references has yet to be validated in the proximal humerus, and vBMD values have yet to be linked to finite element model (FEM) estimated stiffness in the context of stemless shoulder arthroplasty.</div></div><div><h3>OBJECTIVE</h3><div>1) To determine the correlation between vBMD and finite element model (FEM) estimated stiffness 2) To determine the bias in internal density-based vBMD using three different referent tissue combinations compared to phantom-based vBMD, in the proximal humerus.</div></div><div><h3>METHODS</h3><div>Non-pathologic cadaveric single-energy CT images (n = 25), containing a K<sub>2</sub>HPO<sub>4</sub> phantom, were used to analyze a 10 mm region directly below the anatomic neck. Phantom-based vBMD was calculated for each region and used as input to image-based FEMs (ROD). Internal calibration used air (A), adipose (A), skeletal muscle (M), and cortical bone (C) to generate calibrated images from three different referent tissue combinations (AACM, ACM, AAC). Images were used to generate FEMs for each tissue combination. Results were compared between vBMD (mg K<sub>2</sub>HPO<sub>4</sub>/cc) and apparent modulus (E<sub>app</sub>) for each internal calibration tissue combination to the phantom calibration using linear regression. Bland-Altman analysis was used to determine the agreement between tissue combination and phantom calibration for estimated stiffness values (E<sub>app</sub>).</div></div><div><h3>RESULTS</h3><div>Linear regression (Figure 1) showed strong correlations between estimated stiffness and vBMD values for each calibration method (AACM R<sup>2</sup> = 0.7524; ACM R<sup>2</sup> = 0.7723; AAC R<sup>2</sup> = 0.7384; ROD R<sup>2</sup> = 0.7854) and slopes not significantly different from 1 (p < 0.001). Bland-Altman analysis (Figure 2) revealed the ACM tissue combination had the lowest error bounds in apparent modulus, compared to phantom-vBMD derived FEMs, with a mean bias of 80.15 MPa and 95% limits of agreement ranging from -164.55 to 324.86 MPa.</div></div><div><h3>CONCLUSION</h3><div>The results of this study support the use of internal density calibration as a valid method for using internal density calibrated images as input to FEMs for estimating stiffness in the proximal humerus. The ACM tissue combination provided the highest agreement with the gold standard phantom ca
{"title":"VALIDATING INTERNAL DENSITY CALIBRATION IN THE PROXIMAL HUMERUS TO ESTIMATE BONE STIFFNESS FOR STEMLESS SHOULDER ARTHROPLASTY","authors":"C.K.A. Stiles , B.E. Matheson , S.K. Boyd , G.S. Arthwal , J.P. Callaghan , C.R. Dickerson , N.K. Knowles","doi":"10.1016/j.ostima.2025.100321","DOIUrl":"10.1016/j.ostima.2025.100321","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Stemless humeral head components have emerged as a popular choice for patients undergoing shoulder arthroplasty for end-stage OA since they preserve non-diseased bone for future surgical revisions. Current pre-operative clinical measures are limited in assessing volumetric bone mineral density (vBMD) and mechanical properties in the region of bone directly supporting the component. Gold-standard phantom calibration, used to determine vBMD in CT images, is seldom utilized in clinical practice requiring alternative density measures for accurate vBMD. Internal density calibration using internal tissues as references has yet to be validated in the proximal humerus, and vBMD values have yet to be linked to finite element model (FEM) estimated stiffness in the context of stemless shoulder arthroplasty.</div></div><div><h3>OBJECTIVE</h3><div>1) To determine the correlation between vBMD and finite element model (FEM) estimated stiffness 2) To determine the bias in internal density-based vBMD using three different referent tissue combinations compared to phantom-based vBMD, in the proximal humerus.</div></div><div><h3>METHODS</h3><div>Non-pathologic cadaveric single-energy CT images (n = 25), containing a K<sub>2</sub>HPO<sub>4</sub> phantom, were used to analyze a 10 mm region directly below the anatomic neck. Phantom-based vBMD was calculated for each region and used as input to image-based FEMs (ROD). Internal calibration used air (A), adipose (A), skeletal muscle (M), and cortical bone (C) to generate calibrated images from three different referent tissue combinations (AACM, ACM, AAC). Images were used to generate FEMs for each tissue combination. Results were compared between vBMD (mg K<sub>2</sub>HPO<sub>4</sub>/cc) and apparent modulus (E<sub>app</sub>) for each internal calibration tissue combination to the phantom calibration using linear regression. Bland-Altman analysis was used to determine the agreement between tissue combination and phantom calibration for estimated stiffness values (E<sub>app</sub>).</div></div><div><h3>RESULTS</h3><div>Linear regression (Figure 1) showed strong correlations between estimated stiffness and vBMD values for each calibration method (AACM R<sup>2</sup> = 0.7524; ACM R<sup>2</sup> = 0.7723; AAC R<sup>2</sup> = 0.7384; ROD R<sup>2</sup> = 0.7854) and slopes not significantly different from 1 (p < 0.001). Bland-Altman analysis (Figure 2) revealed the ACM tissue combination had the lowest error bounds in apparent modulus, compared to phantom-vBMD derived FEMs, with a mean bias of 80.15 MPa and 95% limits of agreement ranging from -164.55 to 324.86 MPa.</div></div><div><h3>CONCLUSION</h3><div>The results of this study support the use of internal density calibration as a valid method for using internal density calibrated images as input to FEMs for estimating stiffness in the proximal humerus. The ACM tissue combination provided the highest agreement with the gold standard phantom ca","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100321"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523456","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}
<div><h3>INTRODUCTION</h3><div>Traumatic BM lesions occur in about 80% of ACL injuries, typically caused by tibia-femur collisions, indicating significant joint damage and an increased risk of post-traumatic OA (PTOA). MRI is effective for detecting BM lesions, but quantitative assessment of their volume and distribution over time can help identify PTOA risk factors. While BM lesions typically resolve over time, their relationship with knee pain and functional outcomes remains unclear.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to investigate the longitudinal prevalence, characteristics, and progression of BM lesions following ACL injury, with a focus on their association with knee pain, ligamentous injuries, and meniscal tears.</div></div><div><h3>METHODS</h3><div>This prospective observational study analyzed data from 100 individuals (68 females, 32 males) with acute ACL tears in previously uninjured knees. MRI scans were obtained within 6 weeks of their injury using a 1.5-T MR scanner (GE OptimaMR430S, 1.5T, Waukesha, WI, USA). The imaging protocol included T2‐weighted fat‐suppressed fast spin echo images [TR/TE, 4300/56 ms; echo train length, 11; matrix, 320 × 256; field of view, 140 mm; slice thickness, 3.5 mm; gap, 0.3 mm;] for evaluating BM lesions. BM lesion volume quantified using a previously developed automated segmentation tool. Knee pain and symptoms were assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS). Statistical analyses included paired t-tests, Mann-Whitney U tests, Pearson’s Chi-squared test, and Spearman’s rank correlation. Multiple comparisons were corrected using the Benjamini-Hochberg procedure to control for false discovery rate. A subset of 77 participants completed follow-up KOOS surveys, and 19 participants who did not undergo ACL reconstruction had follow-up MRIs at one year.</div></div><div><h3>RESULTS</h3><div>BM lesions were present in 95% of participants (N=100), predominantly in the lateral tibial plateau and lateral femoral condyle. Males exhibited significantly higher BM lesion volumes than females (p = 0.03). Significant associations were identified between medial collateral ligament tears and both lateral collateral ligament (p = 0.01) and posterior cruciate ligament tears (p < 0.01). The BM lesion volume at baseline was negatively correlated with KOOS Symptoms at baseline (r = -0.270, p = 0.01). Longitudinal analyses revealed strong predictive relationships between baseline KOOS scores and future outcomes, with baseline KOOS Pain predicting follow-up Symptoms (r = 0.500) and Pain (r = 0.542). At the one-year follow-up, BM lesions in non-surgical participants (N=19) showed substantial resolution (mean change = -96.7%). Surgery had no significant impact on pain or functional outcomes compared to non-surgical participants.</div></div><div><h3>CONCLUSION</h3><div>BM lesion volume had only a weak association with knee pain after ACL injury, but longitudinal KOOS analyses revea
{"title":"LONGITUDINAL PROGRESSION OF TRAUMATIC BONE MARROW LESIONS FOLLOWING ANTERIOR CRUCIATE LIGAMENT INJURY: ASSOCIATIONS WITH KNEE PAIN AND CONCOMITANT INJURIES","authors":"C.E. Stirling , N. Pavlovic , S.L. Manske , R.E.A. Walker , S.K. Boyd","doi":"10.1016/j.ostima.2025.100323","DOIUrl":"10.1016/j.ostima.2025.100323","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Traumatic BM lesions occur in about 80% of ACL injuries, typically caused by tibia-femur collisions, indicating significant joint damage and an increased risk of post-traumatic OA (PTOA). MRI is effective for detecting BM lesions, but quantitative assessment of their volume and distribution over time can help identify PTOA risk factors. While BM lesions typically resolve over time, their relationship with knee pain and functional outcomes remains unclear.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to investigate the longitudinal prevalence, characteristics, and progression of BM lesions following ACL injury, with a focus on their association with knee pain, ligamentous injuries, and meniscal tears.</div></div><div><h3>METHODS</h3><div>This prospective observational study analyzed data from 100 individuals (68 females, 32 males) with acute ACL tears in previously uninjured knees. MRI scans were obtained within 6 weeks of their injury using a 1.5-T MR scanner (GE OptimaMR430S, 1.5T, Waukesha, WI, USA). The imaging protocol included T2‐weighted fat‐suppressed fast spin echo images [TR/TE, 4300/56 ms; echo train length, 11; matrix, 320 × 256; field of view, 140 mm; slice thickness, 3.5 mm; gap, 0.3 mm;] for evaluating BM lesions. BM lesion volume quantified using a previously developed automated segmentation tool. Knee pain and symptoms were assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS). Statistical analyses included paired t-tests, Mann-Whitney U tests, Pearson’s Chi-squared test, and Spearman’s rank correlation. Multiple comparisons were corrected using the Benjamini-Hochberg procedure to control for false discovery rate. A subset of 77 participants completed follow-up KOOS surveys, and 19 participants who did not undergo ACL reconstruction had follow-up MRIs at one year.</div></div><div><h3>RESULTS</h3><div>BM lesions were present in 95% of participants (N=100), predominantly in the lateral tibial plateau and lateral femoral condyle. Males exhibited significantly higher BM lesion volumes than females (p = 0.03). Significant associations were identified between medial collateral ligament tears and both lateral collateral ligament (p = 0.01) and posterior cruciate ligament tears (p < 0.01). The BM lesion volume at baseline was negatively correlated with KOOS Symptoms at baseline (r = -0.270, p = 0.01). Longitudinal analyses revealed strong predictive relationships between baseline KOOS scores and future outcomes, with baseline KOOS Pain predicting follow-up Symptoms (r = 0.500) and Pain (r = 0.542). At the one-year follow-up, BM lesions in non-surgical participants (N=19) showed substantial resolution (mean change = -96.7%). Surgery had no significant impact on pain or functional outcomes compared to non-surgical participants.</div></div><div><h3>CONCLUSION</h3><div>BM lesion volume had only a weak association with knee pain after ACL injury, but longitudinal KOOS analyses revea","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100323"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523427","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.2025.100318
D. Sherman , J. Stefanik , A. Guermazi , W. Issa , X. He , A.W. Jang , F. Liu , M. Jarraya
<div><h3>INTRODUCTION</h3><div>Arthrogenic muscle inhibition (AMI) is a neuromuscular impairment that is commonly described in patients after knee joint injuries and surgeries. AMI is characterized by profound quadriceps muscle atrophy and persistent muscle weakness secondary to neural inhibition of motor pathways due to altered afferent feedback. While AMI is well-recognized in rehabilitation research, there is a critical lack of standard clinical diagnostic criteria limiting rehabilitation practitioners’ ability to prescribe treatments. In this context, MRI can be a helpful adjunct tool to neurophysiological testing by identifying joint pathology causing AMI and quadriceps muscle inhibition resulting from it.</div></div><div><h3>OBJECTIVE</h3><div>Describe MRI and neurophysiological findings of the knee joint and thighs among patients with AMI secondary to knee injury or surgery.</div></div><div><h3>MEHTODS</h3><div>Four patients with marked quadriceps weakness (presumed AMI) following knee joint injury or surgery are presented. All patients had MR imaging data, including two with unilateral thigh MRI (Patients A-B), 1 with unilateral knee and thigh MRI (Patient C), and 1 with bilateral knee and thigh MRIs, as well as neurophysiological testing (Patient D). Neurophysiological testing included muscle activation failure, Hoffman stretch reflex testing, and cortical inhibition using peripheral nerve and transcranial magnetic stimulation techniques. All imaging data was acquired 12-16 weeks post knee injury or surgery.</div></div><div><h3>RESULTS</h3><div>Patients A-C (each 12-14 weeks status-post ACL reconstruction, uni-compartment arthroplasty, and arthroscopic drilling, respectively) present with marked quadriceps volume loss and diffuse increased T2 signal, resembling denervation edema (<strong>Figure 1</strong>). Patient C, who underwent arthroscopic drilling, had osteochondral fracture prior to surgery which worsened on the postoperative imaging. Patient D (12-16 weeks post soccer injury) presented with osteochondral fracture of the lateral trochlea with marked atrophy of the quadriceps muscle (<strong>Figure 2A-B</strong>). Neurophysiological testing revealed volitional quadricep activation failure (51%, <strong>Figure 2C</strong>), as well as intracortical inhibition (37%, <strong>Figure 2D</strong>), afferent inhibition (81%, <strong>Figure 2E</strong>), and Hoffmann reflex facilitation on the involved limb (cf. 29%, <strong>Figure 2F</strong> vs. 15%, <strong>Figure 2G</strong>). These findings suggest a cortically mediated muscle activation failure and paradoxical reflex facilitation to preserve strength (spinal cord involvement). The absence of denervation edema could be plausibly explained by the central nervous involvement rather than a peripheral nerve or neuromuscular problem.</div></div><div><h3>CONCLUSION</h3><div>These cases highlight the value of combined MR imaging and neurophysiological assessment in AMI. The presence of dener
{"title":"CAN COMBINED NEUROPHYSIOLOGICAL AND MRI EVALUATION HELP GAIN NEW INSIGHTS IN ARTHROGENIC MUSCLE INHIBITION AMONG PATIENTS WITH KNEE PAIN? PROOF OF CONCEPT","authors":"D. Sherman , J. Stefanik , A. Guermazi , W. Issa , X. He , A.W. Jang , F. Liu , M. Jarraya","doi":"10.1016/j.ostima.2025.100318","DOIUrl":"10.1016/j.ostima.2025.100318","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Arthrogenic muscle inhibition (AMI) is a neuromuscular impairment that is commonly described in patients after knee joint injuries and surgeries. AMI is characterized by profound quadriceps muscle atrophy and persistent muscle weakness secondary to neural inhibition of motor pathways due to altered afferent feedback. While AMI is well-recognized in rehabilitation research, there is a critical lack of standard clinical diagnostic criteria limiting rehabilitation practitioners’ ability to prescribe treatments. In this context, MRI can be a helpful adjunct tool to neurophysiological testing by identifying joint pathology causing AMI and quadriceps muscle inhibition resulting from it.</div></div><div><h3>OBJECTIVE</h3><div>Describe MRI and neurophysiological findings of the knee joint and thighs among patients with AMI secondary to knee injury or surgery.</div></div><div><h3>MEHTODS</h3><div>Four patients with marked quadriceps weakness (presumed AMI) following knee joint injury or surgery are presented. All patients had MR imaging data, including two with unilateral thigh MRI (Patients A-B), 1 with unilateral knee and thigh MRI (Patient C), and 1 with bilateral knee and thigh MRIs, as well as neurophysiological testing (Patient D). Neurophysiological testing included muscle activation failure, Hoffman stretch reflex testing, and cortical inhibition using peripheral nerve and transcranial magnetic stimulation techniques. All imaging data was acquired 12-16 weeks post knee injury or surgery.</div></div><div><h3>RESULTS</h3><div>Patients A-C (each 12-14 weeks status-post ACL reconstruction, uni-compartment arthroplasty, and arthroscopic drilling, respectively) present with marked quadriceps volume loss and diffuse increased T2 signal, resembling denervation edema (<strong>Figure 1</strong>). Patient C, who underwent arthroscopic drilling, had osteochondral fracture prior to surgery which worsened on the postoperative imaging. Patient D (12-16 weeks post soccer injury) presented with osteochondral fracture of the lateral trochlea with marked atrophy of the quadriceps muscle (<strong>Figure 2A-B</strong>). Neurophysiological testing revealed volitional quadricep activation failure (51%, <strong>Figure 2C</strong>), as well as intracortical inhibition (37%, <strong>Figure 2D</strong>), afferent inhibition (81%, <strong>Figure 2E</strong>), and Hoffmann reflex facilitation on the involved limb (cf. 29%, <strong>Figure 2F</strong> vs. 15%, <strong>Figure 2G</strong>). These findings suggest a cortically mediated muscle activation failure and paradoxical reflex facilitation to preserve strength (spinal cord involvement). The absence of denervation edema could be plausibly explained by the central nervous involvement rather than a peripheral nerve or neuromuscular problem.</div></div><div><h3>CONCLUSION</h3><div>These cases highlight the value of combined MR imaging and neurophysiological assessment in AMI. The presence of dener","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524029","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.2025.100354
R. van Paassen , N. Tumer , J. Hirvasniemi , T.M. Piscaer , A.A. Zadpoor , S. Klein , S.M.A. Bierma-Zeinstra , E.H.G. Oei , M. van Middelkoop
<div><h3>INTRODUCTION</h3><div>High levels of physical activity or high body mass index (BMI) during growth may negatively influence bone and cartilage, but little is known about how loading relates to the shape of the patella and femur. It is well established that bone shape is primarily determined during growth, and specific variations in bone shape are associated with a higher risk of osteoarthritis (OA). Therefore, we aim to identify the association between the 3D shape of the patella and femur bones and loading factors (i.e., body mass index (BMI) and sports participation) in young adolescents.</div></div><div><h3>OBJECTIVE</h3><div>Our objective is twofold: 1) to determine the differences in bone shape between boys and girls, and 2) to determine which bone shape variations are associated with loading parameters (i.e., BMI and sports participation).</div></div><div><h3>METHODS</h3><div>Data from 1912 participants, aged 14.1 (SD: 0.67), standardized BMI for age and sex (BMI-SDS) of 0.42 (1.20), were retrieved from the Generation R study. The Generation R study is a large population cohort study that follows children from fetal life until adulthood. A subset of participants who underwent knee MRI (3.0T, Discovery MR750w, GE Healthcare, Milwaukee, WI, USA) at the 13-year follow-up time point were included in the current study. Imaging was performed with two knees in full extension using a water excitation Gradient Recalled Acquisition in Steady State sequence. Patellae and distal femora were automatically segmented using a method that combines multi-atlas and appearance models. Two statistical shape models (SSM) were built based on the automatically segmented left and right patellae and femora. Shape modes explaining at least 1% of the total population variation were included in the analyses. Differences between boys and girls were determined using a 2-sample T-test. Generalized estimating equation models, separate for boys and girls, were used to analyze the association between BMI-SDS, sports participation (yes or no), and shape variation. Bonferroni correction was used to correct for multiple testing.</div></div><div><h3>RESULTS</h3><div>A total of 3638 patellae and 3355 femora were included in the shape models. Eleven patellar and fourteen femoral shape modes explained at least 1% of the total variation and were retained for analysis. Eight out of the eleven (modes 1-4, 6, 8, 10, and 11) patellar and twelve out of the fourteen (modes 1-10, 12, and 14) femoral shape modes showed significant differences between boys and girls. Four patella and two femur modes were significantly associated with BMI in both boys and girls, while four patella and seven femur modes were significantly associated in either boys or girls only (Table 1). Patella shape mode 1 was significantly associated with sports participation in both boys and girls, as well as BMI in boys only. Femur shape mode 1 was associated with sports participation in girls and BMI in both bo
{"title":"PATELLAR AND FEMORAL BONE MORPHOLOGY AND ITS ASSOCIATION WITH LOADING IN YOUNG ADOLESCENT BOYS AND GIRLS","authors":"R. van Paassen , N. Tumer , J. Hirvasniemi , T.M. Piscaer , A.A. Zadpoor , S. Klein , S.M.A. Bierma-Zeinstra , E.H.G. Oei , M. van Middelkoop","doi":"10.1016/j.ostima.2025.100354","DOIUrl":"10.1016/j.ostima.2025.100354","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>High levels of physical activity or high body mass index (BMI) during growth may negatively influence bone and cartilage, but little is known about how loading relates to the shape of the patella and femur. It is well established that bone shape is primarily determined during growth, and specific variations in bone shape are associated with a higher risk of osteoarthritis (OA). Therefore, we aim to identify the association between the 3D shape of the patella and femur bones and loading factors (i.e., body mass index (BMI) and sports participation) in young adolescents.</div></div><div><h3>OBJECTIVE</h3><div>Our objective is twofold: 1) to determine the differences in bone shape between boys and girls, and 2) to determine which bone shape variations are associated with loading parameters (i.e., BMI and sports participation).</div></div><div><h3>METHODS</h3><div>Data from 1912 participants, aged 14.1 (SD: 0.67), standardized BMI for age and sex (BMI-SDS) of 0.42 (1.20), were retrieved from the Generation R study. The Generation R study is a large population cohort study that follows children from fetal life until adulthood. A subset of participants who underwent knee MRI (3.0T, Discovery MR750w, GE Healthcare, Milwaukee, WI, USA) at the 13-year follow-up time point were included in the current study. Imaging was performed with two knees in full extension using a water excitation Gradient Recalled Acquisition in Steady State sequence. Patellae and distal femora were automatically segmented using a method that combines multi-atlas and appearance models. Two statistical shape models (SSM) were built based on the automatically segmented left and right patellae and femora. Shape modes explaining at least 1% of the total population variation were included in the analyses. Differences between boys and girls were determined using a 2-sample T-test. Generalized estimating equation models, separate for boys and girls, were used to analyze the association between BMI-SDS, sports participation (yes or no), and shape variation. Bonferroni correction was used to correct for multiple testing.</div></div><div><h3>RESULTS</h3><div>A total of 3638 patellae and 3355 femora were included in the shape models. Eleven patellar and fourteen femoral shape modes explained at least 1% of the total variation and were retained for analysis. Eight out of the eleven (modes 1-4, 6, 8, 10, and 11) patellar and twelve out of the fourteen (modes 1-10, 12, and 14) femoral shape modes showed significant differences between boys and girls. Four patella and two femur modes were significantly associated with BMI in both boys and girls, while four patella and seven femur modes were significantly associated in either boys or girls only (Table 1). Patella shape mode 1 was significantly associated with sports participation in both boys and girls, as well as BMI in boys only. Femur shape mode 1 was associated with sports participation in girls and BMI in both bo","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100354"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524181","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.2025.100297
C. Knight , F. Eckstein , W. Wirth , C. Clemmensen , W. Ma , A. Collins , S. Basnet
<div><h3>INTRODUCTION</h3><div>A putative disease-modifying osteoarthritis drug, “Sprifermin” was studied by a phase 2B RCT (FORWARD - NCT01919164). Given at a 100µg dose, sprifermin increased cartilage thickness, both in absolute terms and compared with placebo [1]. In the full cohort with MRI results (mITT), this effect did, however, not lead to a pain relief greater than placebo [1]. FORWARD contained patients with a variety of radiographic disease stages (KLG 2 or 3, with a medial minimum joint space width (JSW) > 2.5mm). Although all patients had to display > 40mm pain levels at screening, not all of them exceeded that threshold at the actual baseline measurement [2].</div></div><div><h3>OBJECTIVE</h3><div>To elucidate post-hoc whether structural treatment effects on cartilage (thickness) by sprifermin differ between severity strata of symptoms (WOMAC) and radiographic disease status. These observations may inform future clinical trials at which stage (sprifermin-) treatment is structurally and symptomatically most effective.</div></div><div><h3>METHODS</h3><div>Total femorotibial joint (TFTJ) cartilage thickness change at year (Y) 2 by MRI represented the primary endpoint; WOMAC pain was secondary [1]. Patients aged 40–85 with primary symptomatic TFTJ OA (KLG 2 or 3; medial mJSW ≥2.5 mm) were studied. Cartilage thickness was determined quantitatively from 1.5-3T MRI by expert readers, using proprietary software (Chondrometrics). The analysis focused on the 2Y MRI TFTJ cartilage thickness change for the two highest sprifermin dose groups (100µg given every 6 or 12 months combined) vs. placebo. The Hedges G (sample-size-independent effect size measure) was determined, with 95% CIs obtained by bootstrapping. We studied the modified intent to treat cohort with 24-month data (mITT), and the so-called “subcohort at risk” (SAR)[2] a subgroup with baseline WOMAC pain >40 and more severe radiographic involvement by mJSW criteria.</div></div><div><h3>RESULTS</h3><div>Of 549 FORWARD patients randomized, 474 completed 2Y follow-up. 69% of the mITT with 24M data were female; the median age was 67 and the medial BMI 29.6. The treatment effect on cartilage thickness was 45.6µm for the mITT (Hedges G=0.63). Participants with baseline WOMAC pain ≥ the median displayed a somewhat smaller effect (17±70 µm change over 2 years in treated vs. -15±55µm in placebo participants; Hedges G =0.49 [0.11, 0.86] than those with pain < median (37±69µm in treated vs. -27±84µm in placebo participants; Hedges G =0.86 [0.47, 1.25].</div><div>KLG2 subjects displayed a marginally greater treatment effect (30±75µm in treated vs. -16±46µm in placebo participants; Hedges G =0.68 [0.36, 1.00]) than KLG3 participants (13±68µm in treated vs. -33±110µm in placebo participants; Hedges G =0.55 [0.07, 1.03]). Those with JSN grade 0 (no JSN) showed a stronger treatment effect (50±59µm in those treated vs. -11±45µm in placebo participants; Hedges G =1.09 [0.63, 1.54] than those
{"title":"STRUCTURAL EFFICACY OF INTRA-ARTICULAR SPRIFERMIN TREATMENT ON KNEE OSTEO-ARTHRITIS AS A FUNCTION OF SYMPTOMATIC AND RADIOGRAPHIC DISEASE SEVERITY - A POST-HOC ANALYSIS FROM THE FORWARD PHASE 2 RANDOMIZED CONTROLLED TRIAL","authors":"C. Knight , F. Eckstein , W. Wirth , C. Clemmensen , W. Ma , A. Collins , S. Basnet","doi":"10.1016/j.ostima.2025.100297","DOIUrl":"10.1016/j.ostima.2025.100297","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>A putative disease-modifying osteoarthritis drug, “Sprifermin” was studied by a phase 2B RCT (FORWARD - NCT01919164). Given at a 100µg dose, sprifermin increased cartilage thickness, both in absolute terms and compared with placebo [1]. In the full cohort with MRI results (mITT), this effect did, however, not lead to a pain relief greater than placebo [1]. FORWARD contained patients with a variety of radiographic disease stages (KLG 2 or 3, with a medial minimum joint space width (JSW) > 2.5mm). Although all patients had to display > 40mm pain levels at screening, not all of them exceeded that threshold at the actual baseline measurement [2].</div></div><div><h3>OBJECTIVE</h3><div>To elucidate post-hoc whether structural treatment effects on cartilage (thickness) by sprifermin differ between severity strata of symptoms (WOMAC) and radiographic disease status. These observations may inform future clinical trials at which stage (sprifermin-) treatment is structurally and symptomatically most effective.</div></div><div><h3>METHODS</h3><div>Total femorotibial joint (TFTJ) cartilage thickness change at year (Y) 2 by MRI represented the primary endpoint; WOMAC pain was secondary [1]. Patients aged 40–85 with primary symptomatic TFTJ OA (KLG 2 or 3; medial mJSW ≥2.5 mm) were studied. Cartilage thickness was determined quantitatively from 1.5-3T MRI by expert readers, using proprietary software (Chondrometrics). The analysis focused on the 2Y MRI TFTJ cartilage thickness change for the two highest sprifermin dose groups (100µg given every 6 or 12 months combined) vs. placebo. The Hedges G (sample-size-independent effect size measure) was determined, with 95% CIs obtained by bootstrapping. We studied the modified intent to treat cohort with 24-month data (mITT), and the so-called “subcohort at risk” (SAR)[2] a subgroup with baseline WOMAC pain >40 and more severe radiographic involvement by mJSW criteria.</div></div><div><h3>RESULTS</h3><div>Of 549 FORWARD patients randomized, 474 completed 2Y follow-up. 69% of the mITT with 24M data were female; the median age was 67 and the medial BMI 29.6. The treatment effect on cartilage thickness was 45.6µm for the mITT (Hedges G=0.63). Participants with baseline WOMAC pain ≥ the median displayed a somewhat smaller effect (17±70 µm change over 2 years in treated vs. -15±55µm in placebo participants; Hedges G =0.49 [0.11, 0.86] than those with pain < median (37±69µm in treated vs. -27±84µm in placebo participants; Hedges G =0.86 [0.47, 1.25].</div><div>KLG2 subjects displayed a marginally greater treatment effect (30±75µm in treated vs. -16±46µm in placebo participants; Hedges G =0.68 [0.36, 1.00]) than KLG3 participants (13±68µm in treated vs. -33±110µm in placebo participants; Hedges G =0.55 [0.07, 1.03]). Those with JSN grade 0 (no JSN) showed a stronger treatment effect (50±59µm in those treated vs. -11±45µm in placebo participants; Hedges G =1.09 [0.63, 1.54] than those","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100297"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144521547","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.2025.100342
S.A. Pai, M. Black, K. Young, S. Sherman, C. Chu, A. Williams, G. Gold, F. Kogan, B. Hargreaves, A. Chaudhari, A. Gatti
<div><h3>INTRODUCTION</h3><div>Femoral bone shape scores (B-Score) derived from shape models quantify 3D structural features associated with OA<sup>1,2</sup>. A higher B-Score is indicative of more OA-like bone shape. B-Scores have high sensitivity to quantify OA progression and stratify patients for interventions<sup>1</sup>. Neural Shape Models (NSM) capture non-linear bone shape features and outperform traditional Statistical Shape Models (SSMs) in encoding OA-related shapes<sup>3</sup>. Prior work that used a SSM-based B-Score showed that anterior cruciate ligament reconstructed (ACLR) knees exhibit higher B-Scores than their contralateral knees 2 years post-surgery, reflecting OA-like bone shape features<sup>4</sup>. However, little is known about how femoral bone shape changes immediately following ACLR and how it progresses during the early post-surgical period—a critical window when post-traumatic osteoarthritis (PTOA) may still be most responsive to intervention.</div></div><div><h3>OBJECTIVE</h3><div>To use a Neural Shape Model-based B-Score to quantify femoral shape differences between ACLR and contralateral knees immediately post-surgery (3-weeks) and to detect early PTOA bone shape changes over 30 months.</div></div><div><h3>METHODS</h3><div>ACLR and contralateral knees of 17 subjects (11M/6F, age=38±10 yrs, BMI=24±2 kg/m<sup>2</sup>) were scanned at 3 weeks (baseline), 3, 9, 18, and 30 months post-ACLR in a 3T MRI scanner (GE Healthcare, USA) using a qDESS sequence (TE/TR=6/22 ms<sub>,</sub> flip angle=25°, FOV=160 × 160 mm, bandwidth=31.25 kHz, pixel spacing=0.42 × 0.50 mm, slice thickness=1.5 mm). The femur was automatically segmented, and the B-Score was computed for each subject at all visits using a NSM that was trained on 9,376 femoral segmentations from the baseline DESS images in the OAI dataset<sup>1</sup>. To assess bone shape differences immediately after surgery, we compared B-Scores between the ACLR and contralateral knees at the baseline visit using a linear mixed effects model. To capture longitudinal bone shape changes after surgery, we calculated change in B-Score at each follow-up visit with respect to the baseline visit. We used a linear mixed effects model to assess the effect of knee-type and time post-surgery on B-Scores. Effect sizes [η<sub>p</sub><sup>2</sup> is small (0.01), medium (0.06), or large (0.14)] were computed for significant effects (p<0.05).</div></div><div><h3>RESULTS AND DISCUSSION</h3><div>At baseline, the ACLR knee B-Score was significantly lower than the contralateral knee (η<sub>p</sub><sup>2</sup>=0.40, p=0.005; Fig. 1A). Longitudinally, ACLR knees showed a significantly greater increase in B-Score than contralateral knees (η<sub>p</sub><sup>2</sup>=0.19, p<0.001; Fig 2A). The lower B-Scores in ACLR knees at baseline indicate that the surgical knee had a healthier, less OA-like bone shape than the contralateral knee. Visualization revealed that ACLR knees had a wider intercondylar no
{"title":"NEURAL SHAPE MODEL QUANTIFIES EARLY AND PROGRESSIVE BONE SHAPE CHANGES AFTER ACLR","authors":"S.A. Pai, M. Black, K. Young, S. Sherman, C. Chu, A. Williams, G. Gold, F. Kogan, B. Hargreaves, A. Chaudhari, A. Gatti","doi":"10.1016/j.ostima.2025.100342","DOIUrl":"10.1016/j.ostima.2025.100342","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Femoral bone shape scores (B-Score) derived from shape models quantify 3D structural features associated with OA<sup>1,2</sup>. A higher B-Score is indicative of more OA-like bone shape. B-Scores have high sensitivity to quantify OA progression and stratify patients for interventions<sup>1</sup>. Neural Shape Models (NSM) capture non-linear bone shape features and outperform traditional Statistical Shape Models (SSMs) in encoding OA-related shapes<sup>3</sup>. Prior work that used a SSM-based B-Score showed that anterior cruciate ligament reconstructed (ACLR) knees exhibit higher B-Scores than their contralateral knees 2 years post-surgery, reflecting OA-like bone shape features<sup>4</sup>. However, little is known about how femoral bone shape changes immediately following ACLR and how it progresses during the early post-surgical period—a critical window when post-traumatic osteoarthritis (PTOA) may still be most responsive to intervention.</div></div><div><h3>OBJECTIVE</h3><div>To use a Neural Shape Model-based B-Score to quantify femoral shape differences between ACLR and contralateral knees immediately post-surgery (3-weeks) and to detect early PTOA bone shape changes over 30 months.</div></div><div><h3>METHODS</h3><div>ACLR and contralateral knees of 17 subjects (11M/6F, age=38±10 yrs, BMI=24±2 kg/m<sup>2</sup>) were scanned at 3 weeks (baseline), 3, 9, 18, and 30 months post-ACLR in a 3T MRI scanner (GE Healthcare, USA) using a qDESS sequence (TE/TR=6/22 ms<sub>,</sub> flip angle=25°, FOV=160 × 160 mm, bandwidth=31.25 kHz, pixel spacing=0.42 × 0.50 mm, slice thickness=1.5 mm). The femur was automatically segmented, and the B-Score was computed for each subject at all visits using a NSM that was trained on 9,376 femoral segmentations from the baseline DESS images in the OAI dataset<sup>1</sup>. To assess bone shape differences immediately after surgery, we compared B-Scores between the ACLR and contralateral knees at the baseline visit using a linear mixed effects model. To capture longitudinal bone shape changes after surgery, we calculated change in B-Score at each follow-up visit with respect to the baseline visit. We used a linear mixed effects model to assess the effect of knee-type and time post-surgery on B-Scores. Effect sizes [η<sub>p</sub><sup>2</sup> is small (0.01), medium (0.06), or large (0.14)] were computed for significant effects (p<0.05).</div></div><div><h3>RESULTS AND DISCUSSION</h3><div>At baseline, the ACLR knee B-Score was significantly lower than the contralateral knee (η<sub>p</sub><sup>2</sup>=0.40, p=0.005; Fig. 1A). Longitudinally, ACLR knees showed a significantly greater increase in B-Score than contralateral knees (η<sub>p</sub><sup>2</sup>=0.19, p<0.001; Fig 2A). The lower B-Scores in ACLR knees at baseline indicate that the surgical knee had a healthier, less OA-like bone shape than the contralateral knee. Visualization revealed that ACLR knees had a wider intercondylar no","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100342"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522509","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.2025.100278
A. Boddu , T. Whitmarsh , N.A. Segal , N.H. Degala , J.A. Lynch , T.D. Turmezei
<div><h3>INTRODUCTION</h3><div>Weight bearing CT (WBCT) has shown promise in the evaluation of the knee joint instead of radiography. However, bringing weight bearing to 3-D imaging poses technical challenges that have to be overcome if repeatability is to be optimised. From prior study and experience, maintaining knee flexion angle (KFA) and centering in the vertical scan range with consistency can be difficult. One means to evaluate these distance and angle measurements from WBCT is to use a bone surface landmarking system.</div></div><div><h3>OBJECTIVE</h3><div>(1) To evaluate the repeatability of a manual WBCT landmarking system of the femur and tibia at the knee; and (2) from this develop a technique for evaluating repeatability of KFA and vertical scan range centering.</div></div><div><h3>METHODS</h3><div>14 individuals recruited and consented at the University of Kansas Medical Center had baseline and follow-up WBCT imaging suitable for analysis. Participant demographics were: mean ± SD age 61.3 ± 8.4 years, BMI 30.7 ± 4.3 kg/m<sup>2</sup> and male:female ratio 8:6. All scanning was performed on the same XFI WBCT scanner (Planmed Oy, Helsinki, Finland) with the mean ± SD interval between baseline and follow-up attendances 14.9 ± 8.1 days. A Synaflexer<sup>TM</sup> device was used to standardise knee positioning during scanning. Imaging acquisition parameters were 96 kV tube voltage, 51.4 mA tube current, 3.5 s exposure time. A standard bone algorithm was applied for reconstruction with 0.3 mm isotropic voxels and a 21 cm vertical scan range. Both knees were included in all analyses with SD adjustments made for multiple observations from the same individual. Participant identification and scan sequence were anonymised prior to analyses. A first observer (A.B.) placed 10 femoral and 12 tibial landmarks using Stradview. These landmarks were reviewed by a second observer (T.D.T.), who placed additional landmarks at the extremes of the vertical scan within the centre of the bone medullary cavities. Bone segmentations from ScanXM were used to register landmarks from follow-up to baseline in wxRegSurf; the follow-up-to-baseline femur registration was applied to the follow-up tibial co-ordinates to assess joint positioning. Landmark repeatability was taken as the mean ± SD distance (mm) between baseline and follow-up for each landmark. A method to extract KFA and valgus alignment was developed as the angle between the lines of the extreme scan range landmarks (F00 and T00) and the centre of gravity (CoG) of the rest of the landmarks in the same bone. Valgus alignment was taken from the anterior view (<180° laterally = valgus) and KFA from lateral.</div></div><div><h3>RESULTS</h3><div>Landmark placement with their codes is shown in Figure 1a (in a left knee), with code definitions given in Table 1. An example of baseline and follow-up landmarking is shown on the same knee in Figure 1b with error results from all landmarks given in Table 1. Outsi
{"title":"3-D LANDMARKING REPEATABILITY EMPHASIZES CHALLENGES IN SCAN POSITIONING DURING WEIGHT BEARING CT OF THE KNEE","authors":"A. Boddu , T. Whitmarsh , N.A. Segal , N.H. Degala , J.A. Lynch , T.D. Turmezei","doi":"10.1016/j.ostima.2025.100278","DOIUrl":"10.1016/j.ostima.2025.100278","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Weight bearing CT (WBCT) has shown promise in the evaluation of the knee joint instead of radiography. However, bringing weight bearing to 3-D imaging poses technical challenges that have to be overcome if repeatability is to be optimised. From prior study and experience, maintaining knee flexion angle (KFA) and centering in the vertical scan range with consistency can be difficult. One means to evaluate these distance and angle measurements from WBCT is to use a bone surface landmarking system.</div></div><div><h3>OBJECTIVE</h3><div>(1) To evaluate the repeatability of a manual WBCT landmarking system of the femur and tibia at the knee; and (2) from this develop a technique for evaluating repeatability of KFA and vertical scan range centering.</div></div><div><h3>METHODS</h3><div>14 individuals recruited and consented at the University of Kansas Medical Center had baseline and follow-up WBCT imaging suitable for analysis. Participant demographics were: mean ± SD age 61.3 ± 8.4 years, BMI 30.7 ± 4.3 kg/m<sup>2</sup> and male:female ratio 8:6. All scanning was performed on the same XFI WBCT scanner (Planmed Oy, Helsinki, Finland) with the mean ± SD interval between baseline and follow-up attendances 14.9 ± 8.1 days. A Synaflexer<sup>TM</sup> device was used to standardise knee positioning during scanning. Imaging acquisition parameters were 96 kV tube voltage, 51.4 mA tube current, 3.5 s exposure time. A standard bone algorithm was applied for reconstruction with 0.3 mm isotropic voxels and a 21 cm vertical scan range. Both knees were included in all analyses with SD adjustments made for multiple observations from the same individual. Participant identification and scan sequence were anonymised prior to analyses. A first observer (A.B.) placed 10 femoral and 12 tibial landmarks using Stradview. These landmarks were reviewed by a second observer (T.D.T.), who placed additional landmarks at the extremes of the vertical scan within the centre of the bone medullary cavities. Bone segmentations from ScanXM were used to register landmarks from follow-up to baseline in wxRegSurf; the follow-up-to-baseline femur registration was applied to the follow-up tibial co-ordinates to assess joint positioning. Landmark repeatability was taken as the mean ± SD distance (mm) between baseline and follow-up for each landmark. A method to extract KFA and valgus alignment was developed as the angle between the lines of the extreme scan range landmarks (F00 and T00) and the centre of gravity (CoG) of the rest of the landmarks in the same bone. Valgus alignment was taken from the anterior view (<180° laterally = valgus) and KFA from lateral.</div></div><div><h3>RESULTS</h3><div>Landmark placement with their codes is shown in Figure 1a (in a left knee), with code definitions given in Table 1. An example of baseline and follow-up landmarking is shown on the same knee in Figure 1b with error results from all landmarks given in Table 1. Outsi","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100278"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522639","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.2025.100284
J.B. Driban , J. Baek , J.C. Patarini , E. Kirillov , N. Vo , M.J. Richard , M. Zhang , M.S. Harkey , G.H. Lo , S.-H. Liu , C.B. Eaton , J. MacKay , M.F. Barbe , T.E. McAlindon
INTRODUCTION
An impediment to our current treatment strategies and clinical trials for people with knee OA is focusing only on one knee, often ignoring the contralateral knee. Failing to address the contralateral knee may explain why many localized therapeutic approaches fail to achieve optimal results.
OBJECTIVE
We explored whether an MRI-based composite score of BM lesion and effusion-synovitis volumes related to contralateral knee OA disease severity.
METHODS
Using data from the OAI, we conducted cross-sectional knee-based analyses among participants with bilateral knee MRIs and at least one knee with KLG ≥1 and a WOMAC pain score ≥10/100 (n=693). We included 1,386 knees from participants with an average age of 62 (SD=9) years. Most participants were overweight and had mild-to-moderate radiographic OA. MR images were collected at each OAI site using Siemens 3.0 Tesla Trio MR systems and knee coils. Acquisitions included a sagittal IM fat-suppressed sequence (field of view=160mm, slice thickness=3mm, skip=0mm, flip angle=180 degrees, echo time=30ms, recovery time=3200ms, 313 × 448 matrix, x-resolution=0.357mm, y-resolution=0.357mm), which was used to measure BML and effusion-synovitis volumes. BM lesion and effusion-synovitis volumes on MRIs were used to calculate a composite score (“disease activity”). A disease activity score of 0 approximated the average score for a reference sample (n=2,787, 50% had radiographic knee OA, average [SD] WOMAC pain score = 2.8 [3.3]); lower scores (negative scores) indicate milder disease, while greater values indicate worse disease. We divided the disease activity score into tertiles. We used four separate multinomial logistic models to explore the association between disease activity in knees with and without radiographic OA (outcome) and the contralateral disease severity (KLG or disease activity; exposure).
RESULTS
Disease activity among knees without radiographic OA had statistically significant relationships with contralateral disease activity (range of odds ratios: 4.86-23.22) but not contralateral KLG (range of odds ratios: 0.86-1.01; Table). Disease activity among knees with radiographic OA had statistically significant relationships with contralateral disease activity and KLG; however, the association was stronger for contralateral disease activity than KLG (range of odds ratios: 3.67-21.29 versus 1.96-2.20; Table).
CONCLUSION
Contralateral knee OA severity is related to disease activity. Disease activity in the contralateral knee is a more informative measure of disease severity than relying on radiographs. Future studies need to explore how the contralateral knee could impact clinical trial screening, monitoring, and intervention strategies, especially when testing localized therapies.
我们目前对膝关节OA患者的治疗策略和临床试验的一个障碍是只关注单侧膝盖，经常忽略对侧膝盖。未能解决对侧膝关节可能解释了为什么许多局部治疗方法未能达到最佳效果。目的：我们探讨基于mri的BM病变和积液-滑膜炎体积的综合评分是否与对侧膝关节OA疾病的严重程度相关。方法使用来自OAI的数据，我们对双侧膝关节mri和至少一个膝关节KLG≥1和WOMAC疼痛评分≥10/100的参与者（n=693）进行了基于膝关节的横断面分析。我们从平均年龄62岁（SD=9）岁的参与者中纳入1386个膝关节。大多数参与者体重超标，有轻度至中度骨关节炎。采用Siemens 3.0 Tesla Trio MR系统和膝关节线圈采集各OAI部位的MR图像。采集包括矢状面IM脂肪抑制序列（视场=160mm，切片厚度=3mm，跳跃=0mm，翻转角度=180度，回波时间=30ms，恢复时间=3200ms， 313 × 448矩阵，x分辨率=0.357mm， y分辨率=0.357mm），用于测量BML和积液-滑膜炎体积。脑脊髓瘤病变和mri上的积液-滑膜炎体积用于计算综合评分（“疾病活动性”）。疾病活动性评分0近似于参考样本的平均评分（n=2,787, 50%有膝关节炎，平均[SD] WOMAC疼痛评分 = 2.8 [3.3]）；分数越低（负分数）表示病情较轻，分数越大表示病情较重。我们将疾病活动度评分分成几档。我们使用了四个单独的多项逻辑模型来探索有或没有放射学OA的膝关节疾病活动性（结果）与对侧疾病严重程度(KLG或疾病活动性；接触)。结果无骨关节炎的膝关节疾病活动性与对侧疾病活动性有统计学意义（比值比范围：4.86 ~ 23.22），但与对侧KLG无统计学意义(比值比范围：0.86 ~ 1.01；表)。骨性关节炎患者的膝关节疾病活动度与对侧疾病活动度和KLG有统计学意义；然而，与KLG相比，对侧疾病活动性的相关性更强(优势比范围：3.67-21.29 vs 1.96-2.20；表)。结论对侧膝关节OA严重程度与疾病活动度有关。对侧膝关节的疾病活动性比依赖x线片更能提供疾病严重程度的信息。未来的研究需要探索对侧膝关节如何影响临床试验筛选、监测和干预策略，特别是在测试局部治疗时。
{"title":"REVEALING THE HIDDEN CULPRIT: CONTRALATERAL KNEE'S ROLE IN OSTEOARTHRITIS DISEASE ACTIVITY: DATA FROM THE OSTEOARTHRITIS INITIATIVE","authors":"J.B. Driban , J. Baek , J.C. Patarini , E. Kirillov , N. Vo , M.J. Richard , M. Zhang , M.S. Harkey , G.H. Lo , S.-H. Liu , C.B. Eaton , J. MacKay , M.F. Barbe , T.E. McAlindon","doi":"10.1016/j.ostima.2025.100284","DOIUrl":"10.1016/j.ostima.2025.100284","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>An impediment to our current treatment strategies and clinical trials for people with knee OA is focusing only on one knee, often ignoring the contralateral knee. Failing to address the contralateral knee may explain why many localized therapeutic approaches fail to achieve optimal results.</div></div><div><h3>OBJECTIVE</h3><div>We explored whether an MRI-based composite score of BM lesion and effusion-synovitis volumes related to contralateral knee OA disease severity.</div></div><div><h3>METHODS</h3><div>Using data from the OAI, we conducted cross-sectional knee-based analyses among participants with bilateral knee MRIs and at least one knee with KLG ≥1 and a WOMAC pain score ≥10/100 (n=693). We included 1,386 knees from participants with an average age of 62 (SD=9) years. Most participants were overweight and had mild-to-moderate radiographic OA. MR images were collected at each OAI site using Siemens 3.0 Tesla Trio MR systems and knee coils. Acquisitions included a sagittal IM fat-suppressed sequence (field of view=160mm, slice thickness=3mm, skip=0mm, flip angle=180 degrees, echo time=30ms, recovery time=3200ms, 313 × 448 matrix, x-resolution=0.357mm, y-resolution=0.357mm), which was used to measure BML and effusion-synovitis volumes. BM lesion and effusion-synovitis volumes on MRIs were used to calculate a composite score (“disease activity”). A disease activity score of 0 approximated the average score for a reference sample (n=2,787, 50% had radiographic knee OA, average [SD] WOMAC pain score = 2.8 [3.3]); lower scores (negative scores) indicate milder disease, while greater values indicate worse disease. We divided the disease activity score into tertiles. We used four separate multinomial logistic models to explore the association between disease activity in knees with and without radiographic OA (outcome) and the contralateral disease severity (KLG or disease activity; exposure).</div></div><div><h3>RESULTS</h3><div>Disease activity among knees without radiographic OA had statistically significant relationships with contralateral disease activity (range of odds ratios: 4.86-23.22) but not contralateral KLG (range of odds ratios: 0.86-1.01; Table). Disease activity among knees with radiographic OA had statistically significant relationships with contralateral disease activity and KLG; however, the association was stronger for contralateral disease activity than KLG (range of odds ratios: 3.67-21.29 versus 1.96-2.20; Table).</div></div><div><h3>CONCLUSION</h3><div>Contralateral knee OA severity is related to disease activity. Disease activity in the contralateral knee is a more informative measure of disease severity than relying on radiographs. Future studies need to explore how the contralateral knee could impact clinical trial screening, monitoring, and intervention strategies, especially when testing localized therapies.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100284"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523412","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.2025.100332
J. Zhong , Y. Yao , F. Xiao , T.Y.M. Ong , K.W.K. Ho , S. Li , C. Huang , Q. Chan , J.F. Griffith , W. Chen
INTRODUCTION
T1ρ imaging is an emerging technique in knee MRI for the evaluation of OA. This modality possesses the unique capability to image biochemical components, such as proteoglycans, facilitating early detection and post-treatment monitoring of knee OA. However, a significant challenge associated with T1ρ imaging lies in the complexity of its post-processing, which encompasses parameter fitting, cartilage segmentation, and subregional parcellation.
OBJECTIVE
This abstract presents a systematic methodology for automating knee T1ρ MRI post-processing by leveraging deep learning and advanced computational techniques.
METHODS
Our methodology automated the three primary steps of T1ρ knee MRI post-processing and provided the mean T1ρ values for 20 subregions of the femoral and tibial cartilage in the knee (Figure). In our experiments, we utilized four T1ρ-weighted images to generate the T1ρ map for 30 OA patients (age 67.63±5.80 years, BMI 26.00±4.08 kg/m2) and 10 healthy volunteers (age 24.90±2.59 years, BMI 22.75±4.51 kg/m2). For each subject, four T1ρ-weighted images were acquired using a spin-lock frequency of 300 Hz and spin-lock times of 0, 10, 30, and 50 ms, with a resolution of 0.8 × 1 × 3 mm³, resulting in an image matrix size of 44 × 256 × 256 . The spin-lock preparation was followed by an FSE readout with TE/TR = 31/2000 ms. Additionally, we computed the mean of the four T1ρ-weighted images and employed this mean for automated cartilage segmentation and subregion parcellation. We employed a nnU-Net trained with all 40 subjects for cartilage segmentation, while subregion parcellation was conducted using our previously published rule-based method, CartiMorph. The performance of the approach using deep learning segmentation was assessed using the Dice Coefficient Similarity (DSC), the root-mean-squared deviation (RMSD), and the coefficient of variance of RMSD (CVRMSD) against the manual segmentation. We excluded 3 OA patients with full cartilage loss above 50% of one cartilage area (FC, MTC, or LTC) in subregion analysis.
RESULTS
Our experimental results demonstrated the satisfactory performance of our proposed approach. The mean DSC values for the FC, MTC and LTC in OA patients and healthy volunteers were 0.83, 0.80, and 0.82, respectively. Table 2 provides a comprehensive breakdown of the performance metrics of the agreement in T1ρ quantification across 20 subregions.
CONCLUSION
We proposed a systematic approach for post-processing knee T1ρ MRI data. The experimental results demonstrated the efficacy of the proposed approach.
{"title":"A SYSTEMATIC POST-PROCESSING APPROACH FOR T1Ρ IMAGING OF KNEE ARTICULAR CARTILAGE","authors":"J. Zhong , Y. Yao , F. Xiao , T.Y.M. Ong , K.W.K. Ho , S. Li , C. Huang , Q. Chan , J.F. Griffith , W. Chen","doi":"10.1016/j.ostima.2025.100332","DOIUrl":"10.1016/j.ostima.2025.100332","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>T<sub>1ρ</sub> imaging is an emerging technique in knee MRI for the evaluation of OA. This modality possesses the unique capability to image biochemical components, such as proteoglycans, facilitating early detection and post-treatment monitoring of knee OA. However, a significant challenge associated with T<sub>1ρ</sub> imaging lies in the complexity of its post-processing, which encompasses parameter fitting, cartilage segmentation, and subregional parcellation.</div></div><div><h3>OBJECTIVE</h3><div>This abstract presents a systematic methodology for automating knee T<sub>1ρ</sub> MRI post-processing by leveraging deep learning and advanced computational techniques.</div></div><div><h3>METHODS</h3><div>Our methodology automated the three primary steps of T<sub>1ρ</sub> knee MRI post-processing and provided the mean T<sub>1ρ</sub> values for 20 subregions of the femoral and tibial cartilage in the knee (Figure). In our experiments, we utilized four T<sub>1ρ</sub>-weighted images to generate the T<sub>1ρ</sub> map for 30 OA patients (age 67.63±5.80 years, BMI 26.00±4.08 kg/m<sup>2</sup>) and 10 healthy volunteers (age 24.90±2.59 years, BMI 22.75±4.51 kg/m<sup>2</sup>). For each subject, four T<sub>1ρ</sub>-weighted images were acquired using a spin-lock frequency of 300 Hz and spin-lock times of 0, 10, 30, and 50 ms, with a resolution of 0.8 × 1 × 3 mm³, resulting in an image matrix size of 44 × 256 × 256 . The spin-lock preparation was followed by an FSE readout with TE/TR = 31/2000 ms. Additionally, we computed the mean of the four T<sub>1ρ</sub>-weighted images and employed this mean for automated cartilage segmentation and subregion parcellation. We employed a nnU-Net trained with all 40 subjects for cartilage segmentation, while subregion parcellation was conducted using our previously published rule-based method, CartiMorph. The performance of the approach using deep learning segmentation was assessed using the Dice Coefficient Similarity (DSC), the root-mean-squared deviation (RMSD), and the coefficient of variance of RMSD (CV<sub>RMSD</sub>) against the manual segmentation. We excluded 3 OA patients with full cartilage loss above 50% of one cartilage area (FC, MTC, or LTC) in subregion analysis.</div></div><div><h3>RESULTS</h3><div>Our experimental results demonstrated the satisfactory performance of our proposed approach. The mean DSC values for the FC, MTC and LTC in OA patients and healthy volunteers were 0.83, 0.80, and 0.82, respectively. Table 2 provides a comprehensive breakdown of the performance metrics of the agreement in T<sub>1ρ</sub> quantification across 20 subregions.</div></div><div><h3>CONCLUSION</h3><div>We proposed a systematic approach for post-processing knee T<sub>1ρ</sub> MRI data. The experimental results demonstrated the efficacy of the proposed approach.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100332"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523434","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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