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Texture analysis of T2 maps of the cartilage indicates differences in knee cartilage matrix in subjects with type 2 diabetes: data from the osteoarthritis initiative
Abstracts / Osteoarthritis and Cartilage 25 (2017) S8eS75
Purpose: The aim of this study was to develop a risk prediction model for the development of moderate-severe osteoarthritis (OA) over 8 years based on subject demographics, X-ray, and MRI data at baseline, using data from the Osteoarthritis Initiative (OAI). Methods: 641 subjects with no or mild radiographic OA (Kellgren Lawrence (KL) 0-2) and no clinical symptoms based on Western Ontario and McMaster Universities Arthritis Index (WOMAC) from 0-3 in the right knee were selected from the Osteoarthritis Initiative (OAI) database. Region-specific baseline 3T MRI cartilage and meniscus readings (WORMS scoring) and cartilage T2 quantification were performed. Baseline subject demographics, risk factors, Kellgren Lawrence (KL) scores, presence of cartilage defects and of meniscus tears (WORMS grade >1), and mean cartilage T2 (5 regions) were used to predict the development of moderate to severe radiographic or symptomatic OA over 8 years (defined by incident TKR over 8 years, or worsening to KL 3 or 4, or progression of WOMAC pain score to >¼5). Best subsets variable selection followed by 10-fold cross-validated Akaike information criteria (AIC, lowest being most desirable) and Discrimination indices (DI, difference in predicted probability of an outcome between those with and without outcomes, highest being most desirable) were used to assess which combinations of the above-listed variables best predict the outcome. This procedure was performed for 3 models: Model 1 (most basic) included KL score and risk factors; Model 2 included KL score, risk factors, and WORMS scores; Model 3 (most sophisticated) included KL score, risk factors, WORMS scores, and mean cartilage T2. All models included age, gender, and BMI. The reason for the three models was for ease of use in a clinical setting, regardless of whether advanced MR imaging data was available. The resulting three models were compared using 10-fold cross-validated receiver operating characteristic (ROC) analysis. Results: The 641 participants in this study had a mean age of 56.4 ± 7.5 years and a mean BMI of 27.0 ± 4.3 kg/m2 at baseline. 80 subjects (12.5%) had a positive outcome (either an incident TKR [n ¼ 8, 1.25%], worsening to KL 3 or 4 [n ¼ 34, 5.31%], or progression to a WOMAC pain score of >¼ 5 [n ¼ 53, 8.27%]). 15 subjects had >1 outcome. 381 (59.4%) subjects had partial or full thickness cartilage defects while 190 (29.6%) had a meniscus tear. The following models best predicted the development of knee OA over 8 years: Model 1 included KL score and previous knee injury in the last 12 months (cross-validated DI ¼ 0.048). Model 2 included all variables in Model 1 plus presence of cartilage defects in the lateral femur and patella, and presence of a meniscal tear (cross validated DI ¼ 0.084). Model 3 included all variables in Model 1 and 2, plus mean cartilage T2 in the medial tibia and medial femur (cross validated DI ¼ 0.11, Fig. 1). Compared to Model 1 with a crossvalidated area under the ROC curve (AUC) ¼ 0.67, Model 3 performed significantly better (AUC ¼ 0.72, p ¼ 0.04) while Model 2 was not significantly different (AUC ¼ 0.71, p ¼ 0.08). There was no difference in
Fig. 1. (a) A graphic of the Risk Score calculator, (b) An illustration of the effects of cartilage T2 on OA risk prediction, while keeping the subject characteristics including KL and WORMS scores constant. As cartilage T2 increases, the risk for OA development increases, as illustrated by the red areas in the “high risk” T2 map.
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performance between Models 2 and 3. These results demonstrate that including both cartilage T2 and WORMS significantly improves model performance compared to a model with risk factors and KL-score alone. Figure 1a illustrates a risk calculator graphic designed for use in the clinic; Fig. 1b illustrates the isolated effects of low, medium and high medial femur cartilage T2 on OA risk probability, while keeping the other subject characteristics (risk factors, KL score, and WORMS scores) constant. Conclusions: We have developed a risk calculator for the development of knee OA over 8 years that includes radiographic and MRI data. The inclusion of WORMS and cartilage T2 significantly improves model performance, and suggests that these are valuable biomarkers that may help identify those at increased risk for knee OA. This risk calculator could be used to identify individuals who may benefit most from lifestyle modifications to prevent knee OA. Acknowledgements: We would like to acknowledge support from NIH/ NIAMS (grants R01AR064771 and P50-AR060752) for this work. 101 TEXTURE ANALYSIS OF T2 MAPS OF THE CARTILAGE INDICATES DIFFERENCES IN KNEE CARTILAGE MATRIX IN SUBJECTS WITH TYPE 2 DIABETES: DATA FROM THE OSTEOARTHRITIS INITIATIVE J. Neumann y, U. Heilmeier y, G.B. Joseph y, F.C. Hofmann y, W. Ashmeik y, A.S. Gersing y, N. Chanchek y, z, B.J. Schwaiger y, M.C. Nevitt x, C.E. McCulloch x, N.E. Lane k, F. Liu x, J.A. Lynch x, T.M. Link y. y UCSF Dept. of Radiology & BioMed. Imaging, University of California, San Francisco, CA, USA; z Dept. of Radiology, Naresuan University, Phitsanulok, Thailand; x Dept. of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; k Dept. of Med. and Ctr. for Musculoskeletal Hlth., University of California, Davis, Sacramento, CA, USA Purpose: Osteoarthritis (OA) and type 2 diabetes mellitus (T2DM) are both common disorders of rising incidence. OA is the most common cause of chronic disability in the field of musculoskeletal diseases and is associated with a significant reduction in quality of life. Interestingly, T2DM is reportedly to be present in a high proportion of knee OA subjects. While both diseases share many risk factors the biologic relationship between these two diseases is not yet completely understood. However, early epidemiologic studies suggest that the diabetesinduced impaired glucose metabolism may impact cartilage matrix microanatomy by influencing protein folding and thus induce cartilage matrix degradation. Grey-level co-occurrence matrix (GLCM) texture analysis of T2 relaxation time maps of the articular cartilage is a novel quantitative compositional MR imaging technique that quantifies the heterogeneity of cartilage T2 values and is driven by changes in hydration and organization of the anisotropic arrangement of collagen fibrils. Thus, other than standard T2 relaxation time measures, texture analysis may be more sensitive to microarchitectural changes in the cartilage matrix caused by the diabetes-induced impaired glucose metabolism. The purpose of this cross-sectional case-control study was to assess texture measures of cartilage T2 maps in patients with T2DM compared with diabetes-free controls. Methods: We assessed the right knee of 416 subjects from the Osteoarthritis Initiative (OAI). Subjects with T2DM were selected based on a self-administered comorbidity questionnaire (Charlson Comorbidity Index). MRI scans obtained at 3 Tesla in subjects with T2DM and a Kellgren and Lawrence (KL) score of 0-2 (n¼208) were matched in groups with non-diabetic controls (n¼208) by sex, KL score, age and BMI. On 2D multi-slice multi-echo (MSME) spin-echo sequences the cartilage of each compartment (patella, lateral femur, medial femur, lateral tibia, medial tibia) was manually segmented and analyzed in a mono-exponential decay model with fitting function for the signal intensity and calculating T2 maps for each compartment. Texture analysis of T2 maps was performed based on the GLCM calculating spatial distribution of neighboring voxels within the cartilage. In each compartment, the texture algorithm analyzed three texture parameters (variance, contrast and entropy), reflecting the heterogeneity of T2 values throughout the cartilage matrix. Statistical analysis used conditional logistic regression analysis to compare the texture parameters in each compartment as well as the mean across all compartments. Results: Both study groups were similar in age (63.3 vs 63.0 years, p¼0.76), BMI (31.0 vs 31.2 kg/m2, p¼0.70), sex (female 53.4% vs 53.4%), KL score distribution (p¼0.93). Average over all compartments, knees of subjects with T2DM showed significantly higher texture parameters in
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Abstracts / Osteoarthritis and Cartilage 25 (2017) S8eS75
the linear dependency of grey levels of neighboring voxels (¼contrast p < 0.05), the variety of voxels from the compartment mean (¼variance p < 0.05) and the general irregularity of voxels pairs occurring in the cartilage matrix (¼entropy p < 0.001). Analysis of the different compartments also showed significantly higher texture parameters for all categories in T2DM subjects for the patella (Fig. 1), the lateral and medial tibia (p < 0.05) and a higher entropy for the medial femur (p < 0.05). Conclusions: Compared to a non-diabetic control group the articular cartilage in subjects with T2DM showed a significantly more varied, heterogeneous and disordered cartilage composition. These results support the observation that in T2DM the cartilage microarchitecture is altered by the effects of elevated blood glucose and this may influence deterioration of the collagen architecture in these subjects. We would like to acknowledge support from NIH/NIAMS (National Institute of Arthritis and Musculoskeletal and Skin Diseases grants R01AR064771 and P50-AR060752) for this work.
clinical progression display longitudinal changes in IPFP volume and/or 3D MRI signal and whether these changes differ from those knees without progression. Fast clinical progression was hereby defined by progressing from a relatively early stage of radiographic OA (i.e. KLG2) to knee replacement (KR) over a relatively short time interval of 5 years. Methods: 42 participants from the Osteoarthritis Initiative who received a surgical KR between the 2- and 5-year follow-up visits and who displayed early radiographic disease (i.e. KLG2) at baseline were included. These were matched with 42 control knees who had no evidence of a KR in the ipsi- and/or contralateral knee up to the 5-year follow-up visit; 1:1 matched controls had to have the same sex, age (±5yrs), and radiographic disease stage at baseline (KLG 0-1, 2). The clinical visit before the KR was designated T0, and the visit 2 years before T0 was designated T-2. The IPFP was manually segmented from 3.0 mm IW TSE MR images, excluding peripheral pathologies such as cysts, with blinding to case-control-status and time point of acquisition (J.D.). Quality control of the segmentations was performed by an expert reader (A.R.). IPFP volume, 3D MRI signal mean (mean gray value of the segmented voxels), and the 3D MRI signal standard deviation (SD; ¼ signal heterogeneity) were determined at T0 and T-2. The longitudinal 2year changes were compared between KR cases and matched non-KR controls (paired t-tests). Exploratory analyses were performed analyzing changes in the anterior and posterior surface area of the IPFP, in the maximum sagittal cross-sectional area and central slice of the IPFP (Fig. 1), and analyzing cross-sectional differences of the above parameters at T0 and T-2 (paired t-tests).
Fig. 1. Colored texture map of the patella of a 49y old male with diabetes (A) and a 52y old non-diabetic male (B). The diabetic patient (A) shows an inhomogeneous, mosaic like, signal distribution, indicating an altered matrix cartilage microarchitecture, whereas the healthy control (B) shows a more homogeneous and smooth texture color map. 102 LONGITUDINAL CHANGE IN INFRAPATELLAR FAT PAD VOLUME AND MRI SIGNAL PRIOR TO TOTAL KNEE REPLACEMENT e DATA FROM THE OSTEOARTHRITIS INITIATIVE € rrenberg x, C.K. Kwoh k, D.J. Hunter ¶, A.S. Ruhdorfer y, W. Wirth y, z, J. Do F. Eckstein y. y Paracelsus Med. Univ. Salzburg & Nuremberg, Salzburg, Austria; z Chondrometrics GmbH, Ainring, Germany; x Paracelsus Med. Univ. Salzburg & Nuremberg, Salzburg, Austria; k Univ. of Arizona Arthritis Ctr., Univ. of Arizona Coll. of Med., Tucson, AZ, USA; ¶ Rheumatology Dept., Royal North Shore Hosp. & Inst. of Bone and Joint Res., Kolling Inst., Univ. of Sydney, Sydney, Australia Purpose: The infra-patellar fat pad (IPFP) represents a potential source of intra-articular leptin secretion, which has been associated with structural pathology in knee osteoarthritis (OA). Upregulated immune cells within the IPFP may promote (synovial) inflammation in knee OA, and Hoffa synovitis has been associated with greater MRI signal heterogeneity of the IPFP. We here examine whether knees with fast
Fig. 1. Manual segmentation of the infra-patellar fat pad and 3D reconstruction with morphometric parameters. Results: At baseline, cases were 65.2±7.1 years old (mean±standard deviation), 67% female, with a BMI of 29.8±4.3. 12% had KLG0, 19% KLG1, and 69% KLG2. Controls were 64.1±7.2 years old, with a BMI of 30.2±4.8. 24% had KLG0, 7% KLG1, and 69% KLG2. The time between baseline and T0 was 3.2±0.9 years. In KR cases, the 3D MRI signal SD (heterogeneity) and the 3D MRI signal mean increased significantly between T-2 to T0 (p < 0.001), with the increase in 3D MRI signal SD (heterogeneity) significantly exceeding the changes in non-KR controls (p ¼ 0.03) (Table 1). There were small, nonsignificant changes in IPFP volume which did not differ between cases versus controls (p ¼ 0.83) (Table 1). No other relevant longitudinal changes in other morphometric parameters of the IPFP were observed over this period (Table 1). At T0, but not T-2, the 3D MRI signal SD (heterogeneity) was significantly greater in cases compared to controls
Table 1 IPFP size and 3D MRI signal at T-2 and T0 and the two-year changes. T-2
Volume [cm3] 3D MRI Signal Mean 3D MRI Signal SD Anterior Surface [cm2] Posterior Surface [cm2] Masimum Slice [cm2] Central Slice [cm2]
T0
Change
Case
Control
Case
Control
Case
Control
27.5 ± 6.23 169.5 ± 39.6 80.5 ± 15.8 26.5 ± 5.00 37.4 ± 7.35 6.88 ± 1.37 6.54 ± 1.41
28.1 ± 6.45 164.6 ± 41.6 80.2 ± 18.6 25.7 ± 4.44 39.1 ± 6.54 6.77 ± 1.21 6.49 ± 1.20
27.1 ± 6.77 215.5 ± 71.2 107.6 ± 34.5# 26.5 ± 4.50 37.6 ± 6.38 6.74 ± 1.46 6.43 ± 1.49
27.8 ± 6.46 193.2 ± 39.9 95.2 ± 20.2# 25.8 ± 4.51 39.5 ± 6.55 6.70 ± 1.18 6.38 ± 1.27
1.8% (-4.0, 0.5) +46.1 (29.3, 62.8)* +27.1 (17.3, 37.0)*# +0.3% (1.7, 2.4) 0.7% (2.7, 1.4) +6.7% (4.3, 0.4) 1.5% (4.5, 1.6)
1.1% (-2.6, 0.5) +28.6 (14.5, 42.8)* +15.0 (7.7, 22.2)*# 0.1% (2.0, 1.9) 1.0% (2.6, 0.7) 0.8% (2.6, 1.1) 1.7% (4.1, 0.8)
IPFP¼infra-ptellar fat apd; mean ± standard deviation; SD¼standard deviation; changes with 95% confidence intervals; *p T-2 vs T0 0.0002; #p case vs control 0.04
Purpose: The aim of this study was to develop a risk prediction model for the development of moderate-severe osteoarthritis (OA) over 8 years based on subject demographics, X-ray, and MRI data at baseline, using data from the Osteoarthritis Initiative (OAI). Methods: 641 subjects with no or mild radiographic OA (Kellgren Lawrence (KL) 0-2) and no clinical symptoms based on Western Ontario and McMaster Universities Arthritis Index (WOMAC) from 0-3 in the right knee were selected from the Osteoarthritis Initiative (OAI) database. Region-specific baseline 3T MRI cartilage and meniscus readings (WORMS scoring) and cartilage T2 quantification were performed. Baseline subject demographics, risk factors, Kellgren Lawrence (KL) scores, presence of cartilage defects and of meniscus tears (WORMS grade >1), and mean cartilage T2 (5 regions) were used to predict the development of moderate to severe radiographic or symptomatic OA over 8 years (defined by incident TKR over 8 years, or worsening to KL 3 or 4, or progression of WOMAC pain score to >¼5). Best subsets variable selection followed by 10-fold cross-validated Akaike information criteria (AIC, lowest being most desirable) and Discrimination indices (DI, difference in predicted probability of an outcome between those with and without outcomes, highest being most desirable) were used to assess which combinations of the above-listed variables best predict the outcome. This procedure was performed for 3 models: Model 1 (most basic) included KL score and risk factors; Model 2 included KL score, risk factors, and WORMS scores; Model 3 (most sophisticated) included KL score, risk factors, WORMS scores, and mean cartilage T2. All models included age, gender, and BMI. The reason for the three models was for ease of use in a clinical setting, regardless of whether advanced MR imaging data was available. The resulting three models were compared using 10-fold cross-validated receiver operating characteristic (ROC) analysis. Results: The 641 participants in this study had a mean age of 56.4 ± 7.5 years and a mean BMI of 27.0 ± 4.3 kg/m2 at baseline. 80 subjects (12.5%) had a positive outcome (either an incident TKR [n ¼ 8, 1.25%], worsening to KL 3 or 4 [n ¼ 34, 5.31%], or progression to a WOMAC pain score of >¼ 5 [n ¼ 53, 8.27%]). 15 subjects had >1 outcome. 381 (59.4%) subjects had partial or full thickness cartilage defects while 190 (29.6%) had a meniscus tear. The following models best predicted the development of knee OA over 8 years: Model 1 included KL score and previous knee injury in the last 12 months (cross-validated DI ¼ 0.048). Model 2 included all variables in Model 1 plus presence of cartilage defects in the lateral femur and patella, and presence of a meniscal tear (cross validated DI ¼ 0.084). Model 3 included all variables in Model 1 and 2, plus mean cartilage T2 in the medial tibia and medial femur (cross validated DI ¼ 0.11, Fig. 1). Compared to Model 1 with a crossvalidated area under the ROC curve (AUC) ¼ 0.67, Model 3 performed significantly better (AUC ¼ 0.72, p ¼ 0.04) while Model 2 was not significantly different (AUC ¼ 0.71, p ¼ 0.08). There was no difference in
Fig. 1. (a) A graphic of the Risk Score calculator, (b) An illustration of the effects of cartilage T2 on OA risk prediction, while keeping the subject characteristics including KL and WORMS scores constant. As cartilage T2 increases, the risk for OA development increases, as illustrated by the red areas in the “high risk” T2 map.
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performance between Models 2 and 3. These results demonstrate that including both cartilage T2 and WORMS significantly improves model performance compared to a model with risk factors and KL-score alone. Figure 1a illustrates a risk calculator graphic designed for use in the clinic; Fig. 1b illustrates the isolated effects of low, medium and high medial femur cartilage T2 on OA risk probability, while keeping the other subject characteristics (risk factors, KL score, and WORMS scores) constant. Conclusions: We have developed a risk calculator for the development of knee OA over 8 years that includes radiographic and MRI data. The inclusion of WORMS and cartilage T2 significantly improves model performance, and suggests that these are valuable biomarkers that may help identify those at increased risk for knee OA. This risk calculator could be used to identify individuals who may benefit most from lifestyle modifications to prevent knee OA. Acknowledgements: We would like to acknowledge support from NIH/ NIAMS (grants R01AR064771 and P50-AR060752) for this work. 101 TEXTURE ANALYSIS OF T2 MAPS OF THE CARTILAGE INDICATES DIFFERENCES IN KNEE CARTILAGE MATRIX IN SUBJECTS WITH TYPE 2 DIABETES: DATA FROM THE OSTEOARTHRITIS INITIATIVE J. Neumann y, U. Heilmeier y, G.B. Joseph y, F.C. Hofmann y, W. Ashmeik y, A.S. Gersing y, N. Chanchek y, z, B.J. Schwaiger y, M.C. Nevitt x, C.E. McCulloch x, N.E. Lane k, F. Liu x, J.A. Lynch x, T.M. Link y. y UCSF Dept. of Radiology & BioMed. Imaging, University of California, San Francisco, CA, USA; z Dept. of Radiology, Naresuan University, Phitsanulok, Thailand; x Dept. of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; k Dept. of Med. and Ctr. for Musculoskeletal Hlth., University of California, Davis, Sacramento, CA, USA Purpose: Osteoarthritis (OA) and type 2 diabetes mellitus (T2DM) are both common disorders of rising incidence. OA is the most common cause of chronic disability in the field of musculoskeletal diseases and is associated with a significant reduction in quality of life. Interestingly, T2DM is reportedly to be present in a high proportion of knee OA subjects. While both diseases share many risk factors the biologic relationship between these two diseases is not yet completely understood. However, early epidemiologic studies suggest that the diabetesinduced impaired glucose metabolism may impact cartilage matrix microanatomy by influencing protein folding and thus induce cartilage matrix degradation. Grey-level co-occurrence matrix (GLCM) texture analysis of T2 relaxation time maps of the articular cartilage is a novel quantitative compositional MR imaging technique that quantifies the heterogeneity of cartilage T2 values and is driven by changes in hydration and organization of the anisotropic arrangement of collagen fibrils. Thus, other than standard T2 relaxation time measures, texture analysis may be more sensitive to microarchitectural changes in the cartilage matrix caused by the diabetes-induced impaired glucose metabolism. The purpose of this cross-sectional case-control study was to assess texture measures of cartilage T2 maps in patients with T2DM compared with diabetes-free controls. Methods: We assessed the right knee of 416 subjects from the Osteoarthritis Initiative (OAI). Subjects with T2DM were selected based on a self-administered comorbidity questionnaire (Charlson Comorbidity Index). MRI scans obtained at 3 Tesla in subjects with T2DM and a Kellgren and Lawrence (KL) score of 0-2 (n¼208) were matched in groups with non-diabetic controls (n¼208) by sex, KL score, age and BMI. On 2D multi-slice multi-echo (MSME) spin-echo sequences the cartilage of each compartment (patella, lateral femur, medial femur, lateral tibia, medial tibia) was manually segmented and analyzed in a mono-exponential decay model with fitting function for the signal intensity and calculating T2 maps for each compartment. Texture analysis of T2 maps was performed based on the GLCM calculating spatial distribution of neighboring voxels within the cartilage. In each compartment, the texture algorithm analyzed three texture parameters (variance, contrast and entropy), reflecting the heterogeneity of T2 values throughout the cartilage matrix. Statistical analysis used conditional logistic regression analysis to compare the texture parameters in each compartment as well as the mean across all compartments. Results: Both study groups were similar in age (63.3 vs 63.0 years, p¼0.76), BMI (31.0 vs 31.2 kg/m2, p¼0.70), sex (female 53.4% vs 53.4%), KL score distribution (p¼0.93). Average over all compartments, knees of subjects with T2DM showed significantly higher texture parameters in
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Abstracts / Osteoarthritis and Cartilage 25 (2017) S8eS75
the linear dependency of grey levels of neighboring voxels (¼contrast p < 0.05), the variety of voxels from the compartment mean (¼variance p < 0.05) and the general irregularity of voxels pairs occurring in the cartilage matrix (¼entropy p < 0.001). Analysis of the different compartments also showed significantly higher texture parameters for all categories in T2DM subjects for the patella (Fig. 1), the lateral and medial tibia (p < 0.05) and a higher entropy for the medial femur (p < 0.05). Conclusions: Compared to a non-diabetic control group the articular cartilage in subjects with T2DM showed a significantly more varied, heterogeneous and disordered cartilage composition. These results support the observation that in T2DM the cartilage microarchitecture is altered by the effects of elevated blood glucose and this may influence deterioration of the collagen architecture in these subjects. We would like to acknowledge support from NIH/NIAMS (National Institute of Arthritis and Musculoskeletal and Skin Diseases grants R01AR064771 and P50-AR060752) for this work.
clinical progression display longitudinal changes in IPFP volume and/or 3D MRI signal and whether these changes differ from those knees without progression. Fast clinical progression was hereby defined by progressing from a relatively early stage of radiographic OA (i.e. KLG2) to knee replacement (KR) over a relatively short time interval of 5 years. Methods: 42 participants from the Osteoarthritis Initiative who received a surgical KR between the 2- and 5-year follow-up visits and who displayed early radiographic disease (i.e. KLG2) at baseline were included. These were matched with 42 control knees who had no evidence of a KR in the ipsi- and/or contralateral knee up to the 5-year follow-up visit; 1:1 matched controls had to have the same sex, age (±5yrs), and radiographic disease stage at baseline (KLG 0-1, 2). The clinical visit before the KR was designated T0, and the visit 2 years before T0 was designated T-2. The IPFP was manually segmented from 3.0 mm IW TSE MR images, excluding peripheral pathologies such as cysts, with blinding to case-control-status and time point of acquisition (J.D.). Quality control of the segmentations was performed by an expert reader (A.R.). IPFP volume, 3D MRI signal mean (mean gray value of the segmented voxels), and the 3D MRI signal standard deviation (SD; ¼ signal heterogeneity) were determined at T0 and T-2. The longitudinal 2year changes were compared between KR cases and matched non-KR controls (paired t-tests). Exploratory analyses were performed analyzing changes in the anterior and posterior surface area of the IPFP, in the maximum sagittal cross-sectional area and central slice of the IPFP (Fig. 1), and analyzing cross-sectional differences of the above parameters at T0 and T-2 (paired t-tests).
Fig. 1. Colored texture map of the patella of a 49y old male with diabetes (A) and a 52y old non-diabetic male (B). The diabetic patient (A) shows an inhomogeneous, mosaic like, signal distribution, indicating an altered matrix cartilage microarchitecture, whereas the healthy control (B) shows a more homogeneous and smooth texture color map. 102 LONGITUDINAL CHANGE IN INFRAPATELLAR FAT PAD VOLUME AND MRI SIGNAL PRIOR TO TOTAL KNEE REPLACEMENT e DATA FROM THE OSTEOARTHRITIS INITIATIVE € rrenberg x, C.K. Kwoh k, D.J. Hunter ¶, A.S. Ruhdorfer y, W. Wirth y, z, J. Do F. Eckstein y. y Paracelsus Med. Univ. Salzburg & Nuremberg, Salzburg, Austria; z Chondrometrics GmbH, Ainring, Germany; x Paracelsus Med. Univ. Salzburg & Nuremberg, Salzburg, Austria; k Univ. of Arizona Arthritis Ctr., Univ. of Arizona Coll. of Med., Tucson, AZ, USA; ¶ Rheumatology Dept., Royal North Shore Hosp. & Inst. of Bone and Joint Res., Kolling Inst., Univ. of Sydney, Sydney, Australia Purpose: The infra-patellar fat pad (IPFP) represents a potential source of intra-articular leptin secretion, which has been associated with structural pathology in knee osteoarthritis (OA). Upregulated immune cells within the IPFP may promote (synovial) inflammation in knee OA, and Hoffa synovitis has been associated with greater MRI signal heterogeneity of the IPFP. We here examine whether knees with fast
Fig. 1. Manual segmentation of the infra-patellar fat pad and 3D reconstruction with morphometric parameters. Results: At baseline, cases were 65.2±7.1 years old (mean±standard deviation), 67% female, with a BMI of 29.8±4.3. 12% had KLG0, 19% KLG1, and 69% KLG2. Controls were 64.1±7.2 years old, with a BMI of 30.2±4.8. 24% had KLG0, 7% KLG1, and 69% KLG2. The time between baseline and T0 was 3.2±0.9 years. In KR cases, the 3D MRI signal SD (heterogeneity) and the 3D MRI signal mean increased significantly between T-2 to T0 (p < 0.001), with the increase in 3D MRI signal SD (heterogeneity) significantly exceeding the changes in non-KR controls (p ¼ 0.03) (Table 1). There were small, nonsignificant changes in IPFP volume which did not differ between cases versus controls (p ¼ 0.83) (Table 1). No other relevant longitudinal changes in other morphometric parameters of the IPFP were observed over this period (Table 1). At T0, but not T-2, the 3D MRI signal SD (heterogeneity) was significantly greater in cases compared to controls
Table 1 IPFP size and 3D MRI signal at T-2 and T0 and the two-year changes. T-2
Volume [cm3] 3D MRI Signal Mean 3D MRI Signal SD Anterior Surface [cm2] Posterior Surface [cm2] Masimum Slice [cm2] Central Slice [cm2]
T0
Change
Case
Control
Case
Control
Case
Control
27.5 ± 6.23 169.5 ± 39.6 80.5 ± 15.8 26.5 ± 5.00 37.4 ± 7.35 6.88 ± 1.37 6.54 ± 1.41
28.1 ± 6.45 164.6 ± 41.6 80.2 ± 18.6 25.7 ± 4.44 39.1 ± 6.54 6.77 ± 1.21 6.49 ± 1.20
27.1 ± 6.77 215.5 ± 71.2 107.6 ± 34.5# 26.5 ± 4.50 37.6 ± 6.38 6.74 ± 1.46 6.43 ± 1.49
27.8 ± 6.46 193.2 ± 39.9 95.2 ± 20.2# 25.8 ± 4.51 39.5 ± 6.55 6.70 ± 1.18 6.38 ± 1.27
1.8% (-4.0, 0.5) +46.1 (29.3, 62.8)* +27.1 (17.3, 37.0)*# +0.3% (1.7, 2.4) 0.7% (2.7, 1.4) +6.7% (4.3, 0.4) 1.5% (4.5, 1.6)
1.1% (-2.6, 0.5) +28.6 (14.5, 42.8)* +15.0 (7.7, 22.2)*# 0.1% (2.0, 1.9) 1.0% (2.6, 0.7) 0.8% (2.6, 1.1) 1.7% (4.1, 0.8)
IPFP¼infra-ptellar fat apd; mean ± standard deviation; SD¼standard deviation; changes with 95% confidence intervals; *p T-2 vs T0 0.0002; #p case vs control 0.04