Between-group differences in infra-patellar fat pad size and signal in symptomatic and radiographic progression of knee osteoarthritis vs non-progressive controls and healthy knees - data from the FNIH Biomarkers Consortium Study and the Osteoarthritis Initiative

Role of joint adipose tissues in osteoarthritis

Osteoarthritis (OA) is the most common musculoskeletal disease, without any curative treatment. Obesity being the main modifiable risk factor for OA, much attention focused on the role of adipose tissues (AT). In addition to the involvement of visceral and subcutaneous AT via systemic ways, many arguments also highlight the involvement of local AT, present in joint tissues. Local AT include intra-articular AT (IAAT), which border the synovium, and bone marrow AT (BMAT) localized within marrow cavities in the bones. This review describes the known features and involvement of IAAT and BMAT in joint homeostasis and OA. Recent findings evidence that alteration in magnetic resonance imaging signal intensity of infrapatellar fat pad can be predictive of the development and progression of knee OA. IAAT and synovium are partners of the same functional unit; IAAT playing an early and pivotal role in synovial inflammation and fibrosis and OA pain. BMAT, whose functions have only recently begun to be studied, is in close functional interaction with its microenvironment. The volume and molecular profile of BMAT change according to the pathophysiological context, enabling fine regulation of haematopoiesis and bone metabolism. Although its role in OA has not yet been studied, the localization of BMAT, its functions and the importance of the bone remodelling processes that occur in OA argue in favour of a role for BMAT in OA.

MAVRIC based T2 mapping assessment of infrapatellar fat pad scarring in patients with total knee arthroplasty

The infrapatellar fat pad (IPFP) has been implicated as a source of postoperative knee pain. Imaging the IPFP is challenging in patients with total knee arthroplasty (TKA) due to metallic susceptibility artifact. Multi-Acquisition Variable-Resonance Image Combination (MAVRIC)-based T2 Mapping has been developed to mitigate this artifact and can generate quantitative T2 data. Objectives of this study were to (1) measure T2 values of the IPFP in patients with TKAs using a MAVRIC based T2 mapping technique and (2) determine if IPFP T2 values are related to the degree of fat pad scarring or clinical magnetic resonance imaging (MRI) findings. Twenty-eight subjects (10 males, 18 females, Age: 66 + 7.2 years [Mean ± standard deviations]) undergoing clinical MRIs were sequentially recruited. Morphological imaging and quantitative T2 mapping sequences were performed on a clinical 1.5 T scanner. The morphologic images were graded for the presence and severity of fat pad scarring and clinical outcomes. T2 values were calculated in the total fat pad volume, a normal regions of interest (ROI), and an abnormal ROI. T2 values were shortened in the total IPFP volume (p = 0.001) and within abnormal regions (p = 0.003) in subjects with more severe IPFP scarring. The difference between T2 values in normal-abnormal regions was greater in subjects with severe versus no scarring (+1426.1%, p = 0.008). T2 values were elevated in patients with MRI findings of osteolysis (+32.3%, p = 0.02). These findings indicate that MAVRIC-based T2 Mapping may be used as a quantitative biomarker of postoperative IPFP scarring in individuals following TKA.

Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis

BACKGROUND

To develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis.

METHODS

This retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features in cartilage portions were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), K-nearest neighbour (KNN) and support vector machine (SVM). In each algorithm, ten models derived from all available planes of three joint compartments and their various combinations were, respectively, constructed for comparative analysis. The performance of classifiers was mainly evaluated and compared by receiver operating characteristic (ROC) analysis.

RESULTS

All models achieved satisfying performances, especially the Final model, where accuracy and area under ROC curve (AUC) of LR classifier were 0.968, 0.983 (0.957-1.000, 95% CI) in the validation cohort, and 0.940, 0.984 (0.969-0.995, 95% CI) in the training cohort, respectively.

CONCLUSION

The MRI radiomics analysis represented promising performance in noninvasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints.

Peripheral Blood DNA Methylation-Based Machine Learning Models for Prediction of Knee Osteoarthritis Progression: Biologic Specimens and Data From the Osteoarthritis Initiative and Johnston County Osteoarthritis Project

OBJECTIVE

The lack of accurate biomarkers to predict knee osteoarthritis (OA) progression is a key unmet need in OA clinical research. The objective of this study was to develop baseline peripheral blood epigenetic biomarker models to predict knee OA progression.

METHODS

Genome-wide buffy coat DNA methylation patterns from 554 individuals from the Osteoarthritis Biomarkers Consortium (OABC) were determined using Illumina Infinium MethylationEPIC 850K arrays. Data were divided into model development and validation sets, and machine learning models were trained to classify future OA progression by knee pain, radiographic imaging, knee pain plus radiographic imaging, and any progression (pain, radiographic, or both). Parsimonious models using the top 13 CpG sites most frequently selected during development were tested on independent samples from participants in the Johnston County Osteoarthritis (JoCo OA) Project (n = 128) and a previously published Osteoarthritis Initiative (OAI) data set (n = 55).

RESULTS

Full models accurately classified future radiographic-only progression (mean ± SEM accuracy 87 ± 0.8%, area under the curve [AUC] 0.94 ± 0.004), pain-only progression (accuracy 89 ± 0.9%, AUC 0.97 ± 0.004), pain plus radiographic progression (accuracy 72 ± 0.7%, AUC 0.79 ± 0.006), and any progression (accuracy 78 ± 0.4%, AUC 0.86 ± 0.004). Pain-only and radiographic-only progressors were not distinguishable (mean ± SEM accuracy 58 ± 1%, AUC 0.62 ± 0.001). Parsimonious models showed similar performance and accurately classified future radiographic progressors in the OABC cohort and in both validation cohorts (mean ± SEM accuracy 80 ± 0.3%, AUC 0.88 ± 0.003 [using JoCo OA Project data], accuracy 80 ± 0.8%, AUC 0.89 ± 0.002 [using previous OAI data]).

CONCLUSION

Our data suggest that pain and structural progression share similar early systemic immune epigenotypes. Further studies should focus on evaluating the pathophysiologic consequences of differential DNA methylation and peripheral blood cell epigenotypes in individuals with knee OA.

Exploring Anatomo-Morphometric Characteristics of Infrapatellar, Suprapatellar Fat Pad, and Knee Ligaments in Osteoarthritis Compared to Post-Traumatic Lesions

Several studies have investigated cartilage degeneration and inflammatory subchondral bone and synovial membrane changes using magnetic resonance (MR) in osteoarthritis (OA) patients. Conversely, there is a paucity of data exploring the role of knee ligaments, infrapatellar fat pad (IFP), and suprapatellar fat pad (SFP) in knee OA compared to post-traumatic cohorts of patients. Therefore, the aim of this study was to analyze the volumetric and morphometric characteristics of the following joint tissues: IFP (volume, surface, depth, femoral and tibial arch lengths), SFP (volume, surface, oblique, antero−posterior, and cranio−caudal lengths), anterior (ACL) and posterior cruciate ligament (PCL) (volume, surface, and length), and patellar ligament (PL) (volume, surface, arc, depth, and length). Eighty-nine MR images were collected in the following three groups: (a) 32 patients with meniscal tears, (b) 29 patients with ACL rupture (ACLR), and (c) 28 patients affected by end-stage OA. Volume, surface, and length of both ACL and PCL were determined in groups a and c. A statistical decrease of IFP volume, surface, depth, femoral and tibial arch lengths was found in end-stage OA compared to patients with meniscal tear (p = 0.002, p = 0.008, p < 0.0001, p = 0.028 and p < 0.001, respectively) and patients with ACLR (p < 0.0001, p < 0.0001, p = 0.008 and p = 0.011, respectively). An increment of volume and surface SFP was observed in group b compared to both groups a and c, while no differences were found in oblique, antero−posterior, and cranio−caudal lengths of SFP among the groups. No statistical differences were highlighted comparing volume, surface, arc, and length of PL between the groups, while PL depth was observed to be decreased in end-OA patients compared with meniscal tear patients (p = 0.023). No statistical differences were observed comparing ACL and PCL lengths between patients undergoing meniscectomy and TKR. Our study confirms that IFP MR morphometric characteristics are different between controls and OA, supporting an important role of IFP in OA pathology and progression in accordance with previously published studies. In addition, PL depth changes seem to be associated with OA pathology. Multivariate analysis confirmed that OA patients had a smaller IFP compared to patients with meniscal tears, confirming its involvement in OA.

Infrapatellar fat pad volume and Hoffa-synovitis after ACL reconstruction: Association with early osteoarthritis features and pain over 5 years

Infrapatellar fat pad (IPFP) morphology and Hoffa-synovitis may be relevant to the development and progression of post-traumatic osteoarthritis (OA). We aimed to compare IPFP volume and Hoffa-synovitis in participants with anterior cruciate ligament reconstruction (ACLR) and uninjured controls, and to determine their association with prevalent and worsening early knee OA features and pain in participants post-ACLR. We assessed IPFP volume and Hoffa-synovitis from magnetic resonance imaging (MRI) in 111 participants 1-year post-ACLR and 20 uninjured controls. Patellofemoral and tibiofemoral cartilage and bone marrow lesions (BMLs) were assessed from MRIs at 1 and 5 years post-ACLR, and worsening defined as any longitudinal increase in lesion size/severity. IPFP volume and Hoffa-synovitis prevalence were compared between groups with analysis of covariance and χ ² tests, respectively. Generalized linear models assessed the relation of IPFP volume and Hoffa-synovitis to prevalent and worsening features of OA and knee pain (Knee injury and Osteoarthritis Outcome Score-Pain Subscale, Anterior Knee Pain Scale). No significant between-group differences were observed in IPFP volume (ACLR 34.39 ± 7.29cm³ , Control 34.27 ± 7.56cm³ ) and Hoffa-synovitis (ACLR 61%, Control 80%). Greater IPFP volume at 1-year post-ACLR was associated with greater odds of patellofemoral BMLs at 1-year (odds ratio [OR] [95% confidence intervals]: 1.104 [1.016, 1.200]) and worsening tibiofemoral cartilage lesions at 5-year post-ACLR (OR: 1.234 [1.026, 1.483]). Hoffa-synovitis at 1-year post-ACLR was associated with greater odds of worsening patellofemoral BMLs at 5-year post-ACLR (OR: 7.465 [1.291, 43.169]). In conclusion, IPFP volume and Hoffa-synovitis prevalence are similar between individuals 1-year post-ACLR and controls. Greater IPFP volume and Hoffa-synovitis appear to be associated with the presence and worsening of some early OA features in those post-ACLR, but not pain.

Quantitative volume and dynamic contrast-enhanced MRI derived perfusion of the infrapatellar fat pad in patellofemoral pain

Background

Patellofemoral pain (PFP) is a common knee condition and possible precursor of knee osteoarthritis (OA). Inflammation, leading to an increased perfusion, or increased volume of the infrapatellar fat pad (IPFP) may induce knee pain. The aim of the study was to compare quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters, as imaging biomarkers of inflammation, and volume of the IPFP between patients with PFP and controls and between patients with and without IPFP edema or joint effusion.

Methods

Patients with PFP and healthy controls were included and underwent non-fat suppressed 3D fast-spoiled gradient-echo (FSPGR) and DCE-MRI. Image registration was applied to correct for motion. The IPFP was delineated on FSPGR using Horos software. Volume was calculated and quantitative perfusion parameters were extracted by fitting extended Tofts' pharmacokinetic model. Differences in volume and DCE-MRI parameters between patients and controls were tested by linear regression analyses. IPFP edema and effusion were analyzed identically.

Results

Forty-three controls and 35 PFP patients were included. Mean IPFP volume was 26.04 (4.18) mL in control subjects and 27.52 (5.37) mL in patients. Median K_trans was 0.017 (0.016) min^-1 in control subjects and 0.016 (0.020) min^-1 in patients. None of the differences in volume and perfusion parameters were statistically significant. Knees with effusion showed a higher perfusion of the IPFP compared to knees without effusion in patients only.

Conclusions

The IPFP has been implicated as source of knee pain, but higher DCE-MR blood perfusion, an imaging biomarker of inflammation, and larger volume are not associated with PFP. Patient's knees with effusion showed a higher perfusion, pointing towards inflammation.

Role of adipose tissues in osteoarthritis

PURPOSE OF REVIEW

Epidemiologic studies reveal that the link between obesity and osteoarthritis cannot be uniquely explained by overweight-associated mechanical overload. For this reason, much attention focuses on the endocrine activity of adipose tissues. In addition to the systemic role of visceral and subcutaneous adipose tissues, many arguments highlight the involvement of local adipose tissues in osteoarthritis.

RECENT FINDINGS

Alteration in MRI signal intensity of the infrapatellar fat pad may predict both accelerated knee osteoarthritis and joint replacement. In this context, recent studies show that mesenchymal stromal cells could play a pivotal role in the pathological remodelling of intra-articular adipose tissues (IAATs) in osteoarthritis. In parallel, recent findings underline bone marrow adipose tissue as a major player in the control of the bone microenvironment, suggesting its possible role in osteoarthritis.

SUMMARY

The recent description of adipose tissues of various phenotypes within an osteoarthritic joint allows us to evoke their direct involvement in the initiation and progression of the osteoarthritic process. We can expect in the near future the discovery of novel molecules targeting these tissues.

Infrapatellar Fat Pad and Knee Osteoarthritis

Osteoarthritis is the most prevalent arthritis typically characterized by degradation of cartilage. However, its pathogenesis is not fully understood. Currently, osteoarthritis is best considered a disease of the whole "joint organ". Infrapatellar fat pad (IFP), an adipose tissue near synovium, is now attaching importance to researchers for its inflammatory phenotype. In this narrative review, a large body of evidence has been gathered for the involvement of IFP in the development of knee osteoarthritis. Additionally, the underlying mechanisms of how IFP can be involved in this process have been proposed. However, further investigations are needed to better understand its precise role in this process and its underlying mechanism, and beyond that, to develop new strategies to slow down the degenerative process and explore an effective and timely diagnosis of the disease.

Serum adipokines/related inflammatory factors and ratios as predictors of infrapatellar fat pad volume in osteoarthritis: Applying comprehensive machine learning approaches

OBJECTIVE

The infrapatellar fat pad (IPFP) has been associated with knee osteoarthritis onset and progression. This study uses machine learning (ML) approaches to predict serum levels of some adipokines/related inflammatory factors and their ratios on knee IPFP volume of osteoarthritis patients.

METHODS

Serum and MRI were from the OAI at baseline. Variables comprised the 3 main osteoarthritis risk factors (age, gender, BMI), 6 adipokines, 3 inflammatory factors, and their 36 ratios. IPFP volume was assessed on MRI with a ML methodology. The best variables and models were identified in Total-cohort (n = 678), High-BMI (n = 341) and Low-BMI (n = 337), using a selection approach based on ML methods.

RESULTS

The best model for each group included three risk factors and adipsin/C-reactive protein combined for Total-cohort, adipsin/chemerin; High-BMI, chemerin/adiponectin HMW; and Low-BMI, interleukin-8. Gender separation improved the prediction (13-16%) compared to the BMI-based models. Reproducibility with osteoarthritis patients from a clinical trial was excellent (R: female 0.83, male 0.95). Pseudocodes based on gender were generated.

CONCLUSION

This study demonstrates for the first time that the combination of the serum levels of adipokines/inflammatory factors and the three main risk factors of osteoarthritis could predict IPFP volume with high reproducibility, with the superior performance of the model accounting for gender separation.

Quantitative DCE-MRI demonstrates increased blood perfusion in Hoffa's fat pad signal abnormalities in knee osteoarthritis, but not in patellofemoral pain

OBJECTIVE

Infrapatellar fat pad (IPFP) fat-suppressed T2 (T2FS) hyperintense regions on MRI are an important imaging feature of knee osteoarthritis (OA) and are thought to represent inflammation. These regions are also common in non-OA subjects, and may not always be linked to inflammation. Our aim was to evaluate quantitative blood perfusion parameters, as surrogate measure of inflammation, within T2FS-hyperintense regions in patients with OA, with patellofemoral pain (PFP) (supposed OA precursor), and control subjects.

METHODS

Twenty-two knee OA patients, 35 PFP patients and 43 healthy controls were included and underwent MRI, comprising T2 and DCE-MRI sequences. T2FS-hyperintense IPFP regions were delineated and a reference region was drawn in adjacent IPFP tissue with normal signal intensity. After fitting the extended Tofts pharmacokinetic model, quantitative DCE-MRI perfusion parameters were compared between the two regions within subjects in each subgroup, using a paired Wilcoxon signed-rank test.

RESULTS

T2FS-hyperintense IPFP regions were present in 16 of 22 (73%) OA patients, 13 of 35 (37%) PFP patients, and 14 of 43 (33%) controls. DCE-MRI perfusion parameters were significantly different between regions with and without a T2FS-hyperintense signal in OA patients, demonstrating higher Ktrans compared to normal IFPF tissue (0.039 min-1 versus 0.025 min-1, p = 0.017) and higher Ve (0.157 versus 0.119, p = 0.010). For PFP patients and controls no significant differences were found.

CONCLUSIONS

IPFP T2FS-hyperintense regions are associated with higher perfusion in knee OA patients in contrast to identically appearing regions in PFP patients and controls, pointing towards an inflammatory pathogenesis in OA only.

KEY POINTS

• Morphologically identical appearing T2FS-hyperintense infrapatellar fat pad regions show different perfusion in healthy subjects, subjects with patellofemoral pain, and subjects with knee osteoarthritis. • Elevated DCE-MRI perfusion parameters within T2FS-hyperintense infrapatellar fat pad regions in patients with osteoarthritis suggest an inflammatory pathogenesis in osteoarthritis, but not in patellofemoral pain and healthy subjects.

A pilot study of peripheral blood DNA methylation models as predictors of knee osteoarthritis radiographic progression: data from the Osteoarthritis Initiative (OAI)

Knee osteoarthritis (OA) is a leading cause of chronic disability worldwide, but no diagnostic or prognostic biomarkers are available. Increasing evidence supports epigenetic dysregulation as a contributor to OA pathogenesis. In this pilot study, we investigated epigenetic patterns in peripheral blood mononuclear cells (PBMCs) as models to predict future radiographic progression in OA patients enrolled in the longitudinal Osteoarthritis Initiative (OAI) study. PBMC DNA was analyzed from baseline OAI visits in 58 future radiographic progressors (joint space narrowing at 24 months, sustained at 48 months) compared to 58 non-progressors. DNA methylation was quantified via Illumina microarrays and beta- and M-values were used to generate linear classification models. Data were randomly split into a 60% development and 40% validation subsets, models developed and tested, and cross-validated in a total of 40 cycles. M-value based models outperformed beta-value based models (ROC-AUC 0.81 ± 0.01 vs. 0.73 ± 0.02, mean ± SEM, comparison p = 0.002), with a mean classification accuracy of 73 ± 1% (mean ± SEM) for M- and 69 ± 1% for beta-based models. Adjusting for covariates did not significantly alter model performance. Our findings suggest that PBMC DNA methylation-based models may be useful as biomarkers of OA progression and warrant additional evaluation in larger patient cohorts.

Intra-articular corticosteroids and the risk of knee osteoarthritis progression: results from the Osteoarthritis Initiative

OBJECTIVE

A recent randomized clinical trial reported that repeated intra-articular corticosteroids (IACs) were associated with a greater cartilage loss. This study aimed to examine the relation of IACs to knee radiographic osteoarthritis (ROA) progression in a real-world setting.

DESIGN

A cohort that initiated IACs and a comparison cohort without IACs from participants with mild to moderate knee ROA in the Osteoarthritis Initiative (OAI) were assembled (from 0-month to 48-month). Two measures of knee ROA progression were assessed during the follow-up period: (1) an increase in Kellgren and Lawrence (KL) grade by ≥1 grade or having a knee replacement (i.e., KL grade worsening); and (2) a decrease in joint space width (JSW) by ≥0.7 mm or having a knee replacement (i.e., JSW worsening). The associations of IACs initiation using a propensity-score matched cohort study and continuous IACs using marginal structural models with the risk of knee ROA progression were examined.

RESULTS

Among 684 propensity-score matched participants at baseline (148 IACs initiators, 536 comparators), 65 knees (21.7/100 person-years) in the IACs initiation cohort and 90 knees (7.1/100 person-years) in the comparison cohort experienced KL worsening. The hazard ratios (HRs) of KL worsening from IACs initiation and continuous IACs were 3.02 (95% confidence interval [CI], 2.19-4.16) and 4.67 (95% CI, 2.92-7.47), respectively. The corresponding HRs of JSW worsening were 2.93 (95% CI, 2.13-4.02) and 3.26 (95% CI, 1.78-5.96), respectively. All HRs for continuous use of IACs were further away from the null.

CONCLUSIONS

IACs, especially continuous IACs, may be associated with an increased risk of knee ROA progression.

Deep convolutional neural network for segmentation of knee joint anatomy

PURPOSE

To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation.

METHODS

A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder-decoder network was designed as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification for 12 different joint structures. The 3D fully connected CRF was applied to regularize contextual relationship among voxels within the same tissue class and between different classes. The 3D simplex deformable modeling refined the output from 3D CRF to preserve the overall shape and maintain a desirable smooth surface for joint structures. The method was evaluated on 3D fast spin-echo (3D-FSE) MR image data sets. Quantitative morphological metrics were used to evaluate the accuracy and robustness of the method in comparison to the ground truth data.

RESULTS

The proposed segmentation method provided good performance for segmenting all knee joint structures. There were 4 tissue types with high mean Dice coefficient above 0.9 including the femur, tibia, muscle, and other non-specified tissues. There were 7 tissue types with mean Dice coefficient between 0.8 and 0.9 including the femoral cartilage, tibial cartilage, patella, patellar cartilage, meniscus, quadriceps and patellar tendon, and infrapatellar fat pad. There was 1 tissue type with mean Dice coefficient between 0.7 and 0.8 for joint effusion and Baker's cyst. Most musculoskeletal tissues had a mean value of average symmetric surface distance below 1 mm.

CONCLUSION

The combined CNN, 3D fully connected CRF, and 3D deformable modeling approach was well-suited for performing rapid and accurate comprehensive tissue segmentation of the knee joint. The deep learning-based segmentation method has promising potential applications in musculoskeletal imaging.

Quantitative MRI analysis of infrapatellar and suprapatellar fat pads in normal controls, moderate and end-stage osteoarthritis

The aim of this study was to analyze the magnetic resonance imaging (MRI) volumetric and morphometric characteristics of the infrapatellar fat pad (IFP) and the suprapatellar fat pad (SFP) in normal controls, moderate and end-stage osteoarthritis (OA) patients. Forty-four MRI images of the three groups were collected: a) 17 patients undergoing meniscectomy with Outerbridge score 0 (control group); b) 15 patients undergoing meniscectomy with Outerbridge score 3/4 (moderate OA group); and c) 12 patients undergoing total knee replacement (end-stage OA group). Volume, depth, femoral and tibial arch lengths of IFP were quantified. The hypointense IFP signals were also scored. The SFP volume, oblique, antero-posterior and cranio-caudal lengths were determined. IFP and SFP characteristics were compared between groups. A decrease of IFP volume, depth, femoral, and tibial arch lengths in moderate and end-stage OA compared to controls were observed. A difference in IFP hypointense signal was found between groups. No differences were found in SFP characteristics between the groups. In controls and moderate OA patients, correlations were found among the different MRI characteristics of both IFP and SFP, while in the end-stage OA group correlations were found only in SFP. We evidenced differences of the IFP MRI morphometric characteristics between the groups analyzed, supporting an important role of IFP in OA pathology and progression. On the contrary, no differences were highlighted in SFP analysis suggesting that this fat pad is not clearly involved in OA, probably due to its peculiar localization and different function.

Relationship Between Knee Pain and Infrapatellar Fat Pad Morphology: A Within- and Between-Person Analysis From the Osteoarthritis Initiative

OBJECTIVE

Inflammation is known to be strongly associated with knee pain in osteoarthritis. The infrapatellar fat pad represents a potential source of proinflammatory cytokines. Yet the relationship between infrapatellar fat pad morphology and osteoarthritis symptoms is unclear.

METHODS

Here we investigate quantitative imaging parameters of infrapatellar fat pad morphology between painful versus contralateral pain-free legs of subjects with unilateral knee pain and patients with chronic knee pain versus those of matched pain-free control subjects. A total of 46 subjects with strictly unilateral frequent knee pain and bilateral radiographic osteoarthritis (Kellgren/Lawrence grade 2/3) were drawn from the Osteoarthritis Initiative. Further, 43 subjects with chronic knee pain over 4 years and 43 matched pain-free controls without pain over this period were studied. Infrapatellar fat pad morphology (volume, surface area, and depth) was determined by manual segmentation of sagittal magnetic resonance images.

RESULTS

No significant differences in infrapatellar fat pad morphology were observed between painful versus painless knees of persons with strictly unilateral knee pain (mean difference -0.7% (95% confidence interval [95% CI] -0.6, 0.9; P = 0.64) or between chronically painful knees versus matched painless controls (-2.1% [95% CI -2.2, 1.1]; P = 0.51).

CONCLUSION

Independent of the ambiguous role of the infrapatellar fat pad in knee osteoarthritis (a potential source of proinflammatory cytokines or a mechanical shock absorber), the size of the infrapatellar fat pad does not appear to be related to knee pain.

Osteoarthritis year in review 2017: updates on imaging advancements

OBJECTIVE

This narrative review covers original research publications related to imaging advancements in osteoarthritis (OA) published in the English language between 1^st April 2016 and 30^th April 2017.

METHODS

Relevant human studies (excluding pre-clinical and in vitro studies), were searched and selected from PubMed database using the search terms of "osteoarthritis (OA)" in combination with "radiography", "magnetic resonance imaging (MRI)", "computed tomography (CT)", "ultrasound", "positron emission tomography (PET)," "single-photon emission computed tomography (SPECT)," and "scintigraphy". The included studies were sorted according to their relevance, novelty, and impact. Original research articles with both imaging advancements and novel clinical information were discussed in this review.

RESULTS

A large portion of the published studies were focused on MRI-based semi-quantitative and quantitative (morphological and structural) metrics of the knee joint to assess OA-related structural damages. New imaging technologies, such as PET, have been investigated for OA diagnosis and characterization, the delineation of predictive factors for OA progression, and to monitor the treatment responses.

CONCLUSION

Advanced imaging modalities play a pivotal role in OA research, and make a significant contribution to our understanding of OA diagnosis, pathogenesis, risk stratification, and prognosis.

Imaging of osteoarthritis-recent research developments and future perspective

In osteoarthritis research, imaging plays an important role in clinical trials and epidemiological observational studies. In this narrative review article, we will describe recent developments in imaging of osteoarthritis in the research arena, mainly focusing on literature evidence published within the past 3 years (2014-2017). We will primarily focus on MRI including advanced imaging techniques that are not currently commonly used in routine clinical practice, although radiography, ultrasound and nuclear medicine (radiotracer) imaging will also be discussed. Research efforts to uncover the disease process of OA as well as to discover a disease modifying OA drug continue. MRI continues to play a large role in these endeavors, while compositional MRI techniques will increasingly become important due to their ability to assess "premorphologic" biochemical changes of articular cartilage and other tissues in and around joints. Radiography remain the primary imaging modality for defining inclusion/exclusion criteria as well as an outcome measure in OA clinical trials, despite known limitations for visualization of OA features. Compositional MRI techniques show promise for predicting structural and clinical outcomes in OA research. Ultrasound can be a useful adjunct to radiography and MRI particularly for evaluation of hand OA. Newer imaging techniques such as hybrid PET/MRI may have a potential but require further research and validation.