Scoring Osteoarthritis Reliably in Large Joints and the Spine Using Whole-Body CT: OsteoArthritis Computed Tomography-Score (OACT-Score)

The natural history of protrusio acetabuli in Marfan syndrome and other hereditary connective tissue disorders: a 10-year follow-up CT study

OBJECTIVES

To explore the natural history of protrusio acetabuli (PA) in adults with Marfan syndrome (MFS) via a prospective 10-year follow-up study.

METHODS

2014 through 2015, 62 of 87 survivors from a nationwide cross-sectional study of 105 adults with presumed MFS were re-examined. At follow-up, MFS was diagnosed in 46 participants, and other hereditary connective tissue disorders in 12 participants. As in the baseline study, matched hospital controls were collected for comparison. CT images were obtained of the hips. PA was evaluated quantitatively and qualitatively. Measurements were performed according to the circle-wall distance method. The data was analysed with paired t test, and McNemar's test. A receiver operating characteristic (ROC) curve was constructed for the circle-wall distance.

RESULTS

There was no increase in the number of hips diagnosed with PA or in the circle-wall distance. PA was diagnosed in 58 of 87 hips in patients with MFS and in 71 of 111 hips in all patients with hereditary connective tissue disorders. Significantly more patients with MFS than controls had PA.

CONCLUSION

The prevalence and degree of PA remained unchanged after 10 years. The circle-wall distance seems to have a good ability to discriminate between individuals with MFS and individuals without any known connective tissue disorder. Suggested cutoff level for the circle-wall distance: 2 mm.

CLINICAL RELEVANCE

PA might be suggestive of a hereditary connective tissue disorder but does not develop or increase in adulthood. CT seems to have high sensitivity for PA and might be useful in a diagnostic process.

Aortic, musculoskeletal and organ characteristics on computed tomography in knee osteoarthritis - an explorative study in the IMI-APPROACH cohort

The systemic associations with knee osteoarthritis (KOA) are incompletely understood. This study explores aortic disease, musculoskeletal and organ findings in patients with KOA in relation to their symptoms or radiographic abnormalities. Full body computed tomography (CT) scans of 255 IMI-APPROACH participants were investigated using an automated analysis of multislice CT (Voronoi Health Analytics) that extracts aortic size and calcifications, and volumes and densities of bones, muscles, fat compartments and thoracic and abdominal organs. The CT measurements were primarily related to KOA as measured with Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), visual scores and automated knee radiograph analysis of osteophytes, bone sclerosis and joint space width. The median age was 67 years, body mass index (BMI) 26.8 kg/m2 and 78% were female. About half had Kellgren-Lawrence grade ≥ 2. Larger knee osteophyte area was associated with a larger aortic volume (RSpearman=0.21,P = 0.001), which can be due to elongation or dilatation. We observed an association between more symptoms and increased psoas (RSpearman=-0.23,P < 0.001) and lower leg (RSpearman=-0.23,P < 0.001) muscle density, suggesting less microscopic muscle fat. Symptomatic KOA was associated with substantially lower lung volume (771 ml difference between 50% worst and 50% best WOMAC), but not with visible lung disease. Lung volume and density were significantly associated with the physical functioning WOMAC component. These associations remained significant after adjustment for age, sex and BMI. KOA is associated with significant systemic changes, including altered aortic and organ volumes. These correlations suggest that KOA's impact may extend beyond the joints. Future research should explore the causal relationships and therapeutic implications associations.

MRI-based synthetic CT in the detection of knee osteoarthritis: Comparison with CT

Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1-weighted gradient-echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT- and sCT-based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter-reader agreement for tibiofemoral and patellofemoral joints were almost-perfect for both modalities (κ = 0.83-0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost-perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single-center study, sCT and CT were comparable for the scoring of knee OA.

CT in osteoarthritis: its clinical role and recent advances

Computed tomography (CT) is a widely available imaging method and considered as one of the most reliable techniques in bone assessment. Although CT has limited tissue contrast and needs radiation exposure, it has several advantages like fast scanning time and high spatial resolution. In this regard, CT has unique roles in osteoarthritis (OA) and its variable utilities have been reported. Hence, this review highlights the clinical role of CT in OA of representative joints. In addition, CT showed the several technical advancements recently, for example, acquiring the CT image with standing, obtaining the dual-energy data, and novel photon-counting detector development. Therefore, the recent studies and potential utility of these new CT systems in OA are also discussed.

Early knee OA definition-what do we know at this stage? An imaging perspective

While criteria for early-stage knee osteoarthritis (OA) in a primary care setting have been proposed, the role of imaging has been limited to radiography using the standard Kellgren-Lawrence classification. Standardized imaging and interpretation are critical with radiographs, yet studies have also shown that even early stages of radiographic OA already demonstrate advanced damage to knee joint tissues such as cartilage, menisci, and bone marrow. Morphological magnetic resonance imaging (MRI) shows degenerative damage earlier than radiographs and definitions for OA using MRI have been published though no accepted definition of early OA based on MRI is currently available. The clinical significance of structural abnormalities has also not been well defined, and the differentiation between normal aging and structural OA development remains a challenge. Compositional MRI of cartilage provides information on biochemical, degenerative changes within the cartilage matrix before cartilage defects occur and when cartilage damage is potentially reversible. Studies have shown that cartilage composition can predict cartilage loss and radiographic OA. However, while this technology is most promising for characterizing early OA it has currently limited clinical application. Better standardization of compositional MRI is required, which is currently work in progress. Finally, there has been renewed interest in computed tomography (CT) for assessing early knee OA as new techniques such as weight bearing and spectral CT are available, which may provide information on joint loading, cartilage, and bone and potentially have a role in better characterizing early OA. In conclusion, while imaging may have a limited role in diagnosing early OA in a primary care setting, there are advanced imaging technologies available, which detect early degeneration and may thus significantly alter management as new therapeutic modalities evolve.

Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT

We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images.

INTRODUCTION

In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT).

METHODS

The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images.

RESULTS

Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75-0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67-0.97].

CONCLUSION

CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.

Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort: prevalence and phenotyping

Objectives

Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component.

Methods

Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component.

Results

OA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different.

Conclusions

OA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments.

Trial registration number

The study is registered under clinicaltrials.gov nr: NCT03883568.