Osteoarthritis year in review 2017: updates on imaging advancements

Hafezi-Nejad, Nima; Demehri, Shadpour; Guermazi, Ali; Carrino, John A.

doi:10.1016/j.joca.2018.01.007

Cited by 33 publications

(23 citation statements)

References 69 publications

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“…Osteoarthritis (OA) is a degenerative disease on joints that seriously affects the quality of life of patients and is clinically characterized by slow progression of joint pain, tenderness, stiffness, swelling, limited movement, and joint deformity . OA has become one of the most common joint diseases and a major cause of walking disorders in the elderly . Although articular cartilage damage has long been considered the main characteristic of OA, it is generally believed that OA involves abnormalities of multiple tissues within the joint including articular cartilage, synovium, and subchondral bone .…”

Section: Introductionmentioning

confidence: 99%

“…(1) OA has become one of the most common joint diseases and a major cause of walking disorders in the elderly. (1,2) Although articular cartilage damage has long been considered the main characteristic of OA, it is generally believed that OA involves abnormalities of multiple tissues within the joint including articular cartilage, synovium, and subchondral bone. (3,4) The sequence by which these changes develop and how they contribute to the occurrence and progression of disease remain controversial.…”

Section: Introductionmentioning

confidence: 99%

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Articular Cartilage Degradation and Aberrant Subchondral Bone Remodeling in Patients with Osteoarthritis and Osteoporosis

Chu

Liu

et al. 2019

Journal of Bone and Mineral Research

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Osteoarthritis (OA) and osteoporosis (OP) are two skeletal disorders associated with joint structures. Occasionally, OA and OP occur in the same patient. However, the effect of OP changes on OA progression in patients with osteoporotic OA (OP‐OA) has not been reported, especially the potential association between subchondral bone and articular cartilage. Thus we investigated the alterations in the microstructure, biomechanical properties, and remodeling of subchondral bone as well as their association with cartilage damage in the hip joint of patients with OP‐OA. Thirty‐nine femoral head specimens were obtained from patients who underwent total hip arthroplasty (OA group, n = 19; OP‐OA group, n = 20), and healthy specimens from cadaver donors were used (control group, n = 10). The microstructure and biomechanical properties of subchondral bone were evaluated by micro–computed tomography and micro–finite‐element analysis. Histology, histomorphometric measurements, and immunohistochemistry were used to assess subchondral bone remodeling and cartilage damage. Linear regression analysis was performed to elucidate the relationship between subchondral bone and articular cartilage. In the subchondral bone of the OP‐OA group, compared with that of the OA group, aberrant bone remodeling leads to an inferior microstructure and worsening biomechanical properties, potentially affecting transmission of loading stress from the cartilage to the subchondral bone, and then resulting in accelerated OA progression in patients with OP‐OA. The results indicate that changes in subchondral bone could affect OA development and the improvement in subchondral bone with bone‐metabolism agents may help mitigate OA progression when OP and OA coexist in the same patients. © 2019 American Society for Bone and Mineral Research.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Articular Cartilage Degradation and Aberrant Subchondral Bone Remodeling in Patients with Osteoarthritis and Osteoporosis

Chu

Liu

et al. 2019

Journal of Bone and Mineral Research

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“…Photons from this layer are most likely to be singly-scattered, retaining birefringence information [62]. The clinical potential of polarized reflectance signals and the Pol parameter in orthopedics is high despite well-developed tomographic and three-dimensional imaging techniques, including OCT, laser scanning microscopy, x-ray computed tomography (CT), and magnetic resonance imaging (MRI) [63][64][65][66][67]. The non-invasive medical imaging techniques of CT and MRI have poorer resolution and more difficulty in resolving microscale articular surface and subsurface features that optical approaches, which require arthroscopy or excision of grafts and explants [65,68,69].…”

Section: Discussionmentioning

confidence: 99%

Polarized reflectance from articular cartilage depends upon superficial zone collagen network microstructure

Huynh¹,

Pesante²,

Nehmetallah³

et al. 2019

Biomed. Opt. Express

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Polarized reflectance from articular cartilage involves light scattering dependent on surface features, sub-surface optical properties, and collagen birefringence. To understand how surface roughness, zonal collagen microstructure, and chondrocyte organization contribute to polarized reflectance signals, experiments were conducted on bovine cartilage explants and osteochondral cores to compare polarized reflectance texture with split lines and relate these signals to cartilage zonal features and chondrocyte distribution. Texture parameter sensitivity to articular surface damage was determined from polarized reflectance maps and optimized to detect surface damage. Results indicate that polarized reflectance texture predominantly derives from the superficial zone collagen network, while the parameter average value also depends on surface roughness and total cartilage thickness.

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“…), and computational notebooks used to compute results and create graphs and tables [19,21] for more transparent research.Transparency in image-based research is crucial to provide meaningful and reliable answers to medical and biological questions [50]. In the musculoskeletal field, quantitative analysis from magnetic resonance (MR) imaging has assumed an increasingly important role in investigating osteoarthritis (OA) [22]. OA is the most common joint disease worldwide, affecting about 2 in 10 women and 1 in 10 men over 60 years of age [76].…”

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confidence: 99%

pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

Bonaretti

Gold

Beaupré

2019

Preprint

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Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics, 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions, and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings. IntroductionOpen science and computational reproducibility are recent movements in the scientific community that aim to promote and encourage transparent research. They are supported by national and international funding agencies, such as the United States National Institutes of Health (NIH) [9] and the European Commission [10]. Open science refers to the free availability of data, software, and methods developed by researchers with the aim to share knowledge and tools [75]. Computational reproducibility is the ability of researchers to duplicate the results of a previous study, using the same data, software, and methods used by the original authors [5]. Openness and reproducibility are essential to researchers to assess the accuracy of scientific claims [55], build on the work of other scientists with confidence and efficiency (i.e. without "reinventing the wheel") [54], and collaborate to improve and expand robust scientific workflows to accelerate scientific discoveries [53,14,42]. Historically, research data, tools, and processes were rarely openly available because of limited storage and computational power [42]. Nowadays, there are several opportunities to conduct transparent research: data repositories (e.g. Zenodo and FigShare), code repositories (e.g. GitHub, Git...

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Osteoarthritis year in review 2017: updates on imaging advancements

Abstract: 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.

Cited by 33 publications

References 69 publications

Articular Cartilage Degradation and Aberrant Subchondral Bone Remodeling in Patients with Osteoarthritis and Osteoporosis

Articular Cartilage Degradation and Aberrant Subchondral Bone Remodeling in Patients with Osteoarthritis and Osteoporosis

Polarized reflectance from articular cartilage depends upon superficial zone collagen network microstructure

pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

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