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

Intra-Articular Injection of Human Umbilical Cord-Derived Mesenchymal Stromal Cells Reduces Radiographic Osteoarthritis in an Ovine Model

OBJECTIVE

To determine if mesenchymal stromal cells (MSCs) derived from human umbilical cords (hUC) could reduce degeneration developing when injected into the knee of a large animal model of osteoarthritis (OA).

DESIGN

Ten million culture-expanded UC-MSCs (pooled from 3 human donors) were injected in 50 μL of tissue culture medium into the left stifle joints of 7 sheep whose medial meniscus was transected 4 weeks previously. Seven other sheep had only 50 μL of medium injected as the no treatment "control" group. After 8 weeks the sheep underwent euthanasia, the joints were excised and examined macroscopically, via x-ray and magnetic resonance imaging (MRI), both via histology for degenerative and inflammatory changes and immunohistochemically to identify any human cells within the joint tissues. Activity monitoring both before meniscus transection and euthanasia was also undertaken.

RESULTS

There was a significant reduction in the Kellgren-Lawrence x-ray score for joints injected with hUC-MSCs compared with the control joints. Likewise, macroscopic, MRI, synovitis and OARSI histology scores were all lower (better) in the joints injected with hUC-MSCs than in the control arm, but not significantly. Activity levels and synovitis scores were similar in both groups of animals.

CONCLUSIONS

hUC-MSCs appear to modify and reduce the development of osteoarthritic changes in the ovine stifle joint after meniscal destabilization, an injury which commonly leads to OA in humans. These results are encouraging for the potential benefit of culture expanded UC-MSCs as an allogeneic cell therapy in patients who may have early OA following a meniscal injury of the knee.

Associations of Longitudinal Multiparametric MRI Findings and Clinical Outcomes in Intra-Articular Injections for Knee Osteoarthritis

BACKGROUND

Osteoarthritis (OA) is a complex disease marked by the degradation of articular cartilage.

OBJECTIVE

This study aimed to explore the relationship between cartilage volume/thickness and clinical outcomes in knee OA patients treated with intra-articular injections over one year.

METHODS

Twenty-four patients with mild-to-moderate OA were retrospectively analyzed using knee MRI. OA features were assessed semiquantitatively with the Whole-Organ Magnetic Resonance Imaging Score (WORMS), while cartilage thickness and volume in the medial femoral condyle (MFC) and medial tibial plateau (MTP) were measured. T1ρ and T2 values for MFC cartilage were also recorded. Clinical outcomes were evaluated using the Korean Western Ontario and McMaster Universities (K-WOMAC) and Knee Injury Osteoarthritis Outcomes (KOOS) scores. Spearman's rank test assessed the associations between imaging changes and clinical outcomes.

RESULTS

The baseline MTP and MFC cartilage thickness and MTP cartilage volume showed significant correlations with clinical outcomes. Additionally, less progressive cartilage loss in the medial femorotibial joint (MFTJ) and overall joint was linked to a better clinical response over 12 months.

CONCLUSIONS

In conclusion, thicker baseline MFTJ cartilage and minimal cartilage loss were associated with favorable clinical outcomes in knee OA patients receiving intra-articular injections.

A Comprehensive Review on MRI-based Knee Joint Segmentation and Analysis Techniques

Using magnetic resonance imaging (MRI) in osteoarthritis pathogenesis research has proven extremely beneficial. However, it is always challenging for both clinicians and researchers to detect morphological changes in knee joints from magnetic resonance (MR) imaging since the surrounding tissues produce identical signals in MR studies, making it difficult to distinguish between them. Segmenting the knee bone, articular cartilage and menisci from the MR images allows one to examine the complete volume of the bone, articular cartilage, and menisci. It can also be used to assess certain characteristics quantitatively. However, segmentation is a laborious and time-consuming operation that requires sufficient training to complete correctly. With the advancement of MRI technology and computational methods, researchers have developed several algorithms to automate the task of individual knee bone, articular cartilage and meniscus segmentation during the last two decades. This systematic review aims to present available fully and semi-automatic segmentation methods for knee bone, cartilage, and meniscus published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field of image analysis and segmentation, which helps the development of novel automated methods for clinical applications. The review also contains the recently developed fully automated deep learning-based methods for segmentation, which not only provides better results compared to the conventional techniques but also open a new field of research in Medical Imaging.

Sim2Ls: FAIR simulation workflows and data

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls, a computational environment for developers and users, and the hardware to execute runs and store results at no cost. We exemplify the use of Sim2Ls using two applications and discuss best practices towards FAIR simulation workflows and associated data.

Osteoarthritis year in review 2021: imaging

PURPOSE

To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging.

METHODS

The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further.

RESULTS

The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging.

CONCLUSION

Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.

3D MRI in Osteoarthritis

Osteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.

Use of Computational Modeling to Study Joint Degeneration: A Review

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient’s individualized risk assessment as screening tool for use in clinical practice.