Introduction

Big data in imaging

Content

Big data in imaging

Ali Guermazi, MD, PhD

Boston University School of Medicine, Boston, USA

Osteoarthritis (OA) is nowadays considered as a disease that has a spectrum, from triggering event that initiates the disease process, ‘preclinical’ OA (molecular changes in the composition of bone, cartilage, and other soft tissues), clinically detectable OA (pre-radiographic and radiographic OA), and end-stage OA that requires joint replacement. Moreover, different phenotypes of OA have been recognized to account for seemingly heterogeneous pattern of disease progression in different patients. While there are more than one phenotypic classifications proposed in the literature, one such example includes 5 phenotypes: post-traumatic, metabolic, ageing, genetic, and pain. Especially for the purpose of OA clinical trials, it seems increasingly important to recognize different phenotypes of OA so that researcher can target certain subgroups of OA patients that are most suitable for the drug being tested. The most widely used imaging modality for OA assessment if radiography. Semiquantitative evaluation of OA severity can be done using Kellgren and Lawrence grading or OARSI atlas grading. Radiograph-based outcomes are still the only currently FDA-recommended imaging-based outcomes. OARSI also recommends radiographic joint space width as an option for trials of structural modifications. However, we do need to be aware of limitations of radiography. More advanced imaging of OA can be done with MRI, including conventional MRI for assessment of morphologic changes and compositional MRI for detection of ‘pre-morphologic’ physiologic/biochemical changes. Morphologic changes can be evaluated using semiquantitative or quantitative approach, and articular and periarticular structures can be assessed, such as cartilage damage, meniscal tear, synovitis and effusion, bone marrow lesions, and ligamentous damage. Examples of compositional imaging include dGEMRIC, T2 mapping, T1rho mapping, sodium imaging, and diffusion imaging. These techniques are particularly useful for early and pre-clinical stage of OA. In OA research, population-based studies help researchers analyze and understand causative factors of OA, disease mechanisms of OA, and prevalence of OA in different and specific populations. There are multitude of population-based OA studies, such as: Framingham OA study (USA), Rotterdam study (Netherlands), Model of Early Diagnosis of Knee OA (MoDEKO) and Knee OA Progression (KOAP) studies (Canada), Hallym Ageing study (Korea), Tasmania study (Australia), Beijing study (China), and Research on Osteoarthritis-Osteoporosis Against Disability (ROAD) study (Japan), to name but a few. In addition, there are non-population based epidemiological studies. Examples are Osteoarthritis Initiative, Pivotal Osteoarthritis Initiative Magnetic Resonance Imaging Analyses (POMA), Foundation for the NIH Osteoarthritis Biomarkers Consortium, Multicenter Osteoarthritis Study (MOST), Boston Osteoarthritis of the Knee Study (BOKS), and Mechanical Factors in Arthritis of the Knee (MAK). These studies focus on specific subcohorts of a given population usually those at risk to develop disease or with established OA. This allows enriching populations and decrease the required number of subjects, which are typically large for a population-based study because many subjects are ‘normal’ or not at risk of OA. With the current popularity and increasing importance of artificial intelligence (machine learning), researchers are now applying AI algorithms to existing big data of OA. Recently published papers focus on key issues such as detection of early OA, prediction of OA disease progression and total knee arthroplasty, and identification of risk factors for OA progression.

Introduction

What do we see in Imaging after different Cartilage Procedures?

Content

What do we see in Imaging after different Cartilage Procedures?

Ali Guermazi, MD, PhD

Boston University School of Medicine, Boston/MA, USA

Since 1994, when the first autologous chondrocyte transplantation surgery was described by Brittberg and colleagues, knee cartilage repair surgery has evolved rapidly thanks to many factors including improvement of pre-surgical assessment, imaging techniques, increased availability of matrix products including both fresh and frozen allografts, and focused research on the clinical outcomes. Cartilage repair surgery is aimed to alleviate patient symptoms, to promote cartilage healing, and to prevent or delay the onset of osteoarthritis. There are still a variety of barriers (including cost, regulatory, insurance, and logistical issues) between new cartilage repair products/techniques and their routine clinical applications. However, over the recent years there have been significant advances in our scientific knowledge in regards to cartilage repair techniques and imaging methods for evaluating post-operative repair status. Different MRI techniques are available to assess post-operative cartilage. Conventional, morphological MRI sequences include 2-dimensional (2D) and 3-dimensional (3D) fast spin echo (FSE) sequences provide excellent signal to noise ratio, contrast between tissues, and faster acquisition times. 2D-FSE is the core imaging technique and is part of the cartilage imaging protocol recommended by International Cartilage Repair Society. Isotropic, or near isotropic, 3D sequences can produce higher spatial resolution and high quality reformatted images in any plane, and are thus advantageous over 2D-FSE for shorter image acquisition time. Gradient echo type sequences such as SPGR, DESS are excellent for cartilage segmentation and quantification of cartilage volume and thickness due to the good image contrast between cartilage and subchondral bone, but not ideal for focal cartilage defect evaluation. There are semiquantitative MRI scoring tools for assessment of post-operative cartilage after repair surgery. One is called MRI Observation of Cartilage Repair Tissue (MOCART) and its usefulness in randomized controlled clinical trials of autologous cartilage tissue implants has been demonstrated. MOCART is comprehensive for assessment of the repair site itself; however, assessment of the other structures of the joint is paramount to assess longitudinal outcomes and development of osteoarthritis. The cartilage repair OA knee score (CROAKS) combines features of MOCART and the MRI osteoarthritis knee score (MOAKS), which is an established semi-quantitative scoring system for whole organ assessment of the knee, to provide a comprehensive, reproducible tool for longitudinal postoperative assessment after surgical cartilage. Compositional MRI acquisitions provide a way to detect biochemical and microstructural changes in the cartilage extracellular matrix even before gross morphological changes occur. Although not in routine clinical use, these techniques have been used extensively in cartilage research. Compositional MRI can supplement morphologic imaging, by potentially defining the biomechanical quality of cartilage repair tissue. Available surgical cartilage repair techniques include microfracture/marrow stimulation, osteochondral autograft/allograft (OATS) transplantation, particulate cartilage allograft, autologous chondrocyte implantation (ACI), open reduction and internal fixation of a large osteochondral lesion, and femoral condyle transplantation. All cartilage repair techniques have the same primary goal; to decrease pain symptoms, improve mobility and function, and to prevent the progression of osteoarthritis. These cartilage repair surgery techniques have shown to improve functional outcomes, however, there is urgent need to define outcomes clinically and by MRI measurements including local assessment and in regard to long-term osteoarthritis development/progression. In clinical practice, the MRI assessment of repair tissue relies heavily on morphologic imaging. Compositional MRI provides the opportunity to measure the biochemical and microstructural time-dependent processes of maturation occurring within the cartilage repair tissue. Compositional MRI techniques hold great promise for the clinical determination of surgical success, although such techniques are still limited for research use. Before they can become routinely used in clinical practice, however, compositional MRI techniques needs to be standardized and validated for post-operative cartilage repair tissue evaluation and made time efficient. The combination of MRI–based morphologic and compositional imaging plays a key role in post-operative assessment of cartilage repair tissue and its integration to native tissues.