Bone area provides a responsive outcome measure for bone changes in short-term knee osteoarthritis studies

Objective: This post-hoc study analyzed 3D bone area from an osteoarthritis (OA) cohort demonstrating no change in cartilage thickness. Methods: 27 women with painful medial knee OA had MRI at 0, 3 and 6 months. Images were analysed using active appearance models. Results: At 3 and 6 months the mean change in medial femoral bone area was 0.34% [95% CI 0.04, 0.64] and 0.61% [CI 0.32, 0.90]. 40% of subjects had progression > SDD at 6 months. Conclusion: In this small cohort at high risk of OA progression, bone area changed at 3 and 6 months when cartilage morphometric measures did not

Osteoarthritic bone marrow lesions almost exclusively colocate with denuded cartilage: a 3D study using data from the Osteoarthritis Initiative

Objectives: The aetiology of bone marrow lesions (BMLs) in knee osteoarthritis (OA) is poorly understood. We employed three-dimensional (3D) active appearance modelling (AAM) to study the spatial distribution of BMLs in an OA cohort and compare this with the distribution of denuded cartilage. Methods: Participants were selected from the Osteoarthritis Initiative progressor cohort with Kellgren–Lawrence scores ≥2, medial joint space narrowing and osteophytes. OA and ligamentous BMLs and articular cartilage were manually segmented. Bone surfaces were automatically segmented by AAM. Cartilage thickness of <0.5 mm was defined as denuded and ≥0.5–1.5 mm as severely damaged. Non-quantitative assessment and 3D population maps were used for analysing the comparative position of BMLs and damaged cartilage. Results: 88 participants were included, 45 men, mean age (SD) was 61.3 (9.9) years and mean body mass index was 31.1 (4.6) kg/m2. 227 OA and 107 ligamentous BMLs were identified in 86.4% and 73.8% of participants; OA BMLs were larger. Denuded cartilage was predominantly confined to a central region on the medial femur and tibia, and the lateral facet of the trochlear femur. 67% of BMLs were colocated with denuded cartilage and a further 21% with severe cartilage damage. In the remaining 12%, 25/28 were associated with cartilage defects. 74% of all BMLs were directly opposing (kissing) another BML across the joint. Conclusions: There was an almost exclusive relationship between the location of OA BML and cartilage denudation, which itself had a clear spatial pattern. We propose that OA, ligamentous and traumatic BMLs represent a bone response to abnormal loading

Physiopathology and Intervention in Osteoarthritis: A Systematic Review

In the United States, osteoarthritis (OA) is the most common chronic illness in the adult population affecting an estimated 27 million individuals with a yearly health care cost of over $150 billion (CDC, 2014; Lawrence et al., 2008). The pathological osteoarthritic process results in the progressive degradation of articular cartilage due to chemical and biological imbalances within a joint (Weiland et al., 2005). These imbalances are not well understood and neither are the biomechanical joint changes that occur as a result. Due to these limitations, treating and monitoring this condition is a challenge to clinicians and the processes are currently inefficient. The purpose of this targeted literature review is to identify the main factors contributing to OA, identify the state of the art in diagnosis and physical therapy treatment in OA and to identify the role of animal models in OA research. To accomplish this, 76 peer reviewed journal articles on the relationship between musculoskeletal biomechanics and osteoarthritis have been selected for analysis. Articles were generated from search criteria with key words osteoarthritis, diagnosis, physical therapy, and animal model from the following databases: PubMed, Cochrane Library, ISI Web of Knowledge, and Academic Search Complete. In conclusion, it was found that OA is a multifactorial disease leading to joint failure from abnormal biomechanics, however the exact pathogenesis remains unknown. There is also no quintessential diagnostic tool for OA, however WOMAC score reporting is recommended to monitor patient progress. For conservative treatment, there is also no gold standard protocol but a multimodal approach is necessary to optimize the loading on the pathological joint. Non-invasive animal models will be essential for the future of intervention research regarding OA to assess disease onset and progression in an attempt to translate these findings into a human population

Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee MRI

The detection of cartilage loss due to disease progression in Osteoarthritis remains a challenging problem. We have shown previously that the sensitivity of detection from 3D MR images can be improved significantly by focusing on regions of `at risk' cartilage defined consistently across subjects and time-points. We define these regions in a frame of reference based on the bones, which requires that the bone surfaces are segmented in each image, and that anatomical correspondence is established between these surfaces. Previous results has shown that this can be achieved automatically using surface-based Active Appearance Models (AAMs) of the bones. In this paper we describe a method of refining the segmentations and correspondences by building a volumetric appearance model using the minimum message length principle. We present results from a study of 12 subjects which show that the new approach achieves a significant improvement in segmentation accuracy compared to the surface AAM approach, and reduce the variance in cartilage thickness measurements for key regions of interest. The study makes use of images of the same subjects obtained using different vendors' scanners, and also demonstrates the feasibility of multi-centre trial

Sviluppo di un tool automatico per l\u2019individuazione con risonanza magnetica del livello di attivit\ue0 di malattia nei pazienti affetti da artrite idiopatica giovanile in remissione clinica

INTRODUZIONE Il principale obiettivo del trattamento dei pazienti affetti da Artrite Idiopatica Giovanile (AIG) \ue8 quello di indurre la remissione clinica della malattia, fondamentale per prevenire la progressione del danno articolare e la conseguente disabilit\ue0 funzionale. Lo stato di remissione clinica viene valutato dal medico sulla base dell\u2019esame obiettivo (sinovite) del paziente e degli indici di infiammazione (VES o PCR). \uc8 noto, tuttavia, che l\u2019esame clinico possa non essere sufficientemente sensibile per l\u2019identificazione della presenza di sinovite sub clinica ossia non evidenziabile con l\u2019esame obiettivo del paziente [23]. La RM viene considerata la metodica di riferimento per l\u2019individuazione del processo infiammatorio a carico della membrana sinoviale. In un recente studio condotto presso il nostro istituto in una coorte di 90 pazienti affetti da AIG in remissione clinica, la RM ha dimostrato la presenza di sinovite subclinica nel 63% dei pazienti. \uc8\u2019 stato inoltre dimostrato che la persistenza del processo infiammatorio a livello della membrana sinoviale era significativamente correlato alla ricaduta della malattia e ad una progressione del danno strutturale a livello articolare. I metodi qualitativi e manuali ad oggi adoperati per la lettura delle RM articolari richiedono un notevole impegno temporale e presentano una soggettivit\ue0 intrinseca. Per supportare e superare i limiti dovuti alla valutazione qualitativa, operatore dipendente, sarebbe utile sviluppare dei metodi di segmentazione automatica delle immagini in grado di valutare la presenza di un processo infiammatorio a livello articolare in maniera obiettiva e riproducibile. OBIETTIVI Sviluppo di un metodo di segmentazione automatica delle immagini da RM che individui la presenza di sinovite subclinica in una coorte di pazienti con AIG in remissione clinica. METODI Il progetto ha previsto una revisione della letteratura sui metodi ad oggi proposti per l\u2019analisi automatizzata delle RM articolari nei pazienti affetti da artrite infiammatoria cronica. Per l\u2019analisi delle immagini mediche sono state adoperate le librerie \u201cInsight Toolkit\u201d (ITK), ed il tool ITK-SNAP rispettivamente per la segmentazione automatica e semi-automatica. ITK \ue8 una libreria open-source ampiamente adoperata per lo sviluppo di software di segmentazione e registrazione di immagini. ITK-SNAP \ue8 un\u2019applicazione software anch\u2019essa open-source usata per segmentare le strutture nelle immagini 3D e fornisce una segmentazione semi-automatica adoperando metodi di \u201cactive-countour\u201d (contorni attivi). Lo scopo dell\u2019utilizzo di questo tool \ue8 quello di estendere una segmentazione in modalit\ue0 singola ad una pipeline che combina la preelaborazione multimodale guidata dall\u2019utente e la segmentazione di oggetti di set di livelli (level-set) in cui si combinano in maniera congiunta tutte le informazioni provenienti da pi\uf9 canali. Le sequenze 3D-SPIR ottenute dopo la somministrazione del mezzo di contrasto, in 15 pazienti affetti da AIG con diversi gradi di severit\ue0 della malattia sono state valutate dapprima con una segmentazione semi-automatica e successivamente validate da una segmentazione manuale effettuata da un Radiologo Pediatra. Le stesse sequenze sono state elaborate mediante un metodo completamente automatizzato. Dapprima una pipeline di segmentazione basata su atlante \ue8 stata sviluppata allo scopo di registrare la sequenza di immagini da elaborare con delle immagini di riferimento (atlanti) opportunamente selezionate, l\u2019obiettivo di questa prima segmentazione \ue8 stato quello di individuare la regione di interesse e definire i markers; questi ultimi costituiscono l\u2019input della seconda segmentazione basata sul metodo dei contorni attivi dei nuovi markers ottenuti dalla precedente segmentazione. Il metodo automatico \ue8 stato anch\u2019esso confrontato con i risultati ottenuti dalla segmentazione manuale e semi-automatico e successivamente testato su un dataset pi\uf9 ampio di 30 pazienti con AIG in remissione clinica. RISULTATI Le RM (10 polsi e 5 articolazioni coxo-femorali) ottenute da 15 pazienti con AIG (M:5, 33%; F:10, 67%) sono state utilizzate per lo sviluppo di un metodo di segmentazione automatica per l\u2019identificazione e quantificazione della sinovite. Inizialmente \ue8 stato implementato un approccio di analisi semi automatica che rappresenta un buon compromesso tra affidabilit\ue0 e velocit\ue0 di individuazione dell\u2019area interessata dall\u2019attivit\ue0 della malattia; questo studio ha consentito di testare due differenti approcci algoritmici sullo stesso set di immagini ed individuare i parametri di segmentazione che pi\uf9 si avvicinano alla segmentazione manuale effettuata dal pediatra radiologo. La segmentazione \ue8 stata giudicata soddisfacente in 11 su 15 RM (73%) con il metodo della segmentazione di Threshold, soddisfacente in 10 su 15 RM (67%) con l\u2019utilizzo del metodo dei Cluster, operate entrambe dal tool ITK-SNAP. L\u2019identificazione dei parametri pi\uf9 significativi per eseguire la segmentazione semiautomatica \ue8 stata fondamentale per implementare il tool per automatizzare il processo di segmentazione dell\u2019articolazione. La soggettivit\ue0 dell\u2019operatore, con la selezione dell\u2019area di interesse, dei punti di markers e con la regolazione dei parametri della segmentazione, incide notevolmente sulla qualit\ue0 dell\u2019elaborazione eseguita. A tale scopo si \ue8 sviluppato un metodo completamente automatizzato, quindi intrinsecamente pi\uf9 riproducibile perch\ue9 eseguito esclusivamente dal calcolatore. Il tool per la identificazione automatica della sinovia infiammata \ue8 stato sottoposto ad una validazione preliminare su una corte di 30 RM (20 polsi e 10 bacini) ottenute da pazienti con AIG in remissione clinica. Di questi pazienti 5/30 (17%) erano maschi e 10/30 (33%) erano femmine. La durata mediana di malattia al momento di inclusione dello studio era di 8.5 anni; l\u2019et\ue0 mediana dei pazienti alla visita basale era di 13.8 anni. Di questi 30 pazienti sono state elaborate le articolazioni: 20/30 (67%) di polso e 10/30 (33%) coxo-femorali. La sinovite subclinica \ue8 stata identificata in 15/30 (50%) dei pazienti esaminati. La concordanza tra la lettura del radiologo pediatra e il metodo semi-automatico \ue8 stata valutata essere 86.66% (Cohen K=0.728). La concordanza tra la lettura del radiologo pediatra ed il metodo automatico \ue8 risultata essere dell\u201983.33% (Cohen K=0.663). CONCLUSIONI I risultati di questo studio suggeriscono che il metodo completamente automatico per l\u2019individuazione della sinovite, basato sulla registrazione delle immagini di risonanza \u201catlas-based\u201d, \ue8 affidabile per individuare la persistenza di un processo infiammatorio a livello articolare in pazienti con AIG in remissione clinica. In questo studio \ue8 stata adoperata la piattaforma ITK per le sue caratteristiche \u2019aperte\u2019 che la rendono un ottimo strumento di ricerche; la piattaforma ITK, infatti, fornisce un gran numero di procedure di registrazione/segmentazione adattabili alle caratteristiche del dato in esame. La valutazione automatica \ue8 significativamente pi\uf9 rapida rispetto a quella manuale e pi\uf9 obiettiva (meno operatore dipendente). Per queste caratteristiche appare un promettente strumento da impiegare nelle sperimentazioni cliniche atte a valutare l\u2019efficacia dei nuovi farmaci antireumatici nell\u2019indurre la remissione della malattia. Sono necessarie ulteriori indagini di approfondimento e di test allo scopo di ampliare il database di immagini di registrazione ed estendere l\u2019analisi ad altre articolazioni come ginocchio e caviglia. La segmentazione automatica dei tessuti articolari e muscolo-scheletrici \ue8 ancora una sfida importante per l'elaborazione delle immagini mediche. Con la standardizzazione nell'acquisizione RM e nell'identificazione dei biomarcatori, la segmentazione automatica \ue8 un passo inevitabile per passare dall'analisi di piccoli set di dati in cui la segmentazione manuale \ue8 una soluzione fattibile, a set di dati pi\uf9 grandi e studi multicentrici, ottenendo misure pi\uf9 standardizzate e affidabili. Inoltre, in presenza di un metodo validato di segmentazione automatico sarebbe possibile effettuare diverse analisi quantitative promettenti in modalit\ue0 pi\uf9 agevole, da applicare anche nella comune pratica clinica. La segmentazione automatica del tessuto articolare e muscolo-scheletrico pu\uf2 essere applicata a nuove analisi statistiche di "big data", come l'analisi dei dati topologici o l\u2019utilizzo di pattern di deep learning, che aiuterebbe i medici a comprendere meglio la fisiopatologia e la fenotipizzazione della malattia.INTRODUCTION The main goal of the treatment of patients with Juvenile Idiopathic Arthritis (JIA) is to induce the clinical remission of the disease, which is essential to prevent the progression of joint damage and the consequent functional disability. Clinical remission status is assessed by the physician based on the patient's physical examination (synovitis) and inflammation indices (ESR or CRP). It is known that the clinical examination may not be sensitive for the identification of the presence of subclinical synovitis, that is, not detectable with the patient's physical examination [23]. MRI is considered the reference method for identifying the inflammatory process affecting the synovial membrane. In a recent study conducted at our institute in a cohort of 90 JIA patients in clinical remission, MRI demonstrated the presence of subclinical synovitis in 63% of patients. It was also shown that the persistence of the inflammatory process at the level of the synovial membrane was related to the relapse of the disease and to a progression of structural damage at the joint level. The qualitative and manual methods used to date for the reading of joint MRIs require a considerable time commitment and present an intrinsic subjectivity. To support and overcome the limitations due to qualitative and operator dependent evaluation, it would be useful to develop methods of automatic image segmentation capable of evaluating the presence of an inflammatory process at the joint level in an objective and reproducible way. OBJECTIVES Development of an automatic MR image segmentation method that detects the presence of subclinical synovitis in a cohort of patients with JIA in clinical remission. METHODS The project involved a review of the literature on the methods currently proposed for the automated analysis of joint MRI in patients with chronic inflammatory arthritis. For the analysis of medical images, the "Insight Toolkit" (ITK) libraries and the ITK-SNAP tool were used for automatic and semi-automatic segmentation, respectively. ITK is an open-source library widely used for image segmentation and recording software development. ITK-SNAP is also an open-source software application used to segment structures in 3D images and provides semi-automatic segmentation using "active-countour" methods. The purpose of using this tool is to extend a single-mode segmentation to a pipeline that combines user-driven multimodal preprocessing and the segmentation of level-set objects in which they are combined in a joint manner all information from multiple channels. The 3D-SPIR sequences obtained after administration of the contrast medium, in 15 patients affected by JIA with different degrees of severity of the disease were first evaluated with a semi-automatic segmentation and subsequently validated by a manual segmentation performed by a pediatric radiologist. The sequences themselves were processed using a fully automated method. First, an atlas-based segmentation pipeline was developed in order to record the sequence of images to be processed with appropriately selected reference images (atlases), the goal of this first segmentation was to identify the region of interest and define the markers; the latter constitute the input of the second segmentation based on the method of active contours of the new markers obtained from the previous segmentation. The automatic method was also compared with the results obtained from manual and semi-automatic segmentation and subsequently tested on a larger dataset of 30 patients with JIA in clinical remission. RESULTS MRIs (10 wrists and 5 coxo-femoral joints) obtained from 15 patients with JIA (M: 5.33%; F: 10.67%) were used to develop an automated segmentation method for identification and quantification of synovitis. A semi-automatic analysis approach was initially implemented which represents a good compromise between reliability and speed of identification of the area affected by the activity of the disease; this study made it possible to test two different algorithmic approaches on the same set of images and to identify the segmentation parameters that are closest to the manual segmentation performed by the radiologist pediatrician. The segmentation was judged to be satisfactory in 11 / 15 RM (73%) with the Threshold segmentation method, in 10 / 15 RM (67%) with the use of the Cluster method, both operated by the ITK-SNAP tool. The identification of the most significant parameters to perform the semi-automatic segmentation was essential to implement the tool to automate the joint segmentation process. The subjectivity of the operator, with the selection of the area of interest, of the markers points and with the adjustment of the segmentation parameters, greatly affects the quality of the processing performed. For this purpose, a completely automated method has been developed, therefore intrinsically more reproducible being explicitly performed by the computer. The tool for automatic identification of inflamed synovium was subjected to a preliminary validation on a court of 30 MRIs (20 wrists and 10 coxo-femoral joints) obtained from patients with JIA in clinical remission. Of these patients 5/30 (17%) were male and 10/30 (33%) were female. The median duration of illness at the time of study inclusion was 8.5 years; the median age of the patients at the baseline visit was 13.8 years. Of these 30 patients, the joints were worked out: 20/30 (67%) wrist and 10/30 (33%) coxo-femoral. Subclinical synovitis was identified in 15/30 (50%) of the patients examined. The concordance between the pediatric radiologist's reading and the semi-automatic method was evaluated to be 86.66% (Cohen K = 0.728). The agreement between the reading by the pediatrician radiologist and the automatic method was found to be 83.33% (Cohen K = 0.663). CONCLUSIONS The results of this study suggest that the fully automatic method for the detection of synovitis, based on the recording of "atlas-based" resonance images, is reliable for detecting the persistence of an inflammatory process at the joint level in patients with JIA in remission. clinic. In this study, the ITK platform was used for its 'open' features that make it an excellent research tool; the ITK platform provides a large number of registration / segmentation procedures adaptable to the characteristics of the data in question. Automatic assessment is significantly faster than manual and more objective (less operator dependent). Due to these characteristics, it appears to be a promising tool to be used in clinical trials aimed at evaluating the efficacy of new antirheumatic drugs in inducing remission of the disease. Further in-depth investigations and tests are needed in order to expand the database of registration images and extend the analysis to other joints such as the knee and ankle. Automatic segmentation of joint and musculoskeletal tissues is still a major challenge for medical image processing. With standardization in MR acquisition and biomarker identification, automatic segmentation is an inevitable step in moving from small dataset analysis where manual segmentation is a feasible solution, to larger datasets and multi-center studies, obtaining more standardized and reliable measurements. Moreover, having a validated method of automatic segmentation would allow more easily to carry out several promising quantitative analyses, also applicable in common clinical practice. Automatic segmentation of joint and musculoskeletal tissue can be applied to new "big data" statistical analyzes, such as topological data analysis or the use of deep learning patterns, which would help physicians better understand pathophysiology and the phenotyping of the disease

Stratification of patellofemoral pain using clinical, biomechanical and imaging features

Patellofemoral pain (PFP) is a common musculoskeletal complaint and the efficacy of current therapies aimed at PFP is limited. The aetiology of PFP is widely considered to be multifactorial and as a result the clinical presentation is often heterogeneous. In an attempt to address this issue, an international PFP consensus statement, published in 2013, highlighted the need to sub-group patients with PFP to enable more stratified interventions. A multi-methodological approach was used in this thesis. A systematic review of the existing imaging literature in PFP demonstrated that PFP is associated with a number of imaging features in particular MRI bisect offset and CT congruence angle and that some of these features should be modifiable with conservative treatment. A retrospective analysis investigating the overall 3D shape and 3D equivalents of commonly used PFJ imaging features demonstrated no differences between a group with and without PFP, challenging the current perceptions on the structural associations to PFP. A cross-sectional cluster analysis using modifiable clinical, biomechanical and imaging features identified four subgroups that are present in PFP cohort with a Weak group showing the worst prognosis at 12 months. Lastly, a pragmatic randomised controlled feasibility study comparing matched treatment to usual care management showed that matching treatment to a specific subgroup is feasible in terms of adherence, retention and conversion to consent. In summary, the findings of this thesis improves our understanding of the structural associations to PFP; the subgroups that exist within the PFP population; the natural prognosis of these PFP subgroups; and the feasibility of targeting treatment at PFP subgroups within a clinical trial

The role of subchondral bone in osteoarthritis

Osteoarthritis (OA) is the most common form of arthritis. Affected individuals commonly suffer with chronic pain, joint dysfunction, and reduced quality of life. OA also confers an immense burden on health services and economies. Current OA therapies are symptomatic and there are no therapies that modify structural progression. The lack of validated, responsive and reliable biomarkers represents a major barrier to the development of structure-modifying therapies. MRI provides tremendous insight into OA structural disease and has highlighted the importance of subchondral bone in OA. The hypothesis underlying this thesis is that novel quantitative imaging biomarkers of subchondral bone will provide valid measures for OA clinical trials. The Osteoarthritis Initiative (OAI) provided a large natural history database of knee OA to enable testing of the validity of these novel biomarkers. A systematic literature review identified independent associations between subchondral bone features with structural progression, pain and total knee replacement in peripheral joint OA. However very few papers examined the association of 3D bone shape with these patient-centred outcomes. A cross-sectional analysis of the OAI established a significant association between 3D bone area and conventional radiographic OA severity scores, establishing construct validity of 3D bone shape. A nested case-control analysis within the OAI determined that 3D bone shape was associated with the outcome of future total knee replacement, establishing predictive validity for 3D bone shape. A regression analysis within the OAI identified that 3D bone shape was associated with current knee symptoms but not incident symptoms, establishing evidence of concurrent but not predictive validity for new symptoms. In summary, 3D bone shape is an important biomarker of OA which has construct and predictive validity in knee OA. This thesis, along with parallel work on reliability and responsiveness provides evidence supporting its suitability for use in clinical trials