Defective WNT signaling may protect from articular cartilage deterioration - a quantitative MRI study on subjects with a heterozygous WNT1 mutation

Objective: WNT signaling is of key importance in chondrogenesis and defective WNT signaling may contribute to the pathogenesis of osteoarthritis and other cartilage diseases. Biochemical composition of articular cartilage in patients with aberrant WNT signaling has not been studied. Our objective was to assess the knee articular cartilage in WNT1 mutation-positive individuals using a 3.0T MRI unit to measure cartilage thickness, relaxation times, and texture features. Design: Cohort comprised mutation-positive (N = 13; age 17-76 years) and mutation-negative (N = 13; 16-77 years) subjects from two Finnish families with autosomal dominant WNT1 osteoporosis due to a heterozygous missense mutation c.652T>G (p.C218G) in WNT1. All subjects were imaged with a 3.0T MRI unit and assessed for cartilage thickness, T2 and T1 rho relaxation times, and T2 texture features contrast, dissimilarity and homogeneity of T2 relaxation time maps in six regions of interest (ROIs) in the tibiofemoral cartilage. Results: All three texture features showed opposing trends with age between the groups in the medial tibiofemoral cartilage (P = 0.020-0.085 for the difference of the regression coefficients), the mutation-positive individuals showing signs of cartilage preservation. No significant differences were observed in the lateral tibiofemoral cartilage. Cartilage thickness and means of T2 relaxation time did not differ between groups. Means of T1r relaxation time were significantly different in one ROI but the regression analysis displayed no differences. Conclusions: Our results show less age-related cartilage deterioration in the WNT1 mutation-positive than the mutation-negative subjects. This suggests, that the WNT1 mutation may alter cartilage turnover and even have a potential cartilage-preserving effect. (C) 2019 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.Peer reviewe

Efficacy of progressive aquatic resistance training for tibiofemoral cartilage in postmenopausal women with mild knee osteoarthritis : a randomised controlled trial

Objective: To study the efficacy of aquatic resistance training on biochemical composition of tibiofemoral cartilage in postmenopausal women with mild knee osteoarthritis (OA). Design: Eighty seven volunteer postmenopausal women, aged 60-68 years, with mild knee OA (Kellgren-Lawrence grades I/II and knee pain) were recruited and randomly assigned to an intervention (n = 43) and control (n = 44) group. The intervention group participated in 48 supervised aquatic resistance training sessions over 16 weeks while the control group maintained usual level of physical activity. The biochemical composition of the medial and lateral tibiofemoral cartilage was estimated using single-slice transverse relaxation time (T2) mapping and delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC index). Secondary outcomes were cardiorespiratory fitness, isometric knee extension and flexion force and knee injury and OA outcome (KOOS) questionnaire. Results: After 4-months aquatic training, there was a significant decrease in both T2 -1.2 ms (95% confidence interval (CI): -2.3 to -0.1, P = 0.021) and dGEMRIC index -23 ms (-43 to -3, P = 0.016) in the training group compared to controls in the full thickness posterior region of interest (ROI) of the medial femoral cartilage. Cardiorespiratory fitness significantly improved in the intervention group by 9.8% (P = 0.010). Conclusions: Our results suggest that, in postmenopausal women with mild knee OA, the integrity of the collagen-interstitial water environment (T2) of the tibiofemoral cartilage may be responsive to low shear and compressive forces during aquatic resistance training. More research is required to understand the exact nature of acute responses in dGEMRIC index to this type of loading. Further, aquatic resistance training improves cardiorespiratory fitness. (C) 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.Peer reviewe

Texture analysis of articular cartilage applied on magnetic resonance relaxation time maps:gray level co-occurrence matrices and local binary patterns

Abstract Analysis of an image texture (TA) is an efficient way to increase the information obtained from medical images. Mathematical computations are performed to evaluate and quantitatively characterize the structure and pathology of the target tissue. Resulting variables may provide complementary information regarding the subtle macromolecular changes occurring in various diseases, such as knee osteoarthritis (OA). OA is a common chronic disease that is characterized by disabling pain, joint dysfunction, and morphological alterations in multiple synovial joint structures. One of the structures affected in OA is the articular cartilage (AC) that provides frictionless movement and load-dampening properties for the joint articulation. Current diagnostic tools lack sensitivity for the early stage of OA. TA combined with quantitative magnetic resonance imaging techniques, such as T2 relaxation time mapping, allow sensitive evaluation of AC degeneration and disease onset, and may lead to more accurate diagnostics, individualized treatment planning, and better patient outcomes. The aim of this thesis was to assess two texture analysis methods, gray level co-occurrence matrix (GLCM) and local binary pattern (LBP), on the Oulu knee osteoarthritis (OKOA) dataset which comprises 80 confirmed OA patients and an equal number of healthy controls. The TA method’s ability to discern OA subjects from healthy subjects was evaluated on the whole dataset and on an early OA -stage simulating subset. Texture analyzed data included T2, T1ρ and T2ρ relaxation time maps and DESS images. Developed techniques were compared against the current mean relaxation time analysis scheme. Furthermore, the parametric outcomes of TA were subjected to machine learning classification algorithms and tested for automatic segregation of the study groups. TA demonstrated excellent performance in discerning the study groups and appears to be more sensitive to the early changes than the current mean relaxation time focused analysis methods. TA can be applied on various quantitative contrasts and resulting outcomes can be utilized in automated classification pipelines for OA detection. TA demonstrates great potential for further research evaluations and clinical applications and these findings warrant further inquiries into the topic.Tiivistelmä Kuvan tekstuurin tutkiminen mahdollistaa lääketieteellisten kuvien rakenteissa piilevän strukturaalisen informaation esittämisen kvantitatiivisessa muodossa. Tekstuurianalyysimenetelmät tuovat erilaisten sairauksien, kuten polven nivelrikon, aiheuttamien kudosmuutosten seuraukset esiin laskennallisin keinoin. Polven nivelrikko on yleinen, elämänlaatua laajasti heikentävä sairaus. Nivelrikossa polven ruston makromolekyyliympäristö muuttuu ja kudosta koossa pitävät tukirakenteet heikkenevät. Normaalisti nivelrusto vastaa nivelen lähes kitkattomasta liikkeestä ja toimii merkittävänä iskuja vaimentavana tukityynynä. Ruston vaurioituminen aiheuttaakin usein huomattavia kipuja sekä nivelen toiminnan ja yleisen toimintakyvyn merkittävää heikkenemistä. Nivelruston uusiutumiskyky on erittäin heikko, ja alkavat muutokset tulisi näin ollen pyrkiä tunnistamaan mahdollisimman varhaisessa vaiheessa. Nykyisin nivelrikkodiagnostiikassa käytettävät menetelmät eivät ole erityisen herkkiä alkavan vaurion tunnistamisessa. Tekstuurianalyysimenetelmät yhdistettyinä ruston rakenneosasten koostumusta mittaaviin kvantitatiivisiin kuvantamismenetelmiin, kuten T2-relaksaatioaikakartoitukseen, mahdollistavat uudenlaisen työkalun kehittämisen. Työkalulla voidaan parantaa diagnostiikan tarkkuutta, potilaan ennustetta ja hoitomenetelmiä. Tässä väitöskirjassa tutkitaan kahden erilaisen tekstuurianalyysimenetelmän soveltuvuutta nivelrikkodiagnostiikkaan. Käytetyt menetelmät ovat gray level co-occurrence matrix, joka perustuu harmaasävyjen yhteisesiintyvyyksiin kuvassa, ja local binary pattern, jossa tutkitaan kynnysarvoistetun kuvan rakenteiden suuntautumista. Menetelmien herkkyyttä erottaa nivelrikkopotilaat terveistä verrokeista tutkitaan testiaineistolla ja verrataan nykyisin käytössä oleviin menetelmiin. Analyysit suoritettiin T2-, T1ρ- ja T2ρ-relaksaatioaikakartoille sekä DESS-kuville. Tekstuurianalyysin tuottamaa parametrimuotoista dataa käytetään lisäksi koneoppimispohjaisten luokittelualgoritmien kehittämisessä. Algoritmien tavoitteena on erottaa tutkittavat ryhmät automaattisesti toisistaan. Tekstuurianalyysimenetelmät osoittautuivat tässä väitöskirjatutkimuksessa erittäin herkiksi työkaluiksi nivelrikon aiheuttamien muutosten tunnistamisessa. Myös alkavan vaiheen nivelrikon tunnistaminen onnistui luotettavasti. Tekstuurianalyysia voidaan soveltaa monenlaisiin lähdekuviin, ja tuotettu data soveltuu automaattisten luokitinalgoritmien kehittämiseen

Assessment of meniscus with adiabatic T1ρ and T2ρ relaxation time in asymptomatic subjects and patients with mild osteoarthritis:a feasibility study

Abstract Objective: To investigate the ability of magnetic resonance imaging (MRI) adiabatic relaxation times in the rotating frame (adiabatic T1ρ and T2ρ) to detect structural alterations in meniscus tissue of mild OA patients and asymptomatic volunteers. Method: MR images of 24 subjects (age range: 50–67 years, 12 male), including 12 patients with mild osteoarthritis (OA) (Kellgren–Lawrence (KL) = 1, 2) and 12 asymptomatic volunteers, were acquired using a 3 T clinical MRI system. Morphological assessment was performed using semiquantitative MRI OA Knee Score (MOAKS). Adiabatic T1ρ and T2ρ (AdT1ρ, AdT2ρ) relaxation time maps were calculated in regions of interest (ROIs) containing medial and lateral horns of menisci. The median relaxation time values of the ROIs were compared between subjects classified based on radiographic findings and MOAKS evaluations. Results: MOAKS assessment of patients and volunteers indicated the presence of meniscal and cartilage lesions in both groups. For the combined cohort group, prolonged AdT1ρ was observed in the posterior horn of the medial meniscus (PHMED) in subjects with MOAKS meniscal tear (P < 0.05). AdT2ρ was statistically significantly longer in PHMED of subjects with MOAKS full-thickness cartilage loss (P < 0.05). After adjusting for multiple comparisons, differences in medians of observed AdT1ρ and AdT2ρ values between mild OA patients and asymptomatic volunteers did not reach statistical significance. Conclusions: AdT1ρ and AdT2ρ measurements have the potential to identify changes in structural composition of meniscus tissue associated with meniscal tear and cartilage loss in a cohort group of mild OA patients and asymptomatic volunteers

Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS:6-year data from osteoarthritis initiative

Abstract In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA