G.R. Vincent

Magnetic Resonance Imaging–Based Three‐Dimensional Bone Shape of the Knee Predicts Onset of Knee Osteoarthritis: Data From the Osteoarthritis Initiative

OBJECTIVE To examine whether magnetic resonance imaging (MRI)-based 3-dimensional (3-D) bone shape predicts the onset of radiographic knee osteoarthritis (OA). METHODS We conducted a case-control study using data from the Osteoarthritis Initiative by identifying knees that developed incident tibiofemoral radiographic knee OA (case knees) during followup, and matching them each to 2 random control knees. Using knee MRIs, we performed active appearance modeling of the femur, tibia, and patella and linear discriminant analysis to identify vectors that best classified knees with OA versus those without OA. Vectors were scaled such that -1 and +1 represented the mean non-OA and mean OA shapes, respectively. We examined the relation of 3-D bone shape to incident OA (new-onset Kellgren and Lawrence [K/L] grade ≥2) occurring 12 months later using conditional logistic regression. RESULTS A total of 178 case knees (incident OA) were matched to 353 control knees. The whole joint (i.e., tibia, femur, and patella) 3-D bone shape vector had the strongest magnitude of effect, with knees in the highest tertile having a 3.0 times higher likelihood of developing incident radiographic knee OA 12 months later compared with those in the lowest tertile (95% confidence interval [95% CI] 1.8-5.0, P < 0.0001). The associations were even stronger among knees that had completely normal radiographs before incidence (K/L grade of 0) (odds ratio 12.5 [95% CI 4.0-39.3]). Bone shape at baseline, often several years before incidence, predicted later OA. CONCLUSION MRI-based 3-D bone shape predicted the later onset of radiographic OA. Further study is warranted to determine whether such methods can detect treatment effects in trials and provide insight into the pathophysiology of OA development.

A novel method for bone area measurement provides new insights into osteoarthritis and its progression

BACKGROUND Modern image analysis enables the accurate quantification of knee osteoarthritis (OA) bone using MRI. We hypothesised that three-dimensional changes in bone would be characteristic of OA and provide a responsive measure of progression. METHODS 1312 participants with radiographic knee OA, and 885 non-OA controls with MRIs at baseline, 1, 2 and 4 years were selected from the NIH Osteoarthritis Initiative. Automated segmentation of all knee bones and calculation of bone area was performed using active appearance models. In a subset of 352 participants, responsiveness of bone area change was compared with change in radiographic joint space width (JSW) and MRI cartilage thickness over a 2-year period. RESULTS All OA knee compartments showed increased bone area over time compared with non-OA participants: for example, the 4-year percentage change from baseline in medial femur area for OA (95% CI) was 1.87(0.13), non-OA 0.43 (0.07); p<0.0001. Bone area change was more responsive than cartilage thickness or JSW; 2-year SRM for bone area in the medial femur was 0.83, for the most responsive cartilage thickness measure central medial femorotibial composite (cMFTC): 0.38, JSW: 0.35. Almost half of all knees had change greater than smallest detectable difference at 2 years. Body mass index, gender and alignment had only a small effect on the rate of change of bone area. CONCLUSIONS Changes in bone area discriminated people with OA from controls and was more responsive than the current and impending standards for assessing OA progression. The shape change in OA bone provides a new window on OA pathogenesis and a focus for clinical trials.

Measurement and visualisation of focal cartilage thickness change by MRI in a study of knee osteoarthritis using a novel image analysis tool

We describe the application of a novel analysis method that provides detailed maps of changes in cartilage thickness measured from MRI scans for individuals and cohorts of patients together with regional measures. A cohort of osteoarthritis patients was imaged using a 1.0 T MR scanner over a 36-month period. Hyaline cartilage was manually segmented from a three-dimensional (3D) spoiled gradient-echo sequence with fat suppression. Representative outlines of the bone surfaces of the distal femur and proximal tibia were automatically generated from T₂ weighted images using statistical models of the shape and appearance of the bones. Cartilage thickness was measured from a dense set of points representing the bony surface. The models of the bones provided a common frame of reference, relative to which change maps were generated and aggregated across the cohort and anatomically corresponding subregions of the joint to be identified. In the reproducibility arm involving six patients, the thickness of cartilage had coefficients of variation of 2.66% within the tibiofemoral joint and 2.94% within the medial femoral condyle region. In the 9 patients (6 female, 3 male) who completed the 36-month study, the most striking observation was that lack of change in global measures of cartilage thickness concealed substantial focal changes. Specifically, the cartilage thickness within the tibiofemoral joint decreased by 0.85% per annum (95% CI -2.13% to 0.45%) with the medial femoral condyle as the region with the most significant change, decreasing by 2.43% per annum (uncorrected 95% CI -4.31% to 0.51%).

Evaluation of segmentation methods on head and neck CT: Auto‐segmentation challenge 2015

Purpose Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. Methods In this work, we describe and present the results of the Head and Neck Auto‐Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. Results This paper presents the quantitative results of this challenge using multiple established error metrics and a well‐defined ranking system. The strengths and weaknesses of the different auto‐segmentation approaches are analyzed and discussed. Conclusions The Head and Neck Auto‐Segmentation Challenge 2015 was a good opportunity to assess the current state‐of‐the‐art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure‐specific segmentation algorithms.

A Method to Monitor Local Changes in MR Signal Intensity in Articular Cartilage: A Potential Marker for Cartilage Degeneration in Osteoarthritis

Osteoarthritis (OA) involves changes in the composition and ultimately the loss of cartilage from articulating joints. MRI has the ability to non-invasively probe the compositional integrity of cartilage, thereby potentially identifying dis- eased cartilage before loss occurs. In this study we have developed a technique to compare local changes in signal intensity over time in fat suppressed 3D gradient echo MR images of articular cartilage in patients with OA. We have used an Ac- tive Appearance Model (AAM) based image registration to correspond locations within the cartilage in the same individual at different times. We have applied the technique to data taken over periods of 1 and 3 years in two groups of patients with established OA of the knee. In both these studies, no significant change in total cartilage volume could be detected but we were able to observe some significant changes in signal intensity. We conclude that in a study of cartilage structure the technique can provide additional information without the overhead of extra scans.

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/m(2). 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.

MRI-based three-dimensional bone shape of the knee predicts onset of knee osteoarthritis: Data from the Osteoarthritis Initiative.

OBJECTIVE To examine whether magnetic resonance imaging (MRI)-based 3-dimensional (3-D) bone shape predicts the onset of radiographic knee osteoarthritis (OA). METHODS We conducted a case-control study using data from the Osteoarthritis Initiative by identifying knees that developed incident tibiofemoral radiographic knee OA (case knees) during followup, and matching them each to 2 random control knees. Using knee MRIs, we performed active appearance modeling of the femur, tibia, and patella and linear discriminant analysis to identify vectors that best classified knees with OA versus those without OA. Vectors were scaled such that -1 and +1 represented the mean non-OA and mean OA shapes, respectively. We examined the relation of 3-D bone shape to incident OA (new-onset Kellgren and Lawrence [K/L] grade ≥2) occurring 12 months later using conditional logistic regression. RESULTS A total of 178 case knees (incident OA) were matched to 353 control knees. The whole joint (i.e., tibia, femur, and patella) 3-D bone shape vector had the strongest magnitude of effect, with knees in the highest tertile having a 3.0 times higher likelihood of developing incident radiographic knee OA 12 months later compared with those in the lowest tertile (95% confidence interval [95% CI] 1.8-5.0, P < 0.0001). The associations were even stronger among knees that had completely normal radiographs before incidence (K/L grade of 0) (odds ratio 12.5 [95% CI 4.0-39.3]). Bone shape at baseline, often several years before incidence, predicted later OA. CONCLUSION MRI-based 3-D bone shape predicted the later onset of radiographic OA. Further study is warranted to determine whether such methods can detect treatment effects in trials and provide insight into the pathophysiology of OA development.

OP0164 Optimizing recruitment criteria for an osteoarthritis structure modification trial: data from the oai

Background The design of clinical trials for osteoarthritis is challenging; structural changes in tissues are quantitatively small and proceed very slowly. No clear guidance exists on how to optimise recruitment. KL grade is a poor recruitment criterion as centres interpret KL differently. Quantitative measures should be better, and metric radiographic joint space width (rJSW) is related to subsequent risk of radiographic progression. Although new MRI measures provide increased responsiveness in DMOAD trials, it is unknown whether selecting for recruitment based on radiographic criteria are well suited for responsiveness of these new measures. Objectives (1) To determine which baseline rJSW values are associated with most subsequent progression for rJSW, MRI cartilage and bone outcomes (2) Explore baseline covariates that influence progression rates (3) Estimate the trial numbers needed using the criteria determined by steps (1) and (2). Methods We used all knees from the Osteoarthritis Initiative which had all 3 measures recorded (rJSW – Duryea method; MRI cartilage thickness & bone area, Imorphics) at baseline, 1 and 2 years. We categorised knees into bins of 1mm rJSW, and assessed the 2 year changes of each bin, and characterised the distribution of rJSW in KL 0 knees. We used ANCOVA models to consider which covariates (including gender, height, weight, alignment, age, pain severity) affected 2-year slope of change, and responsiveness using SRM. For the final optimised recruitment groups, we calculated SRMs (CIs assessed using the bootstrap method of Efron) and derived the number of patients per arm in a putative trial. Results 4796 knees were included (2789 females, mean age 61.45). The lower 95th percentile values for rJSW in women and men were 3.9 and 4.5mm respectively. The mean changes at 2-years for all 3 outcomes were greatest for the categories of 2–3 and 3–4mm baseline rJSW (Figure 1A) with notably little change in knees with rJSW<2mm. Of the covariates, only pain improved responsiveness. Using a total WOMAC pain criterion; ≥3/20 reduced numbers from 726 knees to 331 knees, and improved 1 year SRM (95% CI) from 0.27 (0.17,0.34) to 0.41 (0.29,0.51) in rJSW, from 0.45 (0.37,0.51) to 0.55 (0.45,0.65) in MRI cartilage and from 0.60 (0.52,0.66) to 0.73 (0.62,0.83) in MRI bone. Figure 1B shows the relative SRMs for the 3 outcomes based on 2 inclusion criteria (rJSW 2–4mm and pain ≥3/20; n=331) and demonstrates the required trial numbers (with confidence intervals) based on the SRMs. Conclusions Selecting patients based on 2 simple criteria will improve responsiveness in clinical trials for all 3 imaging outcomes using standard imaging outcomes. Selecting for rJSW of 2–4mm is most important while adding a pain criteria further improves responsiveness; no other covariates improved this. Caution should be applied when using SRM to power a study because of the inherent difficulties in calculating standard deviations; Fig 1B shows for example that the confidence limits for rJSW at 12 months vary from 302 to 1489. This analysis also confirms the advantages of MRI outcomes over rJSW in terms of study size and duration: a 12-month study with cartilage thickness or bone area endpoints needs no more than 238 or 137 patients (including the upper 95th percentile confidence limit). Disclosure of Interest M. Bowes Employee of: Imorphics Ltd, G. Guillard Employee of: Imorphics Ltd, A. Brett Employee of: Imorphics Ltd, G. Vincent Employee of: Imorphics Ltd, P. Conaghan: None declared

Magnetic Resonance Imaging-Based Three-Dimensional Bone Shape of the Knee Predicts Onset of Knee Osteoarthritis: Data From the Osteoarthritis Initiative: 3-D Bone Shape Predicts Incident Knee OA

OBJECTIVE To examine whether magnetic resonance imaging (MRI)-based 3-dimensional (3-D) bone shape predicts the onset of radiographic knee osteoarthritis (OA). METHODS We conducted a case-control study using data from the Osteoarthritis Initiative by identifying knees that developed incident tibiofemoral radiographic knee OA (case knees) during followup, and matching them each to 2 random control knees. Using knee MRIs, we performed active appearance modeling of the femur, tibia, and patella and linear discriminant analysis to identify vectors that best classified knees with OA versus those without OA. Vectors were scaled such that -1 and +1 represented the mean non-OA and mean OA shapes, respectively. We examined the relation of 3-D bone shape to incident OA (new-onset Kellgren and Lawrence [K/L] grade ≥2) occurring 12 months later using conditional logistic regression. RESULTS A total of 178 case knees (incident OA) were matched to 353 control knees. The whole joint (i.e., tibia, femur, and patella) 3-D bone shape vector had the strongest magnitude of effect, with knees in the highest tertile having a 3.0 times higher likelihood of developing incident radiographic knee OA 12 months later compared with those in the lowest tertile (95% confidence interval [95% CI] 1.8-5.0, P < 0.0001). The associations were even stronger among knees that had completely normal radiographs before incidence (K/L grade of 0) (odds ratio 12.5 [95% CI 4.0-39.3]). Bone shape at baseline, often several years before incidence, predicted later OA. CONCLUSION MRI-based 3-D bone shape predicted the later onset of radiographic OA. Further study is warranted to determine whether such methods can detect treatment effects in trials and provide insight into the pathophysiology of OA development.

Longitudinal measurements of signal intensity as a potential marker for cartilage degeneration in osteoarthritis

In this study we have developed techniques to compare local changes in signal intensity over time in fat suppressed 3D gradient echo MR images of articular cartilage in patients with osteoarthritis. We have applied these techniques to data taken over a periods of 1 and 3 years in two groups of patients with established OA of the knee. In both these studies, no significant change in total cartilage volume could be detected but we were able to observe some significant changes in signal intensity. We conclude that in a study of cartilage structure this technique can provide additional information without the overhead of extra scans.