INTRODUCTION Cartilage T2 is commonly measured by multi-echo spin-echo (MESE) MRI. MESE, however, requires long acquisition times to obtain sufficient in-plane resolution for laminar T2 analysis and does not fully cover the deep cartilage lamina [1]. Quantitative DESS (qDESS) retains both acquired echoes so that both cartilage morphology and cartilage T2 can be extracted simultaneously from a single acquisition with relatively short acquisition time [2, 3]. The qDESS thus reduces patient burden and analysis time. The MechSens trial [4] investigated the impact of unilateral anterior cruciate ligament (ACL) injury on femorotibial (FTJ) cartilage 2–10 years after injury and is the first clinical study to use qDESS MRI. Based on manual segmentations, deep layer FTJ T2 was longer in ACL than in contra-lateral (CL) non-ACL and in healthy control knees, whereas no differences in superficial layer T2 or cartilage thickness were observed. OBJECTIVE To technically validate an image analysis technique based on convolutional neural networks (CNN) for automated laminar cartilage T2 analysis for qDESS vs. manual segmentations, and to test whether between-knee and -group differences in deep cartilage T2 can be replicated in ACL-injured vs. control knees. METHODS Of 85 participants from two age groups (20–30y & 40–60y) 37 had a unilateral ACL-injury (2–10y prior to baseline: ACL20-30: n=23, ACL40-60, n=14). 48 healthy controls had no history of knee injury (HEA20-30, n=24, HEA40-60, n=24). Coronal qDESS MRIs were acquired using a 3T Siemens Prisma in both knees (resolution: 0.31mm x 0.31mm x 1.5mm, repetition time: 17ms, echo times: 4.85/12.15ms, flip angle: 15°). Manual segmentation of weight-bearing FTJ cartilages was performed with expert quality control. Automated cartilage segmentation was based on a 2D U-Net image analysis workflow. Two U-Nets were trained on both knees of odd- or even-numbered participants and were then employed to segment the knees from the other participants (even- or odd-numbered), respectively. T2 was computed for the FTJ cartilages as previously described [2]. Deep and superficial layer T2 were computed based on the position of the voxels relative to the subchondral bone and cartilage surface and were averaged across the FTJ. The segmentation agreement was evaluated using the Dice similarity coefficient (DSC). T2 was compared between segmentations using Bland & Altman plots and correlation analysis. FTJ T2 of the ACL knees was compared to T2 of uninjured CL and healthy control knees using Conover-Iman and Dunn post-hoc tests, respectively. Paired (between-knee) or unpaired (between-group) Cohen's D was used as measure of effect size of T2 differences. RESULTS The agreement of automated vs. manual cartilage segmentation across the four FTJ cartilages was high, with DSCs between 0.90±0.05 (central medial femur) and 0.93±0.02 (lateral tibia). Both deep and superficial layer T2 correlated strongly between techniques (r≥0.90, Table 1). Bland Altman plots indicated that the automated segmentation tended to underestimate deep T2 and to overestimate superficial T2 (Fig. 1, Table 1). In the ACL20-30 group, deep FTJ T2 was longer in ACL than in uninjured CL knees for both manual (D=-1.24) and automated (D=-1.30) segmentations. In the ACL40-60 group, deep FTJ T2 was longer in ACL-injured vs. CL knees for automated (D=-1.06) but not for manual segmentation (D=-0.67, Fig. 2). Comparing ACL-injured knees with those from healthy controls, deep FTJ T2 was longer in ACL20-30 knees than in the left (but not the right) knees of the HEA20-30 group for both manual (D left/right=-1.22/-1.15) and automated segmentations (D left/right=-1.05/-1.01, Fig. 2). Deep FTJ T2, in contrast, was longer for ACL40-60 than for left and right HEA40-60 knees with manual (D left/right=-1.00/-1.04), but not with automated segmentations (D left/right=-0.94/-0.99, Fig. 2). Superficial FTJ T2 did not differ between ACL-injured vs. CL knees or healthy knees using either segmentation method (Fig. 2). Results for comparisons across age groups are shown in Fig. 2. CONCLUSION CNN-based fully automated cartilage T2 analysis provided a very high agreement with T2 derived from manual segmentations. Importantly, it was also sensitive to ACL-injury-related prolongation in deep cartilage T2. A deep-learning-based image analysis workflow trained from high quality segmentations may thus allow to replace manual segmentations in future studies relying on qDESS MRI for cartilage T2 analyses.

Objective To determine the association between joint structure and gait in patients with knee osteoarthritis (OA). Methods IMI-APPROACH recruited 297 clinical knee OA patients. Gait data was collected (GaitSmart®) and OA-related joint measures determined from knee radiographs (KIDA) and MRIs (qMRI/MOAKS). Patients were divided into those with/without radiographic OA (ROA). Principal component analyses (PCA) were performed on gait parameters; linear regression models were used to evaluate whether image-based structural and demographic parameters were associated with gait principal components. Results Two hundred seventy-one patients (age median 68.0, BMI 27.0, 77% female) could be analyzed; 149 (55%) had ROA. PCA identified two components: upper leg (primarily walking speed, stride duration, hip range of motion [ROM], thigh ROM) and lower leg (calf ROM, knee ROM in swing and stance phases). Increased age, BMI, and radiographic subchondral bone density (sclerosis), decreased radiographic varus angle deviation, and female sex were statistically significantly associated with worse lower leg gait (i.e. reduced ROM) in patients without ROA ( R 2 = 0.24); in ROA patients, increased BMI, radiographic osteophytes, MRI meniscal extrusion and female sex showed significantly worse lower leg gait ( R 2 = 0.18). Higher BMI was significantly associated with reduced upper leg function for non-ROA patients ( R 2 = 0.05); ROA patients with male sex, higher BMI and less MRI synovitis showed significantly worse upper leg gait ( R 2 = 0.12). Conclusion Structural OA pathology was significantly associated with gait in patients with clinical knee OA, though BMI may be more important. While associations were not strong, these results provide a significant association between OA symptoms (gait) and joint structure.

INTRODUCTION Assessing the structure and properties of articular tissues using MRI-based approaches is highly relevant to OA studies, as MRI enables direct visualization of all joint structures. These can be evaluated using semi-quantitative (sq) or quantitative (q) morphometric methods. Insights into the biochemical composition of specific tissues can be obtained with MRI T2 relaxometry. A crucial basis for such OA analysis is the choice of a suitable, and time-efficient MRI acquisition protocol that assures high image quality while lowering patient burden and costs through short scan time. Moreover, standardization of MRI protocols and analysis techniques across studies is helpful to ensure comparability between studies. OBJECTIVE To propose - as an expert opinion - a state-of-the-art MRI acquisition protocol for clinical trials on both early and advanced stages of knee OA. This protocol is designed to support a multitude of semi-quantitative and quantitative image assessments (including synovitis), relevant to the study and management of knee OA, and ideally suitable for automated analysis. METHODS A PubMed literature search of articles published in the last 20 years was performed (focus on the past 5 years) and several OA imaging experts provided input. Specific MRI sequences (including orientations, spatial resolutions, and parameters) were identified that support the above purpose. The implementation of the protocol had to be feasible on standard clinical MRI scanners, with a net acquisition time of <30 minutes. RESULTS The proposed protocol is shown in Tables 1 & 2, and example images in Figure 1. MRIs should be obtained at ≥1.5T, ideally without hardware (or major software) changes during longitudinal studies. Localizer images should be used to spatially align the sequences with the knee anatomy and position. We recommend clinical 2D proton density (PD) turbo spin echo sequences (TSE) with fat suppression (FS) in two planes, and a coronal T1-weighted TSE (without FS) to support sq assessment of all articular tissues and pathologies, and q assessment of Hoffa and effusion synovitis. A high-resolution 3D quantitative double echo steady state (qDESS) sequence [1] is proposed (coronal, or sagittal, or sagittal near-isotropic) for quantitative cartilage morphometry and T2, for bone (shape) and for q meniscus analysis. Inversion recovery spin echo (FLAIR [2]) is included for potential non-contrast-enhanced depiction of synovitis. All images should be checked for quality and protocol adherence as soon as possible (best immediately) after image acquisition. Acquiring repeated scans (re-test) in a few patients per site at baseline and follow-up can provide information on study-specific test-retest errors and the smallest detectable change (SDC). CONCLUSION Here, we propose a state-of-the-art image acquisition protocol for trials on early or advanced knee OA. While assuring technical feasibility in clinical research, a balance between image acquisition efficiency (time), safety, and technical/methodological diversity is proposed. Importantly, the suggested approach offers potential for scientific innovation in the (automated) analysis of tissue structures, composition and pathology in clinical trials on disease modification of knee OA.

INTRODUCTION Most participants of the IMI-APPROACH knee OA cohort displayed cartilage damage, based on quantitative (segmentation-based) MRI morphometry and MOAKS scoring, predominantly in the medial tibiofemoral compartment. Cartilage surface mapping (CaSM) is a quantitative 3D analytic method that, unconstrained by subregional boundaries, can demonstrate visually how cartilage thickness varies across a joint. OBJECTIVE The purpose of this cross-sectional study was to evaluate cartilage thickness distribution in knee OA patients using CaSM, and to analyze how it varies amongst demographic, radiographic, and MRI structural pathology strata. METHODS The cohort included 297 participants with clinical knee OA. 1.5T or 3T MRI 3D gradient echo sequences were acquired at baseline and follow-up, with only baseline being used in the current analysis. Semi-automatic segmentation of the femoral and tibial cartilage was performed using Stradview. Segmentations were registered to canonical surfaces using wxRegSurf and analyzed in MATLAB. The relationship between demographic and structural pathology strata on the cartilage thickness distribution was analyzed using statistical parametric mapping (SPM). SPM allows for vertex-wise comparisons and delivers multiple-comparison-corrected F-test statistics, using the Surfstat MATLAB package. p<0.05 was used as the threshold for statistical significance. Sex, age, and BMI were examined (demographic factors). Presence of radiographic OA (ROA; KLG≥2) and degree of medial/lateral JSN were studied as radiographic factors, and MOAKS BMLs and meniscal extrusion (scored on intermediate-weighted fat-suppressed sequences) as MRI structural pathology features, in individual models. Analysis differentiating patients with and without ROA was performed in addition. RESULTS 287 patients could be analyzed (age 66.4±7.1, BMI 28.0±5.2, 78% female, 55% ROA). Male patients had significantly thicker cartilage across the entire joint, especially the tibiae and trochlea (Figure 1). Age showed a pronounced effect in the trochlea and (central) medial and lateral tibia, with older patients having thinner cartilage (independent of radiographic status); BMI was not significantly associated with cartilage thickness in any region (Figure 1). Patients with ROA showed significantly thinner cartilage in the tibiae and medial femur than those without ROA, but thicker cartilage in the trochlea and lateral femur (Figure 1). Patients with JSN showed opposite effects depending on direction: those with medial JSN displayed significantly thinner cartilage in the medial, and those with lateral JSN in the lateral compartment (Figure 2). Meniscal extrusion results were very similar to JSN results (Figure 2). Presence of BMLs in any subregion of a compartment (lateral/medial FT and PF) was associated with thinner cartilage throughout that entire compartment (Figure 3). While the difference between male and female patients was significant for non-ROA and ROA patients separately, cartilage distribution variations based on structural pathology (JSN, BMLs, meniscal extrusion) were only visible (and significant) for patients with ROA. CONCLUSION Both demographic factors (age and sex) and radiographic/MRI structural pathology features (presence of ROA, JSN, BMLs and meniscal extrusion) are significantly associated with variation in cartilage thickness distribution throughout the joint in patients with clinical knee OA, although for structural pathology only in patients with ROA. Future analyses will indicate whether these and other factors are associated with longitudinal cartilage thickness changes.

INTRODUCTION Manual cartilage segmentation from MRI is a labor-intensive process. This is particularly cumbersome in studies in which cartilage morphology is to be determined from manual segmentation of fat-suppressed, high-resolution gradient echo (GrE) sequences, and then T2 from another manual segmentation of a multi echo spin echo (MESE) sequence. To this end, we have developed a registration algorithm that uses segmentations of the cartilage from the GrE sequences, and rigidly registers these to an optimal position for extracting cartilage T2 signal from the MESE [1,2]. However, we have recently started to develope fully automated analysis technology for T2 directly from the MESE using convolutional neural network (CNN) architectures and deep learning (DL) [3]. OBJECTIVE To compare i) T2 determined from MESE by registration of manually segmented cartilage masks from GrE and ii) T2 determined from MESE directly by fully automated segmentation using CNNs [3] vs. manually segmentation for T2 analysis in the same knees. METHODS We studied 39 ACL patients and 15 healthy controls, enrolled at Charité (n=54; Berlin, Germany). Sagittal 3D VIBEwe MRIs were acquired for cartilage morphometry, and sagittal 2D multi-echo spin-echo (MESE) MRIs for cartilage T2 analysis using a 1.5T Siemens Avanto MRI, at baseline and (n=53) at 1 year follow-up. Segmentation of the femorotibial cartilages was performed manually by expert readers from the 3D VIBE and 2D MESE. A multimodal approach was used to register cartilage segmentations from the VIBE to the MESE [1,2]. Automated cartilage segmentation of the MESE relied on a 2D U-Net [3] that was trained on all 7 echoes from athletes and PCL patients (training/validation set n=50/9), the images being acquired on the same scanner and segmented by the same readers. Agreement between registered and automated vs. manual cartilage segmentation was assessed using dice similarity coefficients (DSCs). Superficial and deep femorotibial cartilage T2 (each 50% thickness) were extracted from the segmentations. Baseline cartilage T2 and 1-year change were compared between methods, using Pearson correlation coefficients, mean differences, and 95% CIs. RESULTS In the deep cartilage layer, baseline T2 derived from automated (CNN) segmentation was very similar to that of the manual expert segmentation on the same images, with mean differences of 0.1ms, and correlations of r=0.97-98 across compartments (Table 1). Deep T2 values obtained from registration were longer than those from manual segmentations, with correlations of 0.12-0.13. Superficial T2 (Table 1) was approx. 6-7ms longer than that in the deep layer across all methods. The CNN method overestimated T2 by about 1.2ms (r=0.91-93), and the registration method by about 8ms (r<0.13). The longitudinal results confirmed superiority of the direct CNN segmentation (Table 2). CONCLUSION Direct automated segmentation of MESE using CNN-based segmentation [3] yields highly accurate results of T2, a measure of cartilage composition, in deep and superficial cartilage laminae. This here applied cross-sectionally and longitudinally at 1.5T, with similar results obtained at 3T [4]. The alternative of extracting T2 via registration [1] of cartilage morphology masks from GrE displayed less accurate results. Although the registration algorithm was previously shown relatively accurate [1], and predictive of progression in the OAI FNIH sample [2], these results were obtained at 3T. Further, a “peel factor” was introduced to limit GrE mask in the depth in order to fit the MESE segmentations. A uniform peel factor of 30% was used, although variation was noted between cartilage locations and participants. The same factor (30%) was used here, without optimization to the VIBEwe sequence and 1.5T. Yet, in future studies we recommend direct extraction of T2 using CNNs rather than trying to further optimize registration-based techniques as a proxy of cartilage composition.

Purpose: KLG 0 knees with contralateral (CL) radiographic joint space narrowing (JSN) have been reported to be at an elevated risk of developing radiographic OA (ROA). Based on a matched case-control study design of KLG 0 knees with CL radiographic joint space narrowing (JSN), we have previously observed elevated femorotibial superficial layer cartilage T2 in n=39 KLG0 knees with CL-JSN (cases) when compared to n=39 matched (1:1) KLG 0 knees with CL KLG 0 (controls) using manual cartilage segmentations from multi-echo spin-echo (MESE) MRI provided by the Osteoarthritis Initiative (OAI).

Purpose: Prolonged cytokine response after knee injuries may play a role in the development of osteoarthritis. We hypothesize that the cartilage thickness is associated with the release of proteins from cartilage and bone and/or the presence of cytokines in the joint in the years following a traumatic knee injury. The aim of our study was to compare knee cartilage thickness, derived from magnetic resonance images (MRI), with concentrations of biomarkers in body fluids 2 and 5 years after an anterior cruciate ligament (ACL) injury.

Objective: To evaluate the efficacy and safety of the anti-catabolic ADAMTS-5 inhibitor S201086/ GLPG1972 for the treatment of symptomatic knee osteoarthritis. Design: ROCCELLA (NCT03595618) was a randomized, double-blind, placebo-controlled, dose-ranging, phase 2 trial in adults (aged 40-75 years) with knee osteoarthritis. Participants had moderate-to-severe pain in the target knee, Kellgren-Lawrence grade 2 or 3 and Osteoarthritis Research Society International joint space narrowing (grade 1 or 2). Participants were randomized 1:1:1:1 to once-daily oral S201086/GLPG1972 75,150 or 300 mg, or placebo for 52 weeks. The primary endpoint was change from baseline to week 52 in central medial femorotibial compartment (cMFTC) cartilage thickness assessed quantitatively by magnetic resonance imaging. Secondary endpoints included change from baseline to week 52 in radiographic joint space width, Western Ontario and McMaster Universities Osteoarthritis Index total and subscores, and pain (visual analogue scale). Treatment-emergent adverse events (TEAEs) were also recorded. Results: Overall, 932 participants were enrolled. No significant differences in cMFTC cartilage loss were observed between placebo and S201086/GLPG1972 therapeutic groups: placebo vs 75 mg, P = 0.165; vs 150 mg, P = 0.939; vs 300 mg, P = 0.682. No significant differences in any of the secondary endpoints were observed between placebo and treatment groups. Similar proportions of participants across treatment groups experienced TEAEs. Conclusions: Despite enrolment of participants who experienced substantial cartilage loss over 52 weeks, during the same time period, S201086/GLPG1972 did not significantly reduce rates of cartilage loss or modify symptoms in adults with symptomatic knee osteoarthritis. & COPY; 2023 The Authors. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

MRI-detected cartilage damage is associated with elevated cartilage T2 relaxation times on MRI. Further, cartilage damage is more frequently observed in knees at higher risk of developing radiographic knee osteoarthritis (OA). Detection of such early changes by means of MRI may be a useful method to identify participants for future observational or interventional OA studies. The aim of this study was to compare a U-Net-based, fully automated cartilage segmentation pipeline vs. manual, quality-controlled cartilage segmentations for determining differences in laminar cartilage T2 in Kellgren-Lawrence Grade (KLG) 0 knees with and without MRI Osteoarthritis Knee Score (MOAKS) cartilage damage. The fully automated U-Net-based image analysis pipeline was trained using sagittal multi-echo spin-echo (MESE) MRIs from the Osteoarthritis Initiative (OAI), for which manual quality-controlled segmentations of the respective structures were available. U-Nets for medial (MFTC) and lateral (LFTC) femorotibial cartilage segmentation were trained on all seven echoes of the MESE MRIs of 92 OAI healthy reference cohort (HRC) participants. A third U-Net was trained on bone segmentations of 60 OAI HRC knees. The bone segmentation served to algorithmically determine the weight-bearing femoral region of interest and to select the MRI slices that required cartilage segmentation. MFTC and LFTC U-Nets were then applied to the selected MRI slices of 123 KLG0 knees of the OAI incidence cohort (small sample) with, and 618 KLG0 knees of the same cohort without manual cartilage segmentations available (total of 741 knees = full sample). Automated post-processing was used to identify and correct implausible segmentations. Differences in superficial and deep layer cartilage T2 were compared between knees with vs. without MOAKS cartilage damage in the small sample (both U-Net and manual segmentations) and full sample (U-Net segmentation only). Mean differences were considered statistically significant when the 95% confidence interval did not include 0. MOAKS readings were performed by a very experienced MSK radiologist. Cartilage damage was defined as MOAKS cartilage score >0 in any of the medial or lateral tibial MOAKS subregions, or in the central medial or lateral femoral MOAKS subregions. Effect sizes were expressed using Cohen's D (d). The full sample comprised knees of 415 women and 326 men (age [mean ±SD]: 60±9 years, BMI: 27±4 kg/m²). Of these, 408 had no cartilage damage, 115 only medial damage, 149 only lateral damage and 69 had medial and lateral damage. Mean cartilage T2 values for the medial and lateral femorotibial compartments, calculated from the manual and automated segmentations in the small sample, as well as from the automated segmentations in the full sample are shown in Figure 1. In the MFTC, superficial layer T2 was longer in knees with cartilage damage than in those without for manual and automated segmentations of the small sample (d [95% CI] = 0.63 [0.26, 0.99]; d=0.58 [0.22, 0.95]), and also for automated segmentations of the full sample (d = 0.70 [0.53, 0.87]). Deep layer T2 did not differ between knees with vs. without cartilage damage for the automated cartilage segmentation of the large sample and the automated and manual cartilage segmentations of the small sample (d=0.04 to 0.09). Manual cartilage segmentations of the LFTC yielded greater T2 for both the deep (d = 0.48 [0.12, 0.85]) and superficial layers (d = 0.66 [0.29, 1.03]) in knees with vs. without cartilage damage. Results for U-Net segmentations were d=0.59 [0.22, 0.95] (small sample) and d=0.43 [0.27, 0.59] (full sample) for the deep layer and d=0.66 [0.29, 1.02] (small sample) and d=0.59 [0.43, 0.75] (full sample) for the superficial layer, respectively. The fully automated U-Net-based cartilage segmentation pipeline was at least as sensitive to differences in laminar cartilage T2 between KLG 0 knees with vs. without MOAKS cartilage damage as quality-controlled, manual cartilage segmentations. Hence, this approach shows great potential for laminar cartilage T2 analyses and for application to large samples. This work was funded as part of the OA-BIO Eurostars-2 project (E! 114932). FE, WW, SM and AW are part-time employees of Chondrometrics GmbH; FE, WW, and SM are co-owners of Chondrometrics GmbH. FE has provided consulting services to Merck KGaA, Kolon-TissueGene, and Novartis. FB is co-owner of 4Moving Biotech. F.W.R. and A.G. are shareholders of BICL, LLC. A.G. is consultant to Pfizer, Kolon TissueGene, Novartis, AstraZeneca, Coval, Medipost and ICM. GND has no conflicts of interest to declare. CORRESPONDENCE ADDRESS: [email protected]

Purpose: Cartilage damage has been reported to be associated with elevated cartilage T2 relaxation times and is frequently observed in the knees of persons at higher risk for knee OA even when radiographic signs of disease are absent. The aim of this study was to evaluate a U-Net-based, automated cartilage segmentation pipeline vs. manual, quality-controlled cartilage segmentations for determining laminar cartilage T2. This work was funded as part of the OA-BIO Eurostars-2 project (E! 114932).

Purpose: While mechanical stress is known to influence the regulation of musculoskeletal tissue metabolism, the effects of immobilization on articular cartilage health in healthy individuals are not well understood. Magnetic resonance imaging (MRI) T2 relaxation times (T2Me) of articular cartilage are qualitative measures of cartilage quality where longer T2Me corresponds to poorer tissue quality. This study aims to investigate the effects of serial 21-days of 6°head-down-tilt bed rest (HDT-BR) periods combined with nutrition and exercise countermeasures on T2Me of the tibiofemoral articular cartilage in healthy male individuals.

Purpose: Automated, U-Net-based cartilage segmentations have been reported to provide a high sensitivity to cartilage thickness loss. However, a persistent concern regarding automated segmentations is the imperfect agreement between automated and manually performed segmentations, particularly in knees with established radiographic osteoarthritis (OA). Incorrect automated segmentations may impair the sensitivity to change or to differences in change between groups. Currently, expert-based manual segmentation is therefore still considered to be the “gold standard”. The purpose of this study was to evaluate the performance of automated, U-Net-based cartilage segmentations of MRIs with and without additional manual quality control and correction (QC&C) and the time effort required for QC&C. To this end, automated segmentation with and without QC&C was compared with the manual “gold standard” cross-sectionally as well as longitudinally, with change measured over 24 months.

Purpose: The IMI-APPROACH consortium is a longitudinal cohort study to combine conventional and new disease markers and to identify different OA phenotypes. It employed a multistep approach for the selection of participants, to include people with an increased likelihood of structural and/or pain progression. Study parameters used to assess structural progression include quantitative MRI of cartilage including thickness and semi-quantitative MRI scoring of cartilaginous and non-cartilaginous features of OA.

Objective: To investigate the test-retest precision and to report the longitudinal change in cartilage thickness, the percentage of knees with progression and the predictive value of the machine-learning-estimated structural progression score (s-score) for cartilage thickness loss in the IMI-APPROACH cohort - an exploratory, 5-center, 2-year prospective follow-up cohort. Design: Quantitative cartilage morphology at baseline and at least one follow-up visit was available for 270 of the 297 IMI-APPROACH participants (78% females, age: 66.4 +/- 7.1 years, body mass index (BMI): 28.1 +/- 5.3 kg/m(2), 55% with radiographic knee osteoarthritis (OA)) from 1.5T or 3T MRI. Test-retest precision (root mean square coefficient of variation) was assessed from 34 participants. To define progressor knees, smallest detectable change (SDC) thresholds were computed from 11 participants with longitudinal test-retest scans. Binary logistic regression was used to evaluate the odds of progression in femorotibial cartilage thickness (threshold: similar to 211 mu m) for the quartile with the highest vs the quartile with the lowest s-scores. Results: The test-retest precision was 69 mu m for the entire femorotibial joint. Over 24 months, mean cartilage thickness loss in the entire femorotibial joint reached -174 mu m (95% CI: [-207, -141] mu m, 32.7% with progression). The s-score was not associated with 24-month progression rates by MRI (OR: 1.30, 95% CI: [0.52, 3.28]). Conclusion: IMI-APPROACH successfully enrolled participants with substantial cartilage thickness loss, although the machine-learning-estimated s-score was not observed to be predictive of cartilage thickness loss. IMI-APPROACH data will be used in subsequent analyses to evaluate the impact of clinical, imaging, biomechanical and biochemical biomarkers on cartilage thickness loss and to refine the machine-learning-based s-score. (c) 2022 The Author(s). Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Purpose: APPROACH (Applied public-private research enabling osteoarthritis clinical headway) is a European, 5-centre, 2-year prospective follow-up cohort project that collected clinical, imaging, biomechanical, and biochemical parameters. APPROACH was designed to identify outcome measures that can characterize different OA phenotypes that may benefit most from personalized therapies. Patients with a high likelihood of joint space width loss and/or increased or sustained knee pain over the course of the study were included based on rankings that were produced by machine learning models.

Purpose: The concentration of articular cartilage blood markers and their load-induced changes serve as surrogate parameters for investigating the response of articular cartilage to mechanical stimuli and may be useful for detecting early changes in cartilage homeostasis. Cartilage oligomeric matrix protein (COMP) is a cartilage constituent that is involved in collagen fibril organization and serum (s) COMP concentration is believed to be a marker of articular cartilage degradation in osteoarthritis (OA).

Articular cartilage transverse relaxation time (T2) on MRI has been observed to reflect collagen integrity, orientation, and hydration, and to be associated with cartilage histological grading, mechanical properties, and early knee OA status. Previously, we reported high agreement of cartilage segmentations obtained from multi echo spin echo (MESE) MRI, using fully automated deep learning methods, in comparison with manual ones. As these results were based on 3T MRI (of the OAI), we here explored the agreement between fully automated segmentation of MESE cartilage at 1.5T. To analyze the agreement between automated, U-Net-based segmentation of femorotibial cartilage from MESE by convolutional neural networks vs. manual expert segmentation with quality control of the same images, and to compare laminar femorotibial cartilage T2 between both approaches. We studied 20 ACL-deficient patients with persistent dynamic knee instability (non-copers), 22 without instability (copers), 13 with surgical ACL reconstruction, and 16 healthy controls. Further patient characterizations are described in a parallel abstract at this conference. Sagittal MESE MRIs were acquired at 1.5T (Siemens Avanto; slice thickness/spacing 3/3.5mm, in-plane resolution 0.31mm, TR 1500ms, TE 9.7/19.4/29.1/38.8/48.5/58.2/67.9ms), at baseline (n=71) and 1 year later (n=55). Manual cartilage segmentation was done by experienced readers, with quality control by an expert (Fig. 1). The U-Net was trained using medial and lateral MRIs from the same scanner (training/ validation set n=50/9) obtained in volleyball athletes of different age groups, and in patients with posterior cruciate ligament (PCL) surgery, segmented by the above readers. Training of the U-net was performed for both all 7 echos and only the 1st echo. Automated U-Net segmentation was then applied to the current study MRIs, without manual intervention or correction. Yet, automated post-processing was employed to correct obvious segmentation errors. The agreement between manual and automated U-Net-based segmentations was evaluated using the Dice Similarly Coefficient (DSC), and by evaluating systematic differences and correlations in cartilage T2 of the 50% superficial and 50% deep layer. When all echoes were used for training, the overall DSC was 0.89 for MT/LT, and 0.83 for cMF/cLF. When only the 1st echo was used,DSCs were 0.87/0.88 and 0.81/0.79. Automated analysis overestimated the segmented tissue volume significantly in most regions, with correlations ranging from 0.93-0.96 for all echoes, and from 0.87-0.94 for the 1st echo. Deep layer T2 across the joint was 45.7ms for manual analysis, 45.7ms with all echoes, and 46.1ms with the 1st echo model; superficial layer T2 was 52.1, 53.2 and 54.4ms. There were statistically significant (albeit small) differences of automated vs. manual analysis across most regions for the superficial layer, and for LT and cLF in the deep layer. The correlation of deep layer T2 across the plates ranged from 0.91-0.99 for all echoes model, and from 0.85-0.98 for the 1st echo model, and that of the superficial T2 from 0.86-0.97, and from 0.73-0.81. Fully automated (U-Net-based) laminar analysis of femorotibial cartilage T2 appears feasible at 1.5T, albeit the agreement for T2 at 3T, and that for cartilage thickness was previously reported to be higher. The agreement with manual analysis was greater when training a model with 7 echoes than with the 1st echo only, and it was greater for the deep than for the superficial layer. No significant change in T2 was observed over 1 year; thus, longer intervals may be required for longitudinal validation.

MRI is now being adopted into roles of patient screening, selection, and outcome assessment in OA clinical trials, yet there are still barriers to universal implementation. Engagement with the OA research community and pharmaceutical industry partners should be a useful reflective exercise that may yield insights to help drive necessary progress in the future. To gain views from attendees at the Osteoarthritis Research Society International (OARSI) 2023 Congress and Imaging Discussion Group session on the role of MRI in OA clinical trial imaging. The OARSI Imaging Discussion Group session took place on 18 March at the 2023 World Congress in Denver, Colorado, USA, on invitation of the congress committee. All congress attendees were invited to an on-line poll (Turning Point Solutions) via a QR code advertised across the venue and signposted during the session. Questions were curated by speakers and panel members and were available to answer online on the day of the event and during the session: Q1. Are current MRI measures accurate enough to enrich DMOAD clinical trials? Q2. In contrast to X-ray, using abbreviated MRI protocols for screening in DMOAD trials allows for... Q3. When designing a DMOAD clinical trial it is important to only include participants with medial radiographic joint space narrowing. Q4. What is the most promising imaging technology to facilitate future DMOAD development? Q5. How could MRI be best used in OA clinical trials for phenotyping beyond cartilage? Q6. What is your perceived greatest hurdle for a successful DMOAD clinical trial? Only one choice was allowed per question, but free text comment was also permitted. The choices for each question are given in the figure. Up to 45 individuals responded, 40 present at the session, 5 in absentia. Q1. Just over half (21/40, 53%) replied “maybe”, suggesting the role of MRI in OA clinical trial enrichment needs clarification. Q2. Most respondents (29/38, 76%) chose the ‘all’ option, suggesting support for the use of MRI in trial screening across several roles. Q3. There was a spread from “Strongly agree” to “Strongly disagree” suggesting that restricting clinical trials to individuals with medial joint space narrowing was divisive. Q4. Non-contrast MRI relaxometry (11/29, 40%) and MRI inflammation-related measures (8/29, 28%) were most popular. Q5. Synovitis (11/30, 37%) and subchondral bone damage (6/30, 20%) were most popular, but 6/30 (20%) also selected “other”. Q6. “Inadequate outcome measures” was most popular (10/29, 35%), but a split between all other options suggested there is still a range of challenges to DMOAD clinical trial success. Results are presented in the figure. The OA imaging research community appears strongly engaged in supporting the use of MRI in OA clinical trials. There are, however, several important areas where clarification and consensus could be enhanced, such as trial enrichment and appropriate OA phenotype selection. Of note, the use of abbreviated MRI protocols in trial screening appeared to be particularly appealing across several roles. None. TT has been a consultant for GSK. None. CORRESPONDENCE ADDRESS: [email protected]

Cartilage transverse relaxation time (T2) has been reported to be sensitive to OA-related changes in cartilage composition, but no study previously reported reference values for femorotibial (FTJ) cartilage T2 side-to-side differences for persons with or without previous ACL injury. Since articular cartilage extracellular matrix has a layered organization, this study focused on laminar (deep and superficial) T2 cartilage times. To assess 1) whether laminar cartilage T2 obtained in two different age groups differs between ACL-injured and contralateral knees and 2) whether between-knee differences differ between ACL injured and healthy controls of the two age groups. In addition, we report laminar T2 thresholds for between-knee differences of healthy controls. 85 participants in four groups (20–30 years healthy, HEA20–30, n=24; 20–30 years ACL injured, ACL20–30, n=23; 40–60 years healthy, HEA40–60, n=24; 40–60 years ACL injured, ACL40–60, n=14) completed data collection. ACL injured participants had a unilateral ACL injury 2–10 years before inclusion. MRIs of HEA left (HEA_l) and right (HEA_r) or ACL injured (ACL_in) and uninjured (ACL_unin) side were acquired using a quantitative 3D DESS sequence (in plane resolution 0.3125*0.3125mm, slice thickness 1.5mm, resolution 512*512, TR 17 ms, TE1 4.85ms, TE2 9.75 ms, FA 15°). Weight-bearing FTJ cartilage plates were manually segmented by Chondrometrics into deep 50% (.D) and superficial 50% (.S) zones and total (.T) cartilage. Between-knee differences in T2 for the FTJ (dif_FTJ) were computed from T2 means (HEA_l–HEA_r or ACL_in–ACL_unin). Nonparametric Dunn and Conover-Iman tests were used for between-group and between-knee comparisons, respectively. Holm correction was used to adjust for multiple comparisons (P<0.05). 80% thresholds for detecting differences between knees were computed from healthy participants as mean(dif_FTJ HEA20–30) ± 1.28*SD(dif_FTJ HEA20–30), and the numbers of participants showing differences in each group exceeding these thresholds were obtained. Deep zone T2 was longer in ACL_in than in ACL_unin and HEA knees (Fig. A). Between-knee differences were only bigger in ACL20–30 and ACL40–60 than in HEA20–30 or HEA40–60 for deep zone dif_FTJ (Fig. B) and not for superficial dif_FTJ (Fig. C) or total dif_FTJ (Fig. D). For deep zone dif_FTJ, the number of participants outside the 80% threshold limits of -1.57 to 1.60 ms was 3/24 (12.5 %) for HEA20–30, 15/23 for (65.2 %) for ACL20–30, 5/24 (20.8 %) for HEA40–60 and 11/14 (78.6 %) for ACL40–60. Elevated FTJ deep zone T2 2 to 10 years after ACL injury (with or without surgical ACL reconstruction) suggests a reduction in cartilage quality (i.e. alterations in cartilage composition and mechanics) after trauma. Comparable deep zone dif_FTJ after ACL injury for both age groups suggests that trauma affects cartilage quality in both groups equally. We conclude that the effects of ACL injury were most pronounced in deep zone T2 and most ACL injured participants showed between-knee differences outside the threshold limits for healthy articular cartilage.