The Association Between the Development of Cam Morphology During Skeletal Growth in High-Impact Athletes and the Presence of Cartilage Loss and Labral Damage in Adulthood: A Prospective Cohort Study With a 12-Year Follow-up.

BACKGROUND: Cam morphology develops during skeletal growth, but its influence on cartilage and the labrum in high-impact athletes later in life is unknown. PURPOSE: To (1) explore the association between the presence and duration of cam morphology during adolescence and the cartilage and labral status 7 to 12 years later and (2) report the prevalence of cartilage loss and labral damage in a population of young male athletes (<32 years old) who played professional soccer during skeletal growth. STUDY DESIGN: Cohort study (Prognosis); Level of evidence, 2. METHODS: A total of 89 healthy male academy soccer players from the Dutch soccer club Feyenoord (aged 12-19 years) were included at baseline. At baseline and 2.5- and 5-year follow-ups, standardized supine anteroposterior pelvis and frog-leg lateral radiographs of each hip were obtained. At 12-year follow-up, magnetic resonance imaging of both hips was performed. Cam morphology was defined by a validated alpha angle ≥60° on radiographs at baseline or 2.5- or 5-year follow-up when the growth plates were closed. Hips with the presence of cam morphology at baseline or at 2.5-year follow-up were classified as having a "longer duration" of cam morphology. Hips with cam morphology only present since 5-year follow-up were classified as having a "shorter duration" of cam morphology. At 12-year follow-up, cartilage loss and labral abnormalities were assessed semiquantitatively. Associations were estimated using logistic regression, adjusted for age and body mass index. RESULTS: Overall, 35 patients (70 hips) with a mean age of 28.0 ± 2.0 years and mean body mass index of 24.1 ± 1.8 participated at 12-year follow-up. Cam morphology was present in 56 of 70 hips (80%). The prevalence of cartilage loss was 52% in hips with cam morphology and 21% in hips without cam morphology (adjusted odds ratio, 4.52 [95% CI, 1.16-17.61]; P = .03). A labral abnormality was present in 77% of hips with cam morphology and in 64% of hips without cam morphology (adjusted odds ratio, 1.99 [95% CI, 0.59-6.73]; P = .27). The duration of cam morphology did not influence these associations. CONCLUSION: The development of cam morphology during skeletal growth was associated with future magnetic resonance imaging findings consistent with cartilage loss in young adults but not with labral abnormalities.

Diagnostic models to predict structural spinal osteoarthritis on lumbar radiographs in older adults with back pain: Development and internal validation.

Objective: It is difficult for health care providers to diagnose structural spinal osteoarthritis (OA), because current guidelines recommend against imaging in patients with back pain. Therefore, the aim of this study was to develop and internally validate multivariable diagnostic prediction models based on a set of clinical and demographic features to be used for the diagnosis of structural spinal OA on lumbar radiographs in older patients with back pain. Design: Three diagnostic prediction models, for structural spinal OA on lumbar radiographs (i.e. multilevel osteophytes, multilevel disc space narrowing (DSN), and both combined), were developed and internally validated in the 'Back Complaints in Older Adults' (BACE) cohort (N â€‹= â€‹669). Model performance (i.e. overall performance, discrimination and calibration) and clinical utility (i.e. decision curve analysis) were assessed. Internal validation was performed by bootstrapping. Results: Mean age of the cohort was 66.9 years (±7.6 years) and 59% were female. All three models included age, gender, back pain duration and duration of spinal morning stiffness as predictors. The combined model additionally included restricted lateral flexion and spinal morning stiffness severity, and exhibited the best model performance (optimism adjusted c-statistic 0.661; good calibration with intercept -0.030 and slope of 0.886) and acceptable clinical utility. The other models showed suboptimal discrimination, good calibration and acceptable decision curves. Conclusion: All three models for structural spinal OA displayed lesuboptimal discrimination and need improvement. However, these internally validated models have potential to inform primary care clinicians about a patient with risk of having structural spinal OA on lumbar radiographs. External validation before implementation in clinical care is recommended.

Unlocking the potential of photon counting detector CT for paediatric imaging: a pictorial essay.

Recent advancements in CT technology have introduced a revolutionary innovation to practice known as the Photon-Counting detector (PCD) CT imaging. The pivotal hardware enhancement of the PCD-CT scanner lies in its detectors, which consist of smaller pixels than standard detectors and allow direct conversion of individual X-rays to electrical signals. As a result, CT images are reconstructed at higher spatial resolution (as low as 0.2 mm) and reduced overall noise, at no expense of an increased radiation dose. These features are crucial for paediatric imaging, especially for infants and young children, where anatomical structures are notably smaller than in adults and in whom keeping dose as low as possible is especially relevant. Since January 2022, our hospital has had the opportunity to work with PCD-CT technology for paediatric imaging. This pictorial review will showcase clinical examples of PCD-CT imaging in children. The aim of this pictorial review is to outline the potential paediatric applications of PCD-CT across different anatomical regions, as well as to discuss the benefits in utilizing PCD-CT in comparison to conventional standard energy integrating detector CT.

Interobserver Agreement and Performance of Concurrent AI Assistance for Radiographic Evaluation of Knee Osteoarthritis.

Background Due to conflicting findings in the literature, there are concerns about a lack of objectivity in grading knee osteoarthritis (KOA) on radiographs. Purpose To examine how artificial intelligence (AI) assistance affects the performance and interobserver agreement of radiologists and orthopedists of various experience levels when evaluating KOA on radiographs according to the established Kellgren-Lawrence (KL) grading system. Materials and Methods In this retrospective observer performance study, consecutive standing knee radiographs from patients with suspected KOA were collected from three participating European centers between April 2019 and May 2022. Each center recruited four readers across radiology and orthopedic surgery at in-training and board-certified experience levels. KL grading (KL-0 = no KOA, KL-4 = severe KOA) on the frontal view was assessed by readers with and without assistance from a commercial AI tool. The majority vote of three musculoskeletal radiology consultants established the reference standard. The ordinal receiver operating characteristic method was used to estimate grading performance. Light kappa was used to estimate interrater agreement, and bootstrapped t statistics were used to compare groups. Results Seventy-five studies were included from each center, totaling 225 studies (mean patient age, 55 years ± 15 [SD]; 113 female patients). The KL grades were KL-0, 24.0% (n = 54); KL-1, 28.0% (n = 63); KL-2, 21.8% (n = 49); KL-3, 18.7% (n = 42); and KL-4, 7.6% (n = 17). Eleven readers completed their readings. Three of the six junior readers showed higher KL grading performance with versus without AI assistance (area under the receiver operating characteristic curve, 0.81 ± 0.017 [SEM] vs 0.88 ± 0.011 [P < .001]; 0.76 ± 0.018 vs 0.86 ± 0.013 [P < .001]; and 0.89 ± 0.011 vs 0.91 ± 0.009 [P = .008]). Interobserver agreement for KL grading among all readers was higher with versus without AI assistance (κ = 0.77 ± 0.018 [SEM] vs 0.85 ± 0.013; P < .001). Board-certified radiologists achieved almost perfect agreement for KL grading when assisted by AI (κ = 0.90 ± 0.01), which was higher than that achieved by the reference readers independently (κ = 0.84 ± 0.017; P = .01). Conclusion AI assistance increased junior readers' radiographic KOA grading performance and increased interobserver agreement for osteoarthritis grading across all readers and experience levels. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Fat-Free Mass Derived From Bioimpedance Spectroscopy and Computed Tomography are in Good Agreement in Patients With Chronic Kidney Disease.

OBJECTIVE: Malnutrition is highly prevalent in patients with kidney failure. Since body weight does not reflect body composition, other methods are needed to determine muscle mass, often estimated by fat-free mass (FFM). Bioimpedance spectroscopy (BIS) is frequently used for monitoring body composition in patients with kidney failure. Unfortunately, BIS-derived lean tissue mass (LTMBIS) is not suitable for comparison with FFM cutoff values for the diagnosis of malnutrition, or for calculating dietary protein requirements. Hypothetically, FFM could be derived from BIS (FFMBIS). This study aims to compare FFMBIS and LTMBIS with computed tomography (CT) derived FFM (FFMCT). Secondarily, we aimed to explore the impact of different methods on calculated protein requirements. METHODS: CT scans of 60 patients with kidney failure stages 4-5 were analyzed at the L3 level for muscle cross-sectional area, which was converted to FFMCT. Spearman rank correlation coefficient and 95% limits of agreement were calculated to compare FFMBIS and LTMBIS with FFMCT. Protein requirements were determined based on FFMCT, FFMBIS, and adjusted body weight. Deviations over 10% were considered clinically relevant. RESULTS: FFMCT correlated most strongly with FFMBIS (r = 0.78, P < .001), in males (r = 0.72, P < .001) and in females (r = 0.60, P < .001). A mean difference of -0.54 kg was found between FFMBIS and FFMCT (limits of agreement: -14.88 to 13.7 kg, P = .544). Between LTMBIS and FFMCT a mean difference of -12.2 kg was apparent (limits of agreement: -28.7 to 4.2 kg, P < .001). Using FFMCT as a reference, FFMBIS best predicted protein requirements. The mean difference between protein requirements according to FFMBIS and FFMCT was -0.7 ± 9.9 g in males and -0.9 ± 10.9 g in females. CONCLUSION: FFMBIS correlates well with FFMCT at a group level, but shows large variation within individuals. As expected, large clinically relevant differences were observed in calculated protein requirements.

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

Associations of childhood BMI, general and visceral fat mass with metabolite profiles at school-age.

BACKGROUND: Childhood obesity increases metabolic disease risk. Underlying mechanisms remain unknown. We examined associations of body mass index (BMI), total body fat mass, and visceral fat mass with serum metabolites at school-age, and explored whether identified metabolites improved the identification of children at risk of a metabolically unhealthy phenotype. METHODS: We performed a cross-sectional analysis among 497 children with a mean age of 9.8 (95% range 9.1, 10.6) years, participating in a population-based cohort study. We measured BMI, total body fat mass using DXA, and visceral fat mass using MRI. Serum concentrations of amino-acids, non-esterified-fatty-acids, phospholipids, and carnitines were determined using LC-MS/MS. Children were categorized as metabolically healthy or metabolically unhealthy, according to BMI, blood pressure, lipids, glucose, and insulin levels. RESULTS: Higher BMI and total body fat mass were associated with altered concentrations of branched-chain amino-acids, essential amino-acids, and free carnitines. Higher BMI was also associated with higher concentrations of aromatic amino-acids and alkyl-lysophosphatidylcholines (FDR-corrected p-values < 0.05). The strongest associations were present for Lyso.PC.a.C14.0 and SM.a.C32.2 (FDR-corrected p-values < 0.01). Higher visceral fat mass was only associated with higher concentrations of 6 individual metabolites, particularly Lyso.PC.a.C14.0, PC.aa.C32.1, and SM.a.C32.2. We selected 15 metabolites that improved the prediction of a metabolically unhealthy phenotype, compared to BMI only (AUC: BMI: 0.59 [95% CI 0.47,0.71], BMI + Metabolites: 0.91 [95% CI 0.85,0.97]). CONCLUSIONS: An adverse childhood body fat profile, characterized by higher BMI and total body fat mass, is associated with metabolic alterations, particularly in amino acids, phospholipids, and carnitines. Fewer associations were present for visceral fat mass. We identified a metabolite profile that improved the identification of impaired cardiometabolic health in children, compared to BMI only.

Ultrasound of the forefeet besides the hands in patients at risk for rheumatoid arthritis: is it worth the effort? A longitudinal cohort study.

OBJECTIVE: Ultrasound (US) can detect subclinical joint-inflammation in patients with clinically suspect arthralgia (CSA), which is valuable as predictor for rheumatoid arthritis (RA) development. In most research protocols both hands and forefeet are scanned, but it is unclear if US of the forefeet has additional value for predicting RA, especially since synovial hypertrophy in MTP-joints of healthy individuals is also common. To explore the possibility to omit scanning of the forefeet we determined if US of the forefeet is of additional predictive value for RA-development in CSA patients. METHODS: CSA patients of two independent cohorts underwent US of the hands and forefeet. We analyzed the association between RA-development and US-positivity for the full US-protocol, the full US-protocol with correction for Gray Scale(GS)-findings in the forefeet of healthy and the protocol without-forefeet. RESULTS: In total, 298 CSA patients were studied. In patients with a positive US, subclinical joint-inflammation was mostly present in the hands (90-86%). Only 10-14% of patients had subclinical joint-inflammation solely in the forefeet. US-positivity was associated with inflammatory arthritis development in both cohorts, with HRs 2.6(95%CI 0.9-7.5) and 3.1(95%CI 1.5-6.4) for the full protocol, 3.1(95%CI 1.3-7.7) and 2.7(95%CI 1.3-5.4) for the full US-protocol with correction, and 3.1(95%CI 1.4-6.9) and 2.8(95%CI 1.4-5.6) without the forefeet. AUROCs were equal across both cohorts. CONCLUSION: The forefeet can be omitted when US is used for the prediction of RA-development in CSA patients. This is due to the finding that subclinical joint-inflammation in the forefeet without concomitant inflammation in the hands is infrequent.

Recruitment and baseline characteristics of young adults at risk of early-onset knee osteoarthritis after ACL reconstruction in the SUPER-Knee trial.

Objectives: The study aims to (1) report the process of recruiting young adults into a secondary knee osteoarthritis prevention randomised controlled trial (RCT) after anterior cruciate ligament reconstruction (ACLR); (2) determine the number of individuals needed to be screened to include one participant (NNS) and (3) report baseline characteristics of randomised participants. Methods: The SUpervised exercise-therapy and Patient Education Rehabilitation (SUPER)-Knee RCT compares SUPER and minimal intervention for young adults (aged 18-40 years) with ongoing symptoms (ie, mean score of <80/100 from four Knee injury and Osteoarthritis Outcome Score subscales (KOOS4)) 9-36 months post-ACLR. The NNS was calculated as the number of prospective participants screened to enrol one person. At baseline, participants provided medical history, completed questionnaires (demographic, injury/surgery, rehabilitation characteristics) and underwent physical examination. Results: 1044 individuals were screened to identify 567 eligible people, from which 184 participants (63% male) enrolled. The sample of enrolled participants was multicultural (29% born outside Australia; 2% Indigenous Australians). The NNS was 5.7. For randomised participants, mean±SD age was 30±6 years. The mean body mass index was 27.3±5.2 kg/m2, with overweight (43%) and obesity (21%) common. Participants were, on average, 2.3 years post-ACLR. Over half completed <8 months of postoperative rehabilitation, with 56% having concurrent injury/surgery to meniscus and/or cartilage. The most affected KOOS (0=worst, 100=best) subscale was quality of life (mean 43.7±19.1). Conclusion: Young adults post-ACLR were willing to participate in a secondary osteoarthritis prevention trial. Sample size calculations should be multiplied by at least 5.7 to provide an estimate of the NNS. The SUPER-Knee cohort is ideally positioned to monitor and intervene in the early development and trajectory of osteoarthritis. Trial registration number: ACTRN12620001164987.

Post-traumatic and OA-related lesions in the knee at baseline and 2 years after traumatic meniscal injury: Secondary analysis of a randomized controlled trial.

OBJECTIVE: To assess the presence of early degenerative changes on Magnetic Resonance Imaging (MRI) 24 months after a traumatic meniscal tear and to compare these changes in patients treated with arthroscopic partial meniscectomy or physical therapy plus optional delayed arthroscopic partial meniscectomy. DESIGN: We included patients aged 18-45 years with a recent onset, traumatic, MRI verified, isolated meniscal tear without radiographic osteoarthritis. Patients were randomized to arthroscopic partial meniscectomy or standardized physical therapy with optional delayed arthroscopic partial meniscectomy. MRIs at baseline and 24 months were scored using the MRI Osteoarthritis Knee Score (MOAKS). We compared baseline MRIs to healthy controls aged 18-40 years. The outcome was the progression of bone marrow lesions (BMLs), cartilage defects and osteophytes after 24 months in patients. RESULTS: We included 99 patients and 50 controls. At baseline, grade 2 and 3 BMLs were present in 26% of the patients (n = 26), compared to 2% of the controls (n = 1) (between group difference 24% (95% CI 15% to 34%)). In patients, 35% (n = 35) had one or more cartilage defects grade 1 or higher, compared to 2% of controls (n = 1) (between group difference 33% (95% CI 23% to 44%)). At 24 months MRI was available for 40 patients randomized to arthroscopic partial meniscectomy and 41 patients randomized to physical therapy. At 24 months 30% (n = 12) of the patients randomized to arthroscopic partial meniscectomy showed BML worsening, compared to 22% (n = 9) of the patients randomized to physical therapy (between group difference 8% (95% CI -11% to 27%)). Worsening of cartilage defects was present in 40% (n = 16) of the arthroscopic partial meniscectomy group, compared to 22% (n = 9) of the physical therapy group (between group difference 18% (95% CI -2% to 38%)). Of the patients who had no cartilage defect at baseline, 33% of the arthroscopic partial meniscectomy group had a new cartilage defect at follow-up compared to 14% of the physical therapy group. Osteophyte worsening was present in 18% (n = 7) of the arthroscopic partial meniscectomy group and 15% (n = 6) of the physical therapy group (between group difference 3% (95% CI -13% to 19%)). CONCLUSIONS: Our results might suggest more worsening of BMLs and cartilage defects with arthroscopic partial meniscectomy compared to physical therapy with optional delayed arthroscopic partial meniscectomy at 24-month follow-up in young patients with isolated traumatic meniscal tears without radiographic OA.

Chances and challenges of photon-counting CT in musculoskeletal imaging.

In musculoskeletal imaging, CT is used in a wide range of indications, either alone or in a synergistic approach with MRI. While MRI is the preferred modality for the assessment of soft tissues and bone marrow, CT excels in the imaging of high-contrast structures, such as mineralized tissue. Additionally, the introduction of dual-energy CT in clinical practice two decades ago opened the door for spectral imaging applications. Recently, the advent of photon-counting detectors (PCDs) has further advanced the potential of CT, at least in theory. Compared to conventional energy-integrating detectors (EIDs), PCDs provide superior spatial resolution, reduced noise, and intrinsic spectral imaging capabilities. This review briefly describes the technical advantages of PCDs. For each technical feature, the corresponding applications in musculoskeletal imaging will be discussed, including high-spatial resolution imaging for the assessment of bone and crystal deposits, low-dose applications such as whole-body CT, as well as spectral imaging applications including the characterization of crystal deposits and imaging of metal hardware. Finally, we will highlight the potential of PCD-CT in emerging applications, underscoring the need for further preclinical and clinical validation to unleash its full clinical potential.

Towards clinical implementation of an AI-algorithm for detection of cervical spine fractures on computed tomography.

BACKGROUND: Artificial intelligence (AI) applications can facilitate detection of cervical spine fractures on CT and reduce time to diagnosis by prioritizing suspected cases. PURPOSE: To assess the effect on time to diagnose cervical spine fractures on CT and diagnostic accuracy of a commercially available AI application. MATERIALS AND METHODS: In this study (June 2020 - March 2022) with historic controls and prospective evaluation, we evaluated regulatory-cleared AI-software to prioritize cervical spine fractures on CT. All patients underwent non-contrast CT of the cervical spine. The time between CT acquisition and the moment the scan was first opened (DNT) was compared between the retrospective and prospective cohorts. The reference standard for determining diagnostic accuracy was the radiology report created in routine clinical workflow and adjusted by a senior radiologist. Discrepant cases were reviewed and clinical relevance of missed fractures was determined. RESULTS: 2973 (mean age, 55.4 ± 19.7 [standard deviation]; 1857 men) patients were analyzed by AI, including 2036 retrospective and 938 prospective cases. Overall prevalence of cervical spine fractures was 7.6 %. The DNT was 18 % (5 min) shorter in the prospective cohort. In scans positive for cervical spine fracture according to the reference standard, DNT was 46 % (16 min) shorter in the prospective cohort. Overall sensitivity of the AI application was 89.8 % (95 % CI: 84.2-94.0 %), specificity was 95.3 % (95 % CI: 94.2-96.2 %), and diagnostic accuracy was 94.8 % (95 % CI: 93.8-95.8 %). Negative predictive value was 99.1 % (95 % CI: 98.5-99.4 %) and positive predictive value was 63.0 % (95 % CI: 58.0-67.8 %). 22 fractures were missed by AI of which 5 required stabilizing therapy. CONCLUSION: A time gain of 16 min to diagnosis for fractured cases was observed after introducing AI. Although AI-assisted workflow prioritization of cervical spine fractures on CT shows high diagnostic accuracy, clinically relevant cases were missed.

Body composition and respiratory outcomes in children: a population-based prospective cohort study.

BACKGROUND: Body composition might influence lung function and asthma in children, but its longitudinal relations are unclear. We aimed to identify critical periods for body composition changes during childhood and adolescence in relation to respiratory outcomes in adolescents. METHODS: In a population-based prospective cohort study, we measured body mass index, fat mass index (FMI), lean mass index (LMI) and the ratio of android fat mass divided by gynoid fat mass (A/G ratio) by dual-energy X-ray absorptiometry at 6, 10 and 13 years. At 13 years, lung function was measured by spirometry, and current asthma was assessed by questionnaire. RESULTS: Most prominently and consistently, higher FMI and A/G ratio at age 13 years were associated with lower forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) and forced expiratory flow after exhaling 75% of FVC (FEF75) (range Z-score difference -0.13 (95% CI -0.16 to -0.10) to -0.08 (95% CI -0.11 to -0.05) per SD score increase), and higher LMI at all ages was associated with higher FEF75 (range Z-score difference 0.05 (95% CI 0.01 to 0.08) to 0.09 (95% CI 0.06 to 0.13)). Between the ages of 6 and 13 years, normal to high FMI and A/G ratio were associated with lower FEV1/FVC and FEF75 (range Z-score difference -0.20 (95% CI -0.30 to -0.10) to -0.17 (95% CI -0.28 to -0.06)) and high to high LMI with higher FEF75 (range Z-score difference0.32 (95% CI 0.23 to 0.41)). Body composition changes were not associated with asthma. CONCLUSION: Adolescents with higher total and abdominal fat indices may have impaired lung function, while those with a higher lean mass during childhood and adolescence may have better small airway function. Public health measures should focus on a healthy body composition in adolescents to minimise respiratory morbidity.

Automated Deep Learning-Based Segmentation of Abdominal Adipose Tissue on Dixon MRI in Adolescents: A Prospective Population-Based Study.

BACKGROUND. The prevalence of childhood obesity has increased significantly worldwide, highlighting a need for accurate noninvasive quantification of body fat distribution in children. OBJECTIVE. The purpose of this study was to develop and test an automated deep learning method for subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) segmentation using Dixon MRI acquisitions in adolescents. METHODS. This study was embedded within the Generation R Study, a prospective population-based cohort study in Rotterdam, The Netherlands. The current study included 2989 children (1432 boys, 1557 girls; mean age, 13.5 years) who underwent investigational whole-body Dixon MRI after reaching the age of 13 years during the follow-up phase of the Generation R Study. A 2D competitive dense fully convolutional neural network model (2D-CDFNet) was trained from scratch to segment abdominal SAT and VAT using Dixon MRI-based images. The model underwent training, validation, and testing in 62, eight, and 15 children, respectively, who were selected by stratified random sampling, with manual segmentations used as reference. Segmentation performance was assessed using the Dice similarity coefficient and volumetric similarity. Two observers independently performed subjective visual assessments of automated segmentations in 504 children, selected by stratified random sampling, with undersegmentation and oversegmentation scored on a scale of 0-3 (with a score of 3 denoting nearly perfect segmentation). For 2820 children for whom complete data were available, Spearman correlation coefficients were computed among MRI measurements and BMI and dual-energy x-ray absorptiometry (DEXA)-based measurements. The model used (gitlab.com/radiology/msk/genr/abdomen/cdfnet) is publicly available. RESULTS. In the test dataset, the mean Dice similarity coefficient and mean volu-metric similarity, respectively, were 0.94 ± 0.03 [SD] and 0.98 ± 0.01 [SD] for SAT and 0.85 ± 0.05 and 0.92 ± 0.04 for VAT. The two observers assigned a score of 3 for SAT in 94% and 93% for the undersegmentation proportion and in 99% and 99% for the oversegmentation proportion, and they assigned a score of 3 for VAT in 99% and 99% for the undersegmentation proportion and in 95% and 97% for the oversegmentation proportion. Correlations with SAT and VAT were 0.808 and 0.698 for BMI and 0.941 and 0.801 for DEXA-derived fat mass. CONCLUSION. We trained and evaluated the 2D-CDFNet model on Dixon MRI in adolescents. Quantitative and qualitative measures of automated SAT and VAT segmentations indicated strong model performance. CLINICAL IMPACT. The automated model may facilitate large-scale studies investigating abdominal fat distribution on MRI among adolescents as well as associations of fat distribution with clinical outcomes.

Talocrural Arthrodesis Increases Osteoarthritis Severity in Adjacent Joints: A Midterm Computed Tomography Follow-Up Study.

BACKGROUND: After talocrural arthrodesis, adjacent joints (subtalar, talonavicular, and calcaneocuboid) are often affected by osteoarthritis (OA)). It is unclear if OA is pre-existing to talocrural arthrodesis, or whether it develops after talocrural arthrodesis. This retrospective study is unique because it is the first study with preoperative and follow-up computed tomography (CT). The aim of this study is to investigate whether OA develops in adjacent joints after talocrural arthrodesis or if OA is already pre-existing. In addition, associations between degree of OA and patient-reported outcomes are investigated. METHODS: Patients were selected from electronic files, and adjacent joint OA was assessed on preoperative CT and bilateral follow-up CT. Patient-reported outcomes were collected. RESULTS: Twenty-three patients were included with an average follow-up time of 7 years (SD = 2). In participants without pre-existing OA, OA significantly progressed in all adjacent joints. In participants with pre-existing OA, OA progressed in the subtalar joint. Patient-reported outcomes were not correlated to OA. CONCLUSIONS: Osteoarthritis in the adjacent joints progresses after talocrural arthrodesis, especially in participants without pre-existing OA. The severity of OA is not related to patient-reported outcomes. Therefore, the clinical impact of the progression of OA seems to be limited. LEVEL OF EVIDENCE: Level III: retrospective.

Advanced Magnetic Resonance Imaging and Molecular Imaging of the Painful Knee.

Chronic knee pain is a common condition. Causes of knee pain include trauma, inflammation, and degeneration, but in many patients the pathophysiology remains unknown. Recent developments in advanced magnetic resonance imaging (MRI) techniques and molecular imaging facilitate more in-depth research focused on the pathophysiology of chronic musculoskeletal pain and more specifically inflammation. The forthcoming new insights can help develop better targeted treatment, and some imaging techniques may even serve as imaging biomarkers for predicting and assessing treatment response in the future. This review highlights the latest developments in perfusion MRI, diffusion MRI, and molecular imaging with positron emission tomography/MRI and their application in the painful knee. The primary focus is synovial inflammation, also known as synovitis. Bone perfusion and bone metabolism are also addressed.

Clinical Applications of Photon-counting CT: A Review of Pioneer Studies and a Glimpse into the Future.

CT systems equipped with photon-counting detectors (PCDs), referred to as photon-counting CT (PCCT), are beginning to change imaging in several subspecialties, such as cardiac, vascular, thoracic, and musculoskeletal radiology. Evidence has been building in the literature underpinning the many advantages of PCCT for different clinical applications. These benefits derive from the distinct features of PCDs, which are made of semiconductor materials capable of converting photons directly into electric signal. PCCT advancements include, among the most important, improved spatial resolution, noise reduction, and spectral properties. PCCT spatial resolution on the order of 0.25 mm allows for the improved visualization of small structures (eg, small vessels, arterial walls, distal bronchi, and bone trabeculations) and their pathologies, as well as the identification of previously undetectable anomalies. In addition, blooming artifacts from calcifications, stents, and other dense structures are reduced. The benefits of the spectral capabilities of PCCT are broad and include reducing radiation and contrast material dose for patients. In addition, multiple types of information can be extracted from a single data set (ie, multiparametric imaging), including quantitative data often regarded as surrogates of functional information (eg, lung perfusion). PCCT also allows for a novel type of CT imaging, K-edge imaging. This technique, combined with new contrast materials specifically designed for this modality, opens the door to new applications for imaging in the future.