Ultrasonography or Radiography for Suspected Pediatric Distal Forearm Fractures.

BACKGROUND: Data on whether ultrasonography for the initial diagnostic imaging of forearm fractures in children and adolescents is noninferior to radiography for subsequent physical function of the arm are limited. METHODS: In this open-label, multicenter, noninferiority, randomized trial in Australia, we recruited participants 5 to 15 years of age who presented to the emergency department with an isolated distal forearm injury, without a clinically visible deformity, in whom further evaluation with imaging was indicated. Participants were randomly assigned to initially undergo point-of-care ultrasonography or radiography, and were then followed for 8 weeks. The primary outcome was physical function of the affected arm at 4 weeks as assessed with the use of the validated Pediatric Upper Extremity Short Patient-Reported Outcomes Measurement Information System (PROMIS) score (range, 8 to 40, with higher scores indicating better function); the noninferiority margin was 5 points. RESULTS: A total of 270 participants were enrolled, with outcomes for 262 participants (97%) available at 4 weeks (with a window of ±3 days) as prespecified. PROMIS scores at 4 weeks in the ultrasonography group were noninferior to those in the radiography group (mean, 36.4 and 36.3 points, respectively; mean difference, 0.1 point; 95% confidence interval [CI], -1.3 to 1.4). Intention-to-treat analyses (in 266 participants with primary outcome data recorded at any time) produced similar results (mean difference, 0.1 point; 95% CI, -1.3 to 1.4). No clinically important fractures were missed, and there were no between-group differences in the occurrence of adverse events. CONCLUSIONS: In children and adolescents with a distal forearm injury, the use of ultrasonography as the initial diagnostic imaging method was noninferior to radiography with regard to the outcome of physical function of the arm at 4 weeks. (Funded by the Emergency Medicine Foundation and others; BUCKLED Australian New Zealand Clinical Trials Registry number, ACTRN12620000637943).

Respiratory disease in rhesus macaques inoculated with SARS-CoV-2.

An outbreak of coronavirus disease 2019 (COVID-19), which is caused by a novel coronavirus (named SARS-CoV-2) and has a case fatality rate of approximately 2%, started in Wuhan (China) in December 20191,2. Following an unprecedented global spread3, the World Health Organization declared COVID-19 a pandemic on 11 March 2020. Although data on COVID-19 in humans are emerging at a steady pace, some aspects of the pathogenesis of SARS-CoV-2 can be studied in detail only in animal models, in which repeated sampling and tissue collection is possible. Here we show that SARS-CoV-2 causes a respiratory disease in rhesus macaques that lasts between 8 and 16 days. Pulmonary infiltrates, which are a hallmark of COVID-19 in humans, were visible in lung radiographs. We detected high viral loads in swabs from the nose and throat of all of the macaques, as well as in bronchoalveolar lavages; in one macaque, we observed prolonged rectal shedding. Together, the rhesus macaque recapitulates the moderate disease that has been observed in the majority of human cases of COVID-19. The establishment of the rhesus macaque as a model of COVID-19 will increase our understanding of the pathogenesis of this disease, and aid in the development and testing of medical countermeasures.

Phase-contrast virtual chest radiography.

Respiratory X-ray imaging enhanced by phase contrast has shown improved airway visualization in animal models. Limitations in current X-ray technology have nevertheless hindered clinical translation, leaving the potential clinical impact an open question. Here, we explore phase-contrast chest radiography in a realistic in silico framework. Specifically, we use preprocessed virtual patients to generate in silico chest radiographs by Fresnel-diffraction simulations of X-ray wave propagation. Following a reader study conducted with clinical radiologists, we predict that phase-contrast edge enhancement will have a negligible impact on improving solitary pulmonary nodule detection (6 to 20 mm). However, edge enhancement of bronchial walls visualizes small airways (< 2 mm), which are invisible in conventional radiography. Our results show that phase-contrast chest radiography could play a future role in observing small-airway obstruction (e.g., relevant for asthma or early-stage chronic obstructive pulmonary disease), which cannot be directly visualized using current clinical methods, thereby motivating the experimental development needed for clinical translation. Finally, we discuss quantitative requirements on distances and X-ray source/detector specifications for clinical implementation of phase-contrast chest radiography.

MEDUSA: A cloud-based tool for the analysis of X-ray diffuse scattering to obtain the bending modulus from oriented membrane stacks.

An important mechanical property of cells is their membrane bending modulus, κ. Here, we introduce MEDUSA (MEmbrane DiffUse Scattering Analysis), a cloud-based analysis tool to determine the bending modulus, κ, from the analysis of X-ray diffuse scattering. MEDUSA uses GPU (graphics processing unit) accelerated hardware and a parallelized algorithm to run the calculations efficiently in a few seconds. MEDUSA's graphical user interface allows the user to upload 2-dimensional data collected from different sources, perform background subtraction and distortion corrections, select regions of interest, run the fitting procedure and output the fitted parameters, the membranes' bending modulus κ, and compressional modulus B.

Dark-field computed tomography reaches the human scale.

X-ray computed tomography (CT) is one of the most commonly used three-dimensional medical imaging modalities today. It has been refined over several decades, with the most recent innovations including dual-energy and spectral photon-counting technologies. Nevertheless, it has been discovered that wave-optical contrast mechanisms-beyond the presently used X-ray attenuation-offer the potential of complementary information, particularly on otherwise unresolved tissue microstructure. One such approach is dark-field imaging, which has recently been introduced and already demonstrated significantly improved radiological benefit in small-animal models, especially for lung diseases. Until now, however, dark-field CT could not yet be translated to the human scale and has been restricted to benchtop and small-animal systems, with scan durations of several minutes or more. This is mainly because the adaption and upscaling to the mechanical complexity, speed, and size of a human CT scanner so far remained an unsolved challenge. Here, we now report the successful integration of a Talbot-Lau interferometer into a clinical CT gantry and present dark-field CT results of a human-sized anthropomorphic body phantom, reconstructed from a single rotation scan performed in 1 s. Moreover, we present our key hardware and software solutions to the previously unsolved roadblocks, which so far have kept dark-field CT from being translated from the optical bench into a rapidly rotating CT gantry, with all its associated challenges like vibrations, continuous rotation, and large field of view. This development enables clinical dark-field CT studies with human patients in the near future.

Metabolomic profiles of cartilage and bone reflect tissue type, radiography-confirmed osteoarthritis, and spatial location within the joint.

Osteoarthritis is the most common chronic joint disease, characterized by the abnormal remodeling of joint tissues including articular cartilage and subchondral bone. However, there are currently no therapeutic drug targets to slow the progression of disease because disease pathogenesis is largely unknown. Thus, the goals of this study were to identify metabolic differences between articular cartilage and subchondral bone, compare the metabolic shifts in osteoarthritic grade III and IV tissues, and spatially map metabolic shifts across regions of osteoarthritic hip joints. Articular cartilage and subchondral bone from 9 human femoral heads were obtained after total joint arthroplasty, homogenized and metabolites were extracted for liquid chromatography-mass spectrometry analysis. Metabolomic profiling revealed that distinct metabolic endotypes exist between osteoarthritic tissues, late-stage grades, and regions of the diseased joint. The pathways that contributed the most to these differences between tissues were associated with lipid and amino acid metabolism. Differences between grades were associated with nucleotide, lipid, and sugar metabolism. Specific metabolic pathways such as glycosaminoglycan degradation and amino acid metabolism, were spatially constrained to more superior regions of the femoral head. These results suggest that radiography-confirmed grades III and IV osteoarthritis are associated with distinct global metabolic and that metabolic shifts are not uniform across the joint. The results of this study enhance our understanding of osteoarthritis pathogenesis and may lead to potential drug targets to slow, halt, or reverse tissue damage in late stages of osteoarthritis.

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.

Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.

Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.

Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications.

Although chatbots have existed for decades, the emergence of transformer-based large language models (LLMs) has captivated the world through the most recent wave of artificial intelligence chatbots, including ChatGPT. Transformers are a type of neural network architecture that enables better contextual understanding of language and efficient training on massive amounts of unlabeled data, such as unstructured text from the internet. As LLMs have increased in size, their improved performance and emergent abilities have revolutionized natural language processing. Since language is integral to human thought, applications based on LLMs have transformative potential in many industries. In fact, LLM-based chatbots have demonstrated human-level performance on many professional benchmarks, including in radiology. LLMs offer numerous clinical and research applications in radiology, several of which have been explored in the literature with encouraging results. Multimodal LLMs can simultaneously interpret text and images to generate reports, closely mimicking current diagnostic pathways in radiology. Thus, from requisition to report, LLMs have the opportunity to positively impact nearly every step of the radiology journey. Yet, these impressive models are not without limitations. This article reviews the limitations of LLMs and mitigation strategies, as well as potential uses of LLMs, including multimodal models. Also reviewed are existing LLM-based applications that can enhance efficiency in supervised settings.

Environmental Sustainability and AI in Radiology: A Double-Edged Sword.

According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.

Present and Future Innovations in AI and Cardiac MRI.

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.

Assessment of Right Atrial Function Measured with Cardiac MRI Feature Tracking for Predicting Outcomes in Patients with Dilated Cardiomyopathy.

Background Right atrial (RA) function strain is increasingly acknowledged as an important predictor of adverse events in patients with diverse cardiovascular conditions. However, the prognostic value of RA strain in patients with dilated cardiomyopathy (DCM) remains uncertain. Purpose To evaluate the prognostic value of RA strain derived from cardiac MRI (CMR) feature tracking (FT) in patients with DCM. Materials and Methods This multicenter, retrospective study included consecutive adult patients with DCM who underwent CMR between June 2010 and May 2022. RA strain parameters were obtained using CMR FT. The primary end points were sudden or cardiac death or heart transplant. Cox regression analysis was used to determine the association of variables with outcomes. Incremental prognostic value was evaluated using C indexes and likelihood ratio tests. Results A total of 526 patients with DCM (mean age, 51 years ± 15 [SD]; 381 male) were included. During a median follow-up of 41 months, 79 patients with DCM reached the primary end points. At univariable analysis, RA conduit strain was associated with the primary end points (hazard ratio [HR], 0.82 [95% CI: 0.76, 0.87]; P < .001). In multivariable Cox analysis, RA conduit strain was an independent predictor for the primary end points (HR, 0.83 [95% CI: 0.77, 0.90]; P < .001). A model combining RA conduit strain with other clinical and conventional imaging risk factors (C statistic, 0.80; likelihood ratio, 92.54) showed improved discrimination and calibration for the primary end points compared with models with clinical variables (C statistic, 0.71; likelihood ratio, 37.12; both P < .001) or clinical and imaging variables (C statistic, 0.75; likelihood ratio, 64.69; both P < .001). Conclusion CMR FT-derived RA conduit strain was an independent predictor of adverse outcomes among patients with DCM, providing incremental prognostic value when combined in a model with clinical and conventional CMR risk factors. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Lower Extremity Growth according to AI Automated Femorotibial Length Measurement on Slot-Scanning Radiographs in Pediatric Patients.

Background Commonly used pediatric lower extremity growth standards are based on small, dated data sets. Artificial intelligence (AI) enables creation of updated growth standards. Purpose To train an AI model using standing slot-scanning radiographs in a racially diverse data set of pediatric patients to measure lower extremity length and to compare expected growth curves derived using AI measurements to those of the conventional Anderson-Green method. Materials and Methods This retrospective study included pediatric patients aged 0-21 years who underwent at least two slot-scanning radiographs in routine clinical care between August 2015 and February 2022. A Mask Region-based Convolutional Neural Network was trained to segment the femur and tibia on radiographs and measure total leg, femoral, and tibial length; accuracy was assessed with mean absolute error. AI measurements were used to create quantile polynomial regression femoral and tibial growth curves, which were compared with the growth curves of the Anderson-Green method for coverage based on the central 90% of the estimated growth distribution. Results In total, 1874 examinations in 523 patients (mean age, 12.7 years ± 2.8 [SD]; 349 female patients) were included; 40% of patients self-identified as White and not Hispanic or Latino, and the remaining 60% self-identified as belonging to a different racial or ethnic group. The AI measurement training, validation, and internal test sets included 114, 25, and 64 examinations, respectively. The mean absolute errors of AI measurements of the femur, tibia, and lower extremity in the test data set were 0.25, 0.27, and 0.33 cm, respectively. All 1874 examinations were used to generate growth curves. AI growth curves more accurately represented lower extremity growth in an external test set (n = 154 examinations) than the Anderson-Green method (90% coverage probability: 86.7% [95% CI: 82.9, 90.5] for AI model vs 73.4% [95% CI: 68.4, 78.3] for Anderson-Green method; χ2 test, P < .001). Conclusion Lower extremity growth curves derived from AI measurements on standing slot-scanning radiographs from a diverse pediatric data set enabled more accurate prediction of pediatric growth. © RSNA, 2024 Supplemental material is available for this article.

Sacroiliac radiographic progression over 10 years in axSpA: data from the DESIR inception cohort.

OBJECTIVES: To evaluate sacroiliac radiographic progression over a 10-year follow-up and determine the baseline factors associated with such progression in patients with recent-onset axial spondyloarthritis (axSpA, <3 years). METHODS: This analysis was performed in the DESIR cohort (NCT01648907). The radiographic status of the patients (radiographic axSpA (r-axSpA) vs non-radiographic axSpA (nr-axSpA)) was based on the modified New York (mNY) criteria. Information on mNY criteria on the pelvic radiographs was obtained in four reading waves over a 10-year period. Images were blinded and centrally read by 3 trained readers. The % of mNY net progressors (ie, number of 'progressors' minus number of 'regressors' divided by the total number of patients) was assessed in completers (ie, pelvic radiographs at baseline and 10 years). The yearly likelihood of mNY+ was estimated using an integrated analysis (ie, including all patients with at least one available mNY score ('intention-to-follow' population) using a generalised estimating equations model and time-varying tumour necrosis factor (TNF) use as a confounder. Baseline predictors of mNY+ during 10 years were evaluated. RESULTS: Completers included 294 patients, while intention-to-follow included 659 participants. In the completers, the net % progression (from nr-axSpA to r-axSpA) was 5.8%. In the intention-to-follow population, the probability of being mNY+ was estimated to increase 0.87% (95% CI 0.56 to 1.19) per year (ie, 8.7% after 10 years) while when introducing TNF inhibitors (TNFi) as a time-varying covariate, the probability was 0.45% (95% CI 0.09 to 0.81) (ie, 4.5% after 10 years). Baseline bone marrow oedema (BME) on MRI of the sacroiliac joints (SIJ) was associated with being mNY+ over time OR 6.2 (95% CI 5.3 to 7.2) and OR 3.1 (95% CI 2.4 to 3.9) in HLA-B27+ and HLA-B27-, respectively). Male sex, symptom duration >1.5 years, Axial Spondyloarthritis Disease Activity Score ≥2.1 and smoking (only in HLA-B27 positives) were also associated with being mNY+ over 10 years. BME was not found to be a mediator of the HLA-B27 effect on mNY+ at 10 years. CONCLUSIONS: The yearly likelihood of switching from nr-axSpA to r-axSpA in patients after 10 years of follow-up was low, and even lower when considering TNFi use.

Comparison of the effect of treatment with NSAIDs added to anti-TNF therapy versus anti-TNF therapy alone on the progression of structural damage in the spine over 2 years in patients with radiographic axial spondyloarthritis from the randomised-controlled CONSUL trial.

OBJECTIVES: The study aimed to evaluate the effect of adding a non-steroidal anti-inflammatory drug (NSAID), celecoxib (CEL), to a tumour necrosis factor inhibitor (TNFi), golimumab (GOL), compared with TNFi monotherapy on radiographic spinal progression in patients with radiographic axial spondyloarthritis (r-axSpA) over 2 years. METHODS: R-axSpA patients, having risk factors for radiographic progression (high disease activity plus C reactive protein >5 mg/L and/or ≥1 syndesmophyte(s)), underwent a 12-week run-in phase with GOL 50 mg every 4 weeks. In the core phase (96 weeks), only patients with a good clinical response at week 12 were randomised (1:1) to GOL+CEL 200 mg two times per day (combination therapy) or GOL monotherapy. The primary endpoint was radiographic progression assessed by modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) change at week 108 in the intent-to-treat population. RESULTS: A total of 128 patients were enrolled in the run-in phase; and 109 patients were randomised at week 12 to monotherapy (n=55) or combination therapy (n=54). At week 108, 97 (52 vs 45) patients completed the study. The change in mSASSS at week 108 was 1.7 (95% CI 0.8 to 2.6) in the monotherapy vs 1.1 (95% CI 0.4 to 1.8) in the combination therapy groups (p=0.79). New syndesmophytes occurred in 25% of patients in the monotherapy vs 11% of patients in the combination therapy groups (p=0.12). During the study, no significant differences in adverse events and serious adverse events were observed between the groups. CONCLUSIONS: Combination therapy with GOL+CEL did not demonstrate statistically significant superiority over GOL monotherapy in retarding radiographic spinal progression over 2 years in r-axSpA.

2023 EULAR recommendations on imaging in diagnosis and management of crystal-induced arthropathies in clinical practice.

OBJECTIVE: To formulate evidence-based recommendations and overarching principles on the use of imaging in the clinical management of crystal-induced arthropathies (CiAs). METHODS: An international task force of 25 rheumatologists, radiologists, methodologists, healthcare professionals and patient research partners from 11 countries was formed according to the EULAR standard operating procedures. Fourteen key questions on the role of imaging in the most common forms of CiA were generated. The CiA assessed included gout, calcium pyrophosphate deposition disease and basic calcium phosphate deposition disease. Imaging modalities included conventional radiography, ultrasound, CT and MRI. Experts applied research evidence obtained from four systematic literature reviews using MEDLINE, EMBASE and CENTRAL. Task force members provided level of agreement (LoA) anonymously by using a Numerical Rating Scale from 0 to 10. RESULTS: Five overarching principles and 10 recommendations were developed encompassing the role of imaging in various aspects of patient management: making a diagnosis of CiA, monitoring inflammation and damage, predicting outcome, response to treatment, guided interventions and patient education. Overall, the LoA for the recommendations was high (8.46-9.92). CONCLUSIONS: These are the first recommendations that encompass the major forms of CiA and guide the use of common imaging modalities in this disease group in clinical practice.

Relationships between diagnostic imaging of first carpometacarpal osteoarthritis and pain, functional status, and disease progression: A systematic review.

OBJECTIVE: To systematically review the association of pain, function, and progression in first carpometacarpal (CMC) osteoarthritis (OA) with imaging biomarkers and radiography-based staging. DESIGN: Database searches in PubMed, Embase, and the Cochrane Library, along with citation searching were conducted in accordance with published guidance. Data on the association of imaging with pain, functional status, and disease progression were extracted and synthesized, along with key information on study methodology such as sample sizes, use of control subjects, study design, number of image raters, and blinding. Methodological quality was assessed using National Heart, Lung, and Blood Institute tools. RESULTS: After duplicate removal, a total of 1969 records were screened. Forty-six articles are included in this review, covering a total of 28,202 study participants, 7263 with first CMC OA. Osteophytes were found to be one of the strongest biomarkers for pain across imaging modalities. Radiographic findings alone showed conflicting relationships with pain. However, Kellgren-Lawrence staging showed consistent associations with pain in various studies. Radiographic, sonographic, and MRI findings and staging showed little association to tools evaluating functional status across imaging modalities. The same imaging methods showed limited ability to predict progression of first CMC OA. A major limitation was the heterogeneity in the study base, limiting synthesis of results. CONCLUSION: Imaging findings and radiography-based staging systems generally showed strong associations with pain, but not with functional status or disease progression. More research and improved imaging techniques are needed to help physicians better manage patients with first CMC OA.

Looking back on 30+ years of the Johnston County Osteoarthritis Project while looking forward with the Johnston County Health Study: A narrative review.

Over the last 30 years, knowledge of the epidemiology of osteoarthritis (OA) has dramatically advanced, and Osteoarthritis and Cartilage has been on the forefront of disseminating research findings from large OA cohort studies, including the Johnston County OA Project (JoCoOA). The JoCoOA is a population-based, prospective longitudinal cohort that began roughly 30 years ago with a key focus on understanding prevalence, incidence, and progression of OA, as well as its risk factors, in a predominantly rural population of Black and White adults 45+ years old in a county in the southeastern United States. Selected OA results that will be discussed in this review include racial differences, lifetime risk, biomarkers, mortality, and OA risk factors. The new Johnston County Health Study will also be introduced. This new cohort study of OA and comorbid conditions builds upon current OA knowledge and JoCoOA infrastructure and is designed to reflect changes in demographics and urbanization in the county and the region.

Molecular pathogenesis of OA pain: Past, present, and future.

OBJECTIVE: To provide a historical perspective and narrative review on research into the molecular pathogenesis of osteoarthritis pain. DESIGN: PubMed databases were searched for combinations of "osteoarthritis", "pain" and "animal models" for papers that represented key phases in the history of osteoarthritis pain discovery research including epidemiology, pathology, imaging, preclinical modeling and clinical trials. RESULTS: The possible anatomical sources of osteoarthritis pain were identified over 50 years ago, but relatively slow progress has been made in understanding the apparent disconnect between structural changes captured by radiography and symptom severity. Translationally relevant animal models of osteoarthritis have aided in our understanding of the structural and molecular drivers of osteoarthritis pain, including molecules such as nerve growth factor and C-C motif chemokine ligand 2. Events leading to persistent osteoarthritis pain appear to involve a two-step process involving changes in joint innervation, including neo-innervation of the articular cartilage, as well as sensitization at the level of the joint, dorsal root ganglion and central nervous system. CONCLUSIONS: There remains a great need for the development of treatments to reduce osteoarthritis pain in patients. Harnessing all that we have learned over the past several decades is helping us to appreciate the important interaction between structural disease and pain, and this is likely to facilitate development of new disease modifying therapies in the future.