Knee Magnetic Resonance Imaging Research Articles

Prognostic power of criteria for symptomatic early knee osteoarthritis from a 2-year longitudinal observation of the Iwaki cohort study

PurposeThis study aimed to investigate whether symptomatic early knee osteoarthritis (EKOA) effectively predicts the incidence of definitive knee osteoarthritis (DKOA) in the general Japanese population. MethodsA total of 133 women from the Japanese Iwaki cohort study were enrolled and followed-up over 2 years in order to calculate the rate of progression of EKOA to DKOA. Weight-bearing anteroposterior bilateral knee radiographs and magnetic resonance imaging (MRI) were conducted at baseline and follow-up. Radiographs were classified according to the Kellgren–Lawrence grade. The presence of cartilage lesion, bone marrow lesions, attrition, cysts, osteophytes, and meniscal lesions was evaluated according to the Whole-Organ Magnetic Resonance Imaging Score (WORMS). Logistic regression analyses were performed to determine the predictive power of EKOA. ResultsOf 25 women with EKOA, 15 (60%) progressed to DKOA over 2 years, showing a relative risk (RR) of 1.44 compared with the non-osteoarthritis (non-OA) group (P ​= ​0.120). Logistic regression analysis showed associations of EKOA (P ​= ​0.048) and high body mass index (P ​< ​0.001) with progression to DKOA. Combined EKOA and meniscal lesions increased the RR for osteoarthritis incidence to 2.32 (P ​= ​0.004) compared with the non-OA group. Also, meniscus and bone marrow lesion scores on MRI of the EKOA group maintained high scores over 2 years. ConclusionsThe prognostic power of EKOA criteria was confirmed with a RR of 1.44. The combination of symptomatic EKOA criteria and MRI-detected meniscal lesions was a valuable predictor of progression to DKOA over 2 years in women without radiographic abnormalities.

Management of Patellar and Trochlear Cartilage Lesions with Matrix-Induced Autologous Chondrocyte Implantation in Conjunction with Patellofemoral Realignment Procedures Improves Patient Reported Outcomes and Magnetic Resonance Image Appearance

ObjectivesThe aim of this study is to evaluate the relationship between the achievement of clinically significant improvement in patient-reported outcome measures (PROMs) and the postoperative magnetic resonance image (MRI) appearance of matrix-associated chondrocyte implantation (MACI), in conjunction with patellofemoral realignment procedures, for the treatment of grade IV chondral defects about the patellofemoral joint. MethodsA retrospective review of patients undergoing MACI for grade IV chondral defects of the patella or trochlea by a single sports medicine fellowship-trained surgeon from 2017-2020 was performed. Concomitant realignment procedures, including tibial tubercle osteotomy and medial patellofemoral ligament reconstruction, were also performed as needed. Patients with preoperative and minimum 1-year postoperative PROMs and postoperative knee MRI were included. MRI were obtained at 6.3 [Interquartile range (IQR): 5.8, 7.5] months postoperatively. A fellowship-trained musculoskeletal radiologist assigned a Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score (range: 0-100, with 100 equating to complete graft healing) to each MRI. Achievement of the minimal clinically important difference (MCID) for International Knee Documentation Committee (IKDC), Knee Injury and Osteoarthritis Score–Quality of Life (KOOS-QoL), and Kujala scores were determined for each patient. Paired t-tests or Wilcoxon Rank-Sum tests were used to evaluate for an association between achievement of the MCID for each PROM and MOCART scores. The average follow-up time and time from surgery to PROMs were 2.7±1.5 years and 1.7±0.66 years, respectively. ResultsThirty patients were included. There was a significant improvement in all PROMs from pre- to postoperative (p<0.001). More than two thirds of patients achieved the MCID for each PROM. Patients who achieved the MCID for IKDC had significantly higher MOCART scores (66.5±16.2) compared to those who did not meet the MCID for IKDC (50.6±23.6, p=0.043). ConclusionMACI for the treatment of patellofemoral chondral injuries is associated with clinically significant improvement in PROMs at short-term follow up. Clinically significant improvements in IKDC scores are associated with a more mature MRI appearance of the ACI graft on postoperative MRI, as indicated by higher MOCART scores. Level of EvidenceIV – Case Series

Two-Year Functional Outcomes of Nonsurgical Treatment in Concomitant Anterior Cruciate Ligament and Medial Collateral Ligament Injuries: A Case-Control Study.

The anterior cruciate ligament (ACL) is a common knee injury in high-intensity sports, which can cause early career loss in young athletes. Concomitant damage to other knee stabilizers may occur, such as the medial collateral ligament (MCL). Recent studies have shown that knee stability can increase without surgical intervention in patients with ACL and MCL injuries. Regarding the importance of functional tests in return to exercise prediction, this study aims to measure nonsurgical approach's long-term outcome for concomitant ACL and MCL injuries with a focus on functional tests. This is a case-control study with a 2-year follow-up. The case group consisted of patients who had provided written consent and completed their 2-year follow-up, and the control group was made up of healthy people who did not have any knee medical conditions and were matched by age, gender, and activity level. Physical examinations, Tegner and International Knee Documentation Committee questionnaires, and knee magnetic resonance imaging were conducted, and functional performance tests were performed after a 10-minute warm-up. Lody's index (the ratio of injured-to-uninjured knee results) was calculated. The data were analyzed using independent t-test, one-way analysis of variance, chi-squared test, and Fisher's exact test. The study involved 11 patients in each concomitant ACL and MCL injury cases and healthy control groups with a mean age of 32.4 and 28 years, respectively. None of the patients reported knee instability symptoms in the 2-year follow-up. More than half of the patients continued their sports field without reinjury, with no significant difference in activity levels between case and control groups. The 6-meter hop test and single-leg hop test showed no significant difference between case and control groups (p-value: 0.326, 0.859), and no significant difference was observed in the three Carioca, cocontraction, and Shuttle tests in the 2-year follow-up. Functional tests in ACL and MCL injuries revealed normal outcomes, implying a nonsurgical approach for patients with proximal ACL tears, better knee stability, and no significant differences between the injured and control groups.

High-resolution oblique coronal MRI at optimal flexed-knee angle: a novel imaging method for enhanced anterior cruciate ligament tear diagnosis

BackgroundThe accuracy of traditional knee MR imaging in diagnosing anterior cruciate ligament tears, especially partial tears, is relatively low, which may lead to misdiagnosis and missed diagnosis. This study aimed to assess the diagnostic performance of a novel imaging method, high-resolution oblique coronal MRI at an optimal flexed-knee Angle, for ACL tears.Methods50 healthy volunteers were scanned with a scan-assisted device for the optimal flexion angle of ACL. For 92 knee trauma patients selected strictly according to inclusion and exclusion criteria, conventional extended-knee scans (control group) and high-resolution oblique coronal scans based on the optimal flexed-knee angle (experimental group) were conducted. Two observers rated ACL visibility blindly on a 5-point scale. Arthroscopy-defined outcomes determined diagnostic metrics for each method and sensitivity, specificity, positive predictive value, negative predictive value and accuracy were calculated.ResultsThe average optimal flexion angle for healthy volunteers was approximately 30° (30.3° ± 5.0°). Imaging demonstrated complete visualization of the ACL in 96.7% of images in the experimental group versus 12.0% in the control group. The diagnostic indicators of the experimental group surpassed those of the control group: sensitivity (94.9% vs. 76.3%), specificity (97.0% vs. 81.8%), positive predictive value (98.2% vs. 88.2%), negative predictive value(91.4% vs. 65.9%), and accuracy (95.7% vs. 78.3%). ROC analysis indicated superior diagnostic performance in the experimental group, with an AUC of 0.945 compared with 0.776 for the control group (p < 0.0001).ConclusionsHigh-resolution oblique coronal imaging at the optimal 30° flexed-knee angle improved ACL visualization and diagnostic performance compared with conventional techniques.

Synovial hemangioma presenting with chronic painful infrapatellar mass: a case report

BackgroundSynovial hemangiomas are rare benign vascular anomalies surrounded by a synovial lining and were first described by Bouchut in 1856. These neoplasms can develop in the intra-articular region, resulting in effusions and knee pain. However, their cause remains unknown. Prompt diagnosis and intervention are critical to prevent chondral damage. Histopathological examination is used to achieve the diagnosis, which is often delayed because of a lack of specific clinical signs. This report describes a unique case in which a painful infrapatellar mass was diagnosed as a synovial hemangioma. The absence of typical magnetic resonance imaging (MRI) findings highlights the importance of arthroscopic excision for diagnosis and symptom relief.Case presentationA 20-year-old woman presented with persistent anterior left knee pain that became exacerbated when she climbed stairs. Despite previous pain management and physical therapy, she developed a painful lump beneath her patella that worsened over time. She had also undergone arthrocentesis, but this did not relieve her pain. Physical examination revealed a palpable, immobile 5-cm mass along the patellar tendon with limited knee flexion and extension and normal ligament stability. T1-weighted fat-saturated MRI of the left knee with gadolinium-based contrast revealed a lobulated intra-articular mass in Hoffa’s fat pad that resembled a soft tissue chondroma. A biopsy of the mass was performed to provide histopathological evidence, confirming the benign nature of the mass. The subsequent excisional arthroscopy, combined with incision enlargement for mass removal, confirmed the histopathologic diagnosis of synovial hemangioma based on the presence of numerous dilated blood vessels and venous proliferation within sections of the synovium. Recovery was complete, and no residual tumor was detected on follow-up MRI after 1 year.ConclusionThis case study emphasizes the importance of arthroscopic excision over open surgery for patients with synovial hemangioma. The minimally invasive nature of arthroscopy combined with the well-encapsulated nature and location of the mass facilitates complete resection.

Fresh osteochondral allograft transplantation for knee full-thickness articular cartilage lesions using femoral head of living donors: short-term results.

Fresh osteochondral allograft transplantation is a good treatment option of cartilage defects. However, this treatment option is not available in all countries due to limited graft availability and tissue banks limitations. The purpose of this study is to assess the short term functional and imaging outcomes of fresh osteochondral allograft transplantation in the knee using the femoral head of living donors. Fresh osteochondral allografts from the femoral heads of living donors is a valid graft source for management of distal Femur cartilage defects. This technique can improve functional knee scores with good radiological outcomes. Prospective case series. Fifteen patients with full thickness cartilage defects of the distal femur underwent osteochondral allograft transplantation from the femoral heads of living donors. Grafts were transplanted by both shell and multiple dowels techniques. The average follow up duration was 18.3 months (range, 12-25 months). Patients were evaluated by Lysholm and International Knee Documentation Committee (IKDC) scores, radiography and MR imaging using Osteochondral Allograft MRI Scoring System (OCAMRISS). There was a statistically significant improvement (P < 0.001) in both Lysholm and IKDC average scores at 6 months and 12 months postoperative. Postoperative MRI was done at an average 6.8 months (range, 5-11 months) postoperative. The mean total OCAMRISS score was 3.4 (range, 1-7). A second look arthroscopy was done in four patients and showed intact articular cartilage in all three patients. Femoral head of living donors is a valid new source for fresh osteochondral allograft transplantation of knee osteochondral lesions. Short term results showed improvement in clinical assessment scores. Follow up imaging showed graft incorporation and good MRI scores.

Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T.

The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms. This was a prospective single-center study. Twenty-three healthy volunteers underwent 7 T knee magnetic resonance imaging. Two-, 3-, and 4-fold accelerated high-resolution fat-signal-suppressing proton density (PD-fs) and T1-weighted coronal 2D TSE acquisitions with an encoded voxel volume of 0.31 × 0.31 × 1.5 mm3 were acquired. Each set of raw data was reconstructed with a DL-based and a conventional Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm. Three readers rated image contrast, sharpness, artifacts, noise, and overall quality. Friedman analysis of variance and the Wilcoxon signed rank test were used for comparison of image quality criteria. The mean age of the participants was 32.0 ± 8.1 years (15 male, 8 female). Acquisition times at 4-fold acceleration were 4 minutes 15 seconds (PD-fs, Supplemental Video is available at http://links.lww.com/RLI/A938) and 3 minutes 9 seconds (T1, Supplemental Video available at http://links.lww.com/RLI/A939). At 4-fold acceleration, image contrast, sharpness, noise, and overall quality of images reconstructed with the DL-based algorithm were significantly better rated than the corresponding GRAPPA reconstructions (P < 0.001). Four-fold accelerated DL-reconstructed images scored significantly better than 2- to 3-fold GRAPPA-reconstructed images with regards to image contrast, sharpness, noise, and overall quality (P ≤ 0.031). Image contrast of PD-fs images at 2-fold acceleration (P = 0.087), image noise of T1-weighted images at 2-fold acceleration (P = 0.180), and image artifacts for both sequences at 2- and 3-fold acceleration (P ≥ 0.102) of GRAPPA reconstructions were not rated differently than those of 4-fold accelerated DL-reconstructed images. Furthermore, no significant difference was observed for all image quality measures among 2-fold, 3-fold, and 4-fold accelerated DL reconstructions (P ≥ 0.082). This study explored the technical potential of DL-based image reconstruction in accelerated 2D TSE acquisitions of the knee at 7 T. DL reconstruction significantly improved a variety of image quality measures of high-resolution TSE images acquired with a 4-fold parallel-imaging acceleration compared with a conventional reconstruction algorithm.

Anterior cruciate ligament tear detection using gated recurrent unit and flexible fitness dependent optimizer

Anterior Cruciate Ligament (ACL) tear is a common knee injury that can be diagnosed using Magnetic Resonance Imaging (MRI). In this study, a novel approach has been proposed for improving the accuracy of ACL tear detection using an optimized Gated Recurrent Unit (GRU) model. The GRU model is a type of Recurrent Neural Network (RNN) that can capture the temporal features of MRI images. The optimization of the GRU model is based on a modified metaheuristic technique, called the Flexible Fitness Dependent Optimizer (FFDO). The FFDO is a flexible and problem-dependent algorithm that can find the best solutions to optimize problems by generating and evaluating a set of candidate solutions based on a fitness function. The proposed approach is applied to the MRNet dataset, and the results are compared with 17 state-of-the-art methods. The results show that the proposed approach can achieve a high accuracy of97.95%for ACL tear detection, which is higher than the existing deep learning and machine learning methods. The proposed approach can also achieve high values for other performance metrics, such as specificity, sensitivity, precision, MCC, F1-score, and AUC. The proposed approach can effectively extract the essential features of knee MRI images that are indicative of ACL tears with high diagnostic accuracy.

The Role of Magnetic Resonance Imaging in the Evaluation of Knee Pain.

Objectives This study aimed to characterize and compare the features of traumatic and non-traumatic lesions causing knee pain through magnetic resonance imaging (MRI). Method The study was conducted at a tertiary care center, with data sourced from patients visiting the outpatientand in-patient departments. It involved a descriptive cross-sectional research design focusing on patients referred for knee MRI scanning. The sample size was calculated using Cochran's formula as 112 for symptomatic patients with knee pain with a 95% confidence interval. The MRI findings in 112 patients were analyzed and associated with a history of trauma. Results The average age recorded was 35.38 years. Females made up 41.07% (n=46) of the sample, while males accounted for 58.93% (n=66).Among the participants, the majority (n=82; 71.43%) had a history of trauma, and the most common MRI finding wasjoint effusion (n=74; 66.1%). The second most common was anterior cruciate ligament (ACL) injuries (n=71; 63.4%), followed bymeniscus injury (n=40; 35.71%). The study confirms that those with history oftrauma are at a higher risk (p<0.05) of sustaining injuries like meniscus and ACL tears, collateral ligament damage, bone contusions, chondromalacia patella, and joint effusion. Conclusion In conclusion, the consistency of our findings with existing studies reinforces the pivotal role of MRI in the evaluation of knee pain. Despite its limitations, including cost and accessibility, MRI remains a gold standard for diagnosing a wide range of knee pathologies, offering unparalleled detail and accuracy that significantly enhance clinical decision-making and patient outcomes.

Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning

Our main objective was to use machine learning methods to identify significant structural factors associated with pain severity in knee osteoarthritis patients. Additionally, we assessed the potential of various classes of imaging data using machine learning techniques to gauge knee pain severity. The data of semi-quantitative assessments of knee radiographs, semi-quantitative assessments of knee magnetic resonance imaging (MRI), and MRI images from 567 individuals in the Osteoarthritis Initiative (OAI) were utilized to train a series of machine learning models. Models were constructed using five machine learning methods: random forests (RF), support vector machines (SVM), logistic regression (LR), decision tree (DT), and Bayesian (Bayes). Employing tenfold cross-validation, we selected the best-performing models based on the area under the curve (AUC). The study results indicate no significant difference in performance among models using different imaging data. Subsequently, we employed a convolutional neural network (CNN) to extract features from magnetic resonance imaging (MRI), and class activation mapping (CAM) was utilized to generate saliency maps, highlighting regions associated with knee pain severity. A radiologist reviewed the images, identifying specific lesions colocalized with the CAM. The review of 421 knees revealed that effusion/synovitis (30.9%) and cartilage loss (30.6%) were the most frequent abnormalities associated with pain severity. Our study suggests cartilage loss and synovitis/effusion lesions as significant structural factors affecting pain severity in patients with knee osteoarthritis. Furthermore, our study highlights the potential of machine learning for assessing knee pain severity using radiographs.

Exploring chat generated pre-trained transformer-3 ability to interpret MRI knee images and generate reports

Objectives: The study’s objective was to determine if Chat Generated Pre-Trained Transformer-3 (ChatGPT)-4V can interpret magnetic resonance imaging (MRI) knees and generate preliminary reports based on images and clinical history provided by the radiologist. Materials and Methods: This cross-sectional observational study involved selecting 10 MRI knees with representative imaging findings from the institution’s radiology reporting database. Key MRI images were then input into the ChatGPT-4V model, which was queried with four questions: (i) What does the image show?; (ii) What is the sequence?; (iii) What is the key finding?; and, (iv) Finally, the model generated a report based on the provided clinical history and key finding. Responses from ChatGPT-4 were documented and independently evaluated by two musculoskeletal radiologists through Likert scoring. Results: The mean scores for various questions in the assessment were as follows: 2 for “What does the image show?,” 2.10 for “What is the sequence?,” 1.15 for “What is the key finding?,” and the highest mean score of 4.10 for the command “Write a report of MRI of the…” Radiologists consistently gave mean scores ranging from 2.0 to 2.5 per case, with no significant differences observed between different cases (P &gt; 0.05). The interclass correlation coefficient between the two raters was 0.92 (95% Confidence interval: 0.85–0.96). Conclusion: ChatGPT-4V excelled in generating reports based on user-fed clinical information and key findings, with a mean score of 4.10 (good to excellent proficiency). However, its performance in interpreting medical images was subpar, scoring ≤2.10. ChatGPT-4V, as of now, cannot interpret medical images accurately and generate reports.

Deep Learning Reconstructed New-Generation 0.55 T MRI of the Knee—A Prospective Comparison With Conventional 3 T MRI

Objectives The aim of this study was to compare deep learning reconstructed (DLR) 0.55 T magnetic resonance imaging (MRI) quality, identification, and grading of structural anomalies and reader confidence levels with conventional 3 T knee MRI in patients with knee pain following trauma. Materials and Methods This prospective study of 26 symptomatic patients (5 women) includes 52 paired DLR 0.55 T and conventional 3 T MRI examinations obtained in 1 setting. A novel, commercially available DLR algorithm was employed for 0.55 T image reconstruction. Four board-certified radiologists reviewed all images independently and graded image quality, noted structural anomalies and their respective reporting confidence levels for the presence or absence, as well as grading of bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared (P &lt; 0.05, significant), and MRI findings were correlated between 0.55 T and 3 T MRI using Cohen kappa (κ). Results In reader's consensus, good image quality was found for DLR 0.55 T MRI and 3 T MRI (3.8 vs 4.1/5 points, P = 0.06). There was near-perfect agreement between 0.55 T DLR and 3 T MRI regarding the identification of structural anomalies for all readers (each κ ≥ 0.80). Substantial to near-perfection agreement between 0.55 T and 3 T MRI was reported for grading of cartilage (κ = 0.65–0.86) and meniscus lesions (κ = 0.71–1.0). High confidence levels were found for all readers for DLR 0.55 T and 3 T MRI, with 3 readers showing higher confidence levels for reporting cartilage lesions on 3 T MRI. Conclusions In conclusion, new-generation 0.55 T DLR MRI provides good image quality, comparable to conventional 3 T MRI, and allows for reliable identification of internal derangement of the knee with high reader confidence.

A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis.

This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs). This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears. This study's findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears-whether in the anterior or posterior horn-with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94. Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool.

#2698 Regression of amyloid deposits after treatment with tocilizumab

Abstract Background and Aims AA amyloidosis is a multisystem disease characterized by the deposition of serum amyloid A protein, which is secondary to chronic inflammation. Tocilizumab (an interleukin-6 inhibitor monoclonal antibody) is effective in suppressing inflammation, normalizing serum amyloid A protein levels and inducing remission in patients with amyloidosis. Recently, tocilizumab treatment has been associated with the disappearance of amyloid deposits. Results We report a case of AA amyloidosis secondary to chronic arthritis in a 61-year-old woman referred to our hospital in 2011 due to oligoarthritis of the knees and elevation of acute-phase reactants. Corticosteroids and methotrexate were prescribed, without clinical response. Two years later, the patient presented with foamy urine, nocturia, sweating and dizziness. An elevated C-reactive protein (CRP), erythrocyte sedimentation rate and nephrotic-range proteinuria were found. Rectal and oral mucosa biopsies were performed and amyloid AA deposits were detected in both (Fig. 1A, B). Magnetic resonance imaging (MRI) of the right knee showed arthropathy due to amyloid deposition (Fig. 2). Intravenous monthly tocilizumab was prescribed with rapid improvement of CRP, proteinuria (Fig. 3) and nephrotic syndrome symptoms. Arthritis also improved significantly. Two years later, a new biopsy of the rectal mucosa did not show amyloid deposits (Fig. 1C, D) and right knee MRI was normal, without evidence of amyloid synovitis (Fig. 4). Eight years later, the patient continues treatment with tocilizumab and remains clinically stable, with no evidence of recurrence. Literature review: The efficacy of tocilizumab has been described mainly in patients with rheumatoid arthritis, but also in AA amyloidosis secondary to systemic juvenile idiopathic arthritis, familial Mediterranean fever, psoriatic arthritis, ankylosing spondylitis, Still's disease, Muckle-Wells syndrome, Behçet syndrome, polyarteritis nodosa and Takayasu arteritis. Some of the studies report long-term effectiveness of up to 9 years. In 11 case reports of patients with AA amyloidosis treated with tocilizumab, a follow-up biopsy was performed and a reduction or disappearance of the deposits was also observed. Lane et al. analyzed pre- and on-treatment serial scintigraphy scans in 20 patients with AA amyloidosis, showing that AA amyloid deposits either regressed or did not increase whilst on tocilizumab. We have not identified any study demonstrating the disappearance of amyloid arthropathy in MR images. Conclusion Tocilizumab treatment controls chronic inflammatory disease and improves symptoms of AA amyloidosis. Long-term treatment with tocilizumab may remove amyloid deposits from tissues, leading to a definitive cure of this disease.

One-stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation.

Anterior cruciate ligament (ACL) injuries are closely associated with knee osteoarthritis (OA). However, diagnosing ACL injuries based on knee magnetic resonance imaging (MRI) has been subjective and time-consuming for clinical doctors. Therefore, we aimed to devise a deep learning (DL) model leveraging MRI to enable a comprehensive and automated approach for the detection of ACL injuries. A retrospective study was performed extracting data from the Osteoarthritis Initiative (OAI). A total of 1,589 knees (comprising 1,443 intact, 90 with partial tears, and 56 with full tears) were enrolled to construct the classification model. This one-stop detection pipeline was developed using a tailored YOLOv5m architecture and a ResNet-18 convolutional neural network (CNN) to facilitate tasks based on sagittal 2-dimensional (2D) intermediate-weighted fast spin-echo sequence at 3.0T. To ensure the reliability and robustness of the classification system, it was subjected to external validation across 3 distinct datasets. The accuracy, sensitivity, specificity, and the mean average precision (mAP) were utilized as the evaluation metric for the model performance by employing a 5-fold cross-validation approach. The radiologist's interpretations were employed as the reference for conducting the evaluation. The localization model demonstrated an accuracy of 0.89 and a sensitivity of 0.93, achieving a mAP score of 0.96. The classification model demonstrated strong performance in detecting intact, partial tears, and full tears at the optimal threshold on the internal dataset, with sensitivities of 0.941, 0.833, and 0.929, specificities of 0.925, 0.947, and 0.991, and accuracies of 0.940, 0.941, and 0.989, respectively. In comparison, on a subset consisting of 171 randomly selected knees from the OAI, the radiologists demonstrated a sensitivity ranging between 0.660 and 1.000, specificity ranging between 0.691 and 1.000, and accuracy ranging between 0.689 and 1.000. On a subset consisting of 170 randomly selected knees from the Chinese dataset, the radiologists exhibited a sensitivity ranging between 0.711 and 0.948, specificity ranging between 0.768 and 0.977, and accuracy ranging between 0.683 and 0.917. After retraining, the model achieved sensitivities ranging between 0.630 and 0.961, specificities ranging between 0.860 and 0.961, and accuracies ranging between 0.832 and 0.951, respectively, on the external validation dataset. The proposed model utilizing knee MRI showcases robust performance in the domains of ACL localization and classification.

Correlating quadriceps patellar tendon angle and lateral patellar tilt angle in patients with irregular alignment: a cross-sectional study with retrospective data.

The newly defined angle, quadriceps-patella angle (QPA), reflects the combined force transmitted to the patella by the quadriceps muscles and patellar tendon. An increase in QPA may correlate with an increased force on the patella, which is significant in diagnosing patellofemoral instability and pain syndrome. In our study, we examined how various angles and pathologies vary depending on lateral patellar tilt angle (LPTA). QPA and patellar malalignment was investigated. Thus, the importance of understanding patellar malalignment and the research gap. Three hundred and fifty patients who underwent knee magnetic resonance imaging (MRI) examinations were included. The cross-sectional study conducted retrospectively between the years of 2018-2020 in a tertiary care outpatient clinic. Shapiro-Wilk normality, Chi-square, Mann-Whitney-U, Spearman correlation and receiver operating characteristic (ROC) curve analysis, statistical tests used for analysis. The patellar tendon length, patellar height, tibial tubercle-trochlear groove distance (TT-TG), patella angle, trochlear sulcus angle, trochlear groove depth (TGD), medial trochlea length (MT), lateral trochlea length (LT), medial trochlear/lateral trochlear length ratio (MT/LT), LPTA, patella-patellar tendon angle (PPTA), QPA, Insall-Salvati index (ISI), medial trochlear inclination (MTI), lateral trochlear inclination (LTI) were among these measurements. In addition, we aim to reveal whether there is a significant relationship between two important angles LPTA and QPA. Whether there is a significant increase in the development of chondromalacia for the patient group with LPTA >5°. We examined how the frequency of chondromalacia changes in the patient group with LPTA >5°. Two hundred and seventy seven patients included in the study and many measurements were performed on MRI. Fad-pad edema was found to be significantly higher in the group with LPTA <5° (P=0.046). TT-TG distance was significantly higher, TGD and MT were significantly lower in patients with higher LPTA (P=0.001, P=0.002 and P=0.017, respectively). A low level of significant positive correlation was found between QPA and patellar tendon length. There is no significant difference between QPA and PPTA angles between the groups with LPTA <5° and >5° (P=0.503, P=0.188). In the ROC analysis performed to determine the cut-off value, the LPTA value ≤14.2° which significantly predicted the presence of fad-pad edema, had the highest sensitivity and specificity [sensitivity: 76.71%, specificity: 39.90%, area under the curve (AUC): 0.588, P=0.024]. QPA is independent from many angles of the knee and does not change significantly. As the patellar tendon length increases, QPA angle also increases. In patients with abnormal LPTA, the frequency of TT-TG distance and chondromalacia increased, while TGD and MT decreased. Patients with a low LPTA can be more carefully examined for chondromalacia and fad-pad edema in clinical and MRI examination.

A comprehensive MRI investigation to identify potential biomarkers of Osgood Schlatter disease in adolescents: A cross sectional study comparing Osgood Schlatter disease with controls.

Osgood-Schlatter disease (OSD) is the most common knee pain complaintamong adolescents playing sports. Despite this, there remains controversy over the pathophysiology and whether specific anatomical characteristics are associated with OSD. This study aimed to systematically and comprehensively characterize adolescents with OSD using magnetic resonance imaging (MRI) compared to pain-free controls, including both tissue abnormalities that may be associated with OSD, as well as anatomical characteristics. A secondary objective was to identify potential imaging biomarkers associated with pain. Cross-sectional study. Adolescents with OSD and controls were recruited from 2020 to 2022. Following a clinical exam, demographics, pain, sports participation, and Tanner stage were collected. Knee MRI was conducted on the participants' most symptomatic knee (OSD) or the dominant leg (controls). Sixty-seven adolescents (46 with OSD and 30 controls) were included. 80% of participants with OSD had at least one tissue alteration compared to 54% of controls. Compared to controls, OSD had 36.3 (95%CI 4.5 to 289.7) higher odds of bony oedema at the tibial tuberosity, and 32.7 (95%CI 4.1 to 260.6) and 5.3 (95%CI 0.6 to 46.2) higher odds of bony oedema at the tibial epiphysis and metaphysis respectively. Participants with OSD also had higher odds of fluid/oedema at the patellar tendon (12.3 95%CI 3.3 to 46.6), and superficial infrapatellar bursitis (7.2). Participants with OSD had a more proximal tendon attachment (mean tibial attachment portion difference, -0.05, 95% CI: -0.1 to 0.0, p = 0.02), tendon thickness (proximal mean difference, -0.09, 95% CI: -0.4 to 0.2, p = 0.04; distal mean difference, -0.6, 95% CI: -0.9 to -0.2, p = 0.01). Those with bony/tendon oedema had 1.8 points (95% CI: 0.3 to 3.2) higher pain on palpation than those without (t = -2.5, df = 26.6, p = 0.019), but there was no difference between these groups in a functional single leg pain provocation. Adolescents with OSD present with tissue and structural abnormalities on MRI that differed from age-matched controls. The majority had findings in the patellar tendon and bone, which often co-occurred. However, a small proportion of OSD also presents without alterations. It appears these findings may be associated with clinical OSD-related pain on palpation of the tibial tuberosity. Our highlight the pathophysiology on imaging, which has implications for understanding the mechanism and treatment of OSD.

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

In the current research study, a new method is presented to diagnose Anterior Cruciate Ligament (ACL) tears by introducing an optimized version of the InceptionV4 model. Our proposed methodology utilizes a custom-made variant of the Snow Leopard Optimization Algorithm, known as the Fractional-order Snow Leopard Optimization Algorithm (FO-LOA), to extract essential features from knee magnetic resonance imaging (MRI) images. This results in a substantial improvement in the accuracy of ACL tear detection. By effectively extracting critical features from knee MRI images, our proposed methodology significantly enhances diagnostic accuracy, potentially reducing false negatives and false positives. The enhanced model based on FO-LOA underwent thorough testing using the MRNet dataset, demonstrating exceptional performance metrics including an accuracy rate of 98.00%, sensitivity of 98.00%, precision of 97.00%, specificity of 98.00%, F1-score of 98.00%, and Matthews Correlation Coefficient (MCC) of 88.00%. These findings surpass current methodologies like Convolutional Neural Network (CNN), Inception-v3, Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA), integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO), Self-Supervised Representation Learning (SSRL), signifying a significant breakthrough in ACL injury diagnosis. Using FO-SLO to optimize the InceptionV4 framework shows promise in improving the accuracy of ACL tear identification, enabling prompt and efficient treatment interventions.

Tumor-Like Distal Femoral Cortical Irregularities of the Knee in Adolescent Competitive Alpine Skiers: Longitudinal Assessment Over 48 Months

Background: Tumor-like distal femoral cortical irregularities (DFCIs) are a frequent incidental finding on knee magnetic resonance imaging (MRI) and are common in young competitive athletes. Purpose: To assess and compare the morphology and prevalence of DFCIs in competitive alpine skiers over 48 months during adolescence. Study Design: Case series; Level of evidence, 4. Methods: Adolescent competitive alpine skiers were prospectively recruited in 2018 and received bilateral 3-T MRI of the knee at baseline and after 48 months. All MRIs were evaluated for the presence and location of DFCIs, which were marked at 1 of 3 anatomic positions: (1) the femoral attachment of the medial head of the gastrocnemius muscle, (2) the lateral head of the gastrocnemius muscle, or (3) the attachment of the adductor magnus aponeurosis. The size of the DFCI was measured by 2 radiologists independently. The measurements were compared using the Wilcoxon signed-rank test, the interclass correlation coefficient (ICC), and Cohen Kappa. Results: A total of 63 athletes (mean age at follow-up, 19.6 ± 1.2 years; n = 25 female) were included in the study. At baseline, DFCIs were detected in 84 out of 126 knees (67%). At the 48-month follow-up, DFCIs were found in 88 out of 126 knees (70%), with multiple DFCIs in 3 knees and no significant difference between male and female patients (n = 24 male, n = 19 female; P = .71). No significant increase was detected for the number (P = .21) and size of the DFCIs between the baseline and the 48-month follow-up (mean size: baseline, 3.7 ± 0.8 mm; 48-month follow-up: 3.6 ± 0.9 mm; P = .66). The interrater agreement for the mean size measurements of DFCIs was good to excellent (ICC 0.88). Conclusion: DFCIs remain a frequent finding on knee MRI in competitive alpine skiers after skeletal maturation and do not disappear during adolescence. The DFCI size was constant in athletes aged between 15 and 19 years. Moreover, DFCIs should not be mistaken for a pathologic finding.

Measuring synovial thickness on knee MRI in pediatric patients with arthritis: is contrast necessary?

The use of contrast-enhanced imaging has long been standard for magnetic resonance imaging (MRI) assessments of synovitis in juvenile idiopathic arthritis (JIA). However, advancements in MRI technology have allowed for reliable identification of synovium without contrast. To assess the equivalence of unenhanced MRI with contrast-enhanced MRI in evaluating synovial thickness. This is an institutional review board approved, retrospective study performed in a tertiary children's hospital. Pediatric JIA patients under 21 years old were included who underwent knee MRI scans (1.5 T or 3 T) without and with contrast between January 2012 and January 2022. Two radiologists independently measured synovial thickness at 6 knee sites on contrast-enhanced and unenhanced sequences. Numerical measurements and ordinal scores based on juvenile idiopathic arthritis magnetic resonance imaging scoring (JAMRIS) system were recorded, and tests of equivalence were conducted, as well as between-reader and within-reader reliability by concordance correlation coefficient (CCC). All tests were considered significant at the 5% level. A total of 38 studies from 35 patients (25 females, median age 14 years; interquartile range 7 to 15.7) were included. Equivalence was demonstrated at each of the 6 sites for both continuous measurements (P-values < 0.05) and ordinal scores (P-values < 0.05) based on the average over readers. Within-reader reliability was moderate to high (CCC 0.50-0.89), except for the cruciate ligaments site. Averaged over the 6 sites, reliability between readers was low for unenhanced (CCC 0.47, with 95% CI: [0.41, 0.53]) and moderate for contrast-enhanced (CCC 0.64, with 95% CI: [0.59, 0.69]) sequences. Unenhanced knee MRI is equivalent to contrast-enhanced MRI in assessment of synovial thickness using conventional MRI sequences. Contrast material helped improve inter-reader reliability.