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Abstract Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other outcomes, especially when impacted by co-morbidities such as HIV infection. Brain magnetic resonance imaging (MRI) characterises the extent and severity of disease and may enable more accurate prediction of complications and poor outcomes. We analysed clinical and brain MRI data from a prospective longitudinal study of 216 adults with TBM; 73 (34%) were HIV-positive, a factor highly correlated with mortality. We implemented an end-to-end framework to model clinical and imaging features to predict disease progression. Our model used state-of-the-art machine learning models for automatic imaging feature encoding, and time-series models for forecasting, to predict TBM progression. The proposed approach is designed to be robust to missing data via a novel tailored model optimisation framework. Our model achieved a 60% balanced accuracy in predicting the prognosis of TBM patients over the six different classes. HIV status did not alter the performance of the models. Furthermore, our approach identified brain morphological lesions caused by TBM in both HIV and non-HIV-infected, associating lesions to the disease staging with an overall accuracy of 96%. These results suggest that the lesions caused by TBM are analogous in both populations, regardless of the severity of the disease. Lastly, our models correctly identified changes in disease symptomatology and severity in 80% of the cases. Our approach is the first attempt at predicting the prognosis of TBM by combining imaging and clinical data, via a machine learning model. The approach has the potential to accurately predict disease progression and enable timely clinical intervention.

Lipoma arborescens is a rare and benign intra-articular lesion characterized by a lipomatous proliferation of the synovial membrane, usually affects the knee joint. It presents as an unusual cause of intermittent knee pain and joint effusion.We report a case of lipoma arborescens of the knee in a 23-year-old man that initially resembled inflammatory arthropathy. The diagnosis of Lipoma arborescens was made by magnetic resonance imaging of the knee and confirmed histologically by synovectomy.The purpose of our case is to show the imaging features enabling early diagnosis and appropriate treatment.

Abstract Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model’s performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model’s efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

Abstract Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

Classification of different brain tumors is challenging due to unpredictable variations in intra-inter-classes. Unlike existing methods which are not effective for images of complex backgrounds, the proposed work aims at accurate classification of diverse types of brain tumors such that an appropriate model can be used for disease identification. This study considers glioma, meningioma, no tumor, and pituitary tumors for classification. To achieve an accurate classification, we explore the Xception architecture layer, which involves flattening, dropout, and dense layer operations. The model extracts features based on shapes, spatial relationships, and structure of the image, discriminating between the different brain tumor images. The model is evaluated on a dataset of 7023 MRI images for classification. The results of a large dataset and comparative study with the existing methods show that the proposed method is better than state of the art in terms of classification rate. Specifically, our method achieves more than a 90% average classification rate, which is better than state of the art. The results on noisy and blurred datasets show that the proposed model is robust to noise and blur. [ABSTRACT FROM AUTHOR]

Background: The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective: This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods: The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results: The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion: This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods. [ABSTRACT FROM AUTHOR]

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists' scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists' ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists' scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists' scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland–Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform's low inter-user variability and the high accuracy of its T1c and FLAIR AI models. [ABSTRACT FROM AUTHOR]

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images. [ABSTRACT FROM AUTHOR]

Objective: To synthesize tumor acidic microenvironment-responsive Fe-MOF nanoprobe and investigate its synergistic therapeutic effect of ferroptosis-photodynamic therapy for breast cancer and in vitro MR T1 activation effect. Methods: Fe-MOF nanoprobes were prepared and characterized by transmission electron microscope (TEM) and atomic force microscopy (AFM); Assess its reactive oxygen specis (ROS) generation capability and GSH depletion ability at the solution level using 3,3',5,5'-Tetramethylbenzidine (TMB) and 5,5'-Dithiobis(2-nitrobenzoic acid) (DTNB); The MTT assay was used to determine the cytotoxicity of the nanoprobe on 4T1 breast cancer cells under dark and light conditions, so as to evaluate synergistic therapeutic effect of ferroptosis-photodynamic therapy; After the 4T1 cells were co-incubated with Fe-MOF and treated with laser irradiation, the generation of ROS, lipid peroxide (LPO) and cell live/dead staining were observed using the fluorescence microscope; Observe the activation effect of Fe-MOF in T1-weighted imaging under different pH conditions, and measure the T1 activation efficiency at different time points at the cellular level. Results: The prepared Fe-MOF nanoprobes exhibit a needle-like structure, with a thickness of approximately 44 nm; Fe-MOF can effectively promote the generation of ROS and the consumption of LPO at the solution level; Fluorescence microscope results show that ferroptosis effect triggered by Fe-MOF combined with photodynamic therapy can effectively promote the generation of intracellular ROS and LPO and promote an increase in the death rate of tumor cells (P<0.001); MR imaging results show that the T1 signal of Fe-MOF can be specifically activated under acidic conditions and the r1 relaxation rate under pH 5.0 is 4.954 mM-1s-1. It has good pH responsiveness at the solution level and time-dependent activation efficiency at the cellular level. Conclusion: The pH-responsive diagnostic and therapeutic integrated nanoprobe, Fe-MOF, is capable of achieving synergistic therapeutic efficacy through ferroptosis-photodynamic treatment and magnetic resonance T1 contrast effect. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2). [ABSTRACT FROM AUTHOR]

Background: Bacterial meningitis is an infectious condition associated with high rates of morbidity and mortality. Streptococcus pneumoniae is the most common cause of bacterial meningitis in adults. It is a highly fatal disease that often leaves survivors with serious neurologic damage if left untreated. Cerebral vasculitis is a rare but recognized complication of bacterial meningitis as seen in the literature. Patient and methods: We report a 55-year-old woman diagnosed with pneumococcal meningitis who developed cerebral vasculitis during her stay at the hospital. It was confirmed by cerebral magnetic resonance imaging (MRI) that showed multifocal ischemic lesions of the brain and the patient’s neurologic symptoms resolved after the administration of corticosteroids and the patient made a full recovery. In addition to MRI, many investigations were done to rule out other possible causes of these lesions and confirm cerebral vasculitis post pneumococcal meningitis as a diagnosis in this case. Conclusion: Cerebral vasculititis should be considered in patients who deteriorate on treatment, with MRI remaining the mainstay of diagnosis. However, with the absence of a specific MRI sequence that could confirm the diagnosis of vasculitis, it remains a diagnosis of exclusion. Some patients experience rapid onset of neurological deterioration shortly after meningitis, while others show delayed symptoms. Steroids are found to be the mainstay of treatment for vasculitis in the majority of cases. More research to determine the dosage, timing and duration is required.

BACKGROUND: Epidural steroid injections are common procedures used to treat lumbosacral radicular pain due to lumbar disc herniation (LDH). It is crucial for the clinician to anticipate which patients can benefit from interventional treatment options. OBJECTIVE: This study aimed to examine the effect of radiological and clinical parameters on lumbar transforaminal epidural steroid injections (TFESI)/local anesthetic injection outcomes in patients with LDH. METHODS: This study included 286 patients with LDH (146 males and 140 females). All patients received a fluoroscopically guided TFESI (triamcinolone acetonide 40 mg, lidocaine 2%, and 2.5 ml of physiological saline). Patients were evaluated according to radicular pain, the Oswestry Disability Index (ODI) and the Hospital Anxiety and Depression Scale at baseline and 3 months after the injections. Demographic, clinical and magnetic resonance imaging (MRI) findings were recorded to assess the predictive factors for TFESI outcomes. Pfirrmann Grades 1 and 2 were classified as low-grade nerve root compression and Grade 3 was classified as highgrade nerve root compression. RESULTS: Compared to baseline measurements there were significant improvements in radicular pain, ODI score, Laseque angle, and Schober test scores 3 months after injection. Improvements of at least 50% in radicular pain relief and the ODI functionality index were (n = 214) 82%, (n = 182) 70% respectively at 3 months. Correlation analyses revealed that a shorter duration of symptoms, lowgrade nerve root compression and foraminal/extraforaminal location on MRI findings were associated with a favorable response. CONCLUSIONS: Lowgrade nerve root compression was a predictor of a favorable response to TFESI. [ABSTRACT FROM AUTHOR]

Introduction: Magnetic resonance imaging (MRI) staging scans are critical for the diagnosis and treatment of patients with nasopharyngeal cancer (NPC). We aimed to evaluate the application value of LAVA-Flex and T1WI-IDEAL sequences in MRI staging scans. Methods: Eighty-four newly diagnosed NPC patients underwent both LAVA-Flex and T1WI-IDEAL sequences during MRI examinations. Two radiologists independently scored the acquisitions of image quality, fat suppression quality, artifacts, vascular and nerve display. The obtained scores were compared using the Wilcoxon signed rank test. According to the signal intensity (SI) measurements, the uniformity of fat suppression, contrast between tumor lesions and subcutaneous fat tissue, and signal-to-noise ratio (SNR) were compared by the paired t-test. Results: Compared to the T1WI-IDEAL sequence, LAVA-Flex exhibited fewer artifacts (P<0.05), better visualization of nerves and vessels (P<0.05), and performed superior in the fat contrast ratio of the primary lesion and metastatic lymph nodes (0.80 vs. 0.52, 0.81 vs. 0.56, separately, P<0.001). There was no statistically significant difference in overall image quality, tumor signal-to-noise ratio (SNR), muscle SNR, and the detection rate of lesions between the two sequences (P>0.05). T1WI-IDEAL was superior to LAVA-Flex in the evaluation of fat suppression uniformity (P<0.05). Discussion: LAVA-Flex sequence provides satisfactory image quality and better visualization of nerves and vessels for NPC with shorter scanning times. [ABSTRACT FROM AUTHOR]

Calcific tendinitis is classically a painful condition that most commonly affects the rotator cuff, but may infrequently involve other tendons. We discuss a 57-year-old man who presented to the emergency department with a 4-day history of right hip pain, described as the "worst pain in (his) life." The pain was first noticed at night and had progressively worsened. History, physical examination, and initial laboratory workup indicated an inflammatory vs. infectious process. Continued investigations with imaging techniques revealed the source of pain as calcific tendinitis involving the gluteus maximus tendon. Symptoms of musculoskeletal pain in the emergency department are ubiquitous. In the proper clinical context, the diagnosis of calcific tendinitis, although uncommon, should be considered once emergent conditions are ruled out. Proper imaging techniques will facilitate accurate diagnosis, expedited pain management, and proper outpatient follow-up. [ABSTRACT FROM AUTHOR]

Objective: To investigate the MRI imaging of glioblastoma multiforme with nanoparticles biomimetic to macrophage cell membrane (Fe3O4 NCs@MM). Results: The nanoparticles with macrophage cell membrane (Fe3O4 NCs@MM) were prepared and characterized by Dynamic Light Scattering (DLS) and Transmission Electron Microscope (TEM). Sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) was used to evaluate the complete coating of extracted macrophage cell membranes. The anti-protein adsorption ability of nanoparticles with macrophage cell membrane was determined by UV-VIS spectroscopy. The T1 relaxation effects of Fe3O4 NCs@MM with Fe at different concentrations (0.1-1.6 mM) in the presence or absence of GSH were analyzed by MRI. Cell Counting Kit-8 (CCK-8) was used to determine the cell activity of tumor cells treated with macrophage cell membrane bionic nanoparticles for 24 h. Glioblastoma-bearing mice were injected with macrophage cell membrane biomimetic nanoparticles and scanned by MRI. Results: The hydration kinetic particle size and surface potential of macrophage cell membrane biomimetic nanoparticle (Fe3O4 NCs@MM) were 286.5± 7.6 nm and -20.7± 3.5 mV respectively, with good monodispersion. Nanoparticles coated with macrophage cell membrane can successfully resist protein adsorption. MRI imaging showed that the prepared macrophage membrane bionic nanoparticles (Fe3O4NCs@MM) were considered as GSH-responsive MRI contrast agents for T1-weighted MRI imaging. The enhanced signal at the tumor site can be observed at 0.5 h post-injection of Fe3O4 NCs @MM. Conclusion: MRI imaging of glioblastoma multiforme can be achieved by biomimetic nanoparticles of macrophage cell membrane (Fe3O4 NCs@MM). [ABSTRACT FROM AUTHOR]

BackgroundThe necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error.ObjectiveThis research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans.MethodsThe dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification.ResultsThe proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications.ConclusionThis study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

ABSTRACT: Background: Low back pain (LBP) is the most frequent indication to magnetic resonance imaging (MRI) examinations of the lumbosacral spine. The individual role of soft tissues, including muscles, on LBP is not fully understood and the contribution of each MRI-derived parameter of soft tissues status on the intensity of LBP has not been investigated in detail. Methods: The study design was observational retrospective, single center carried out at a University Hospital. Images were acquired using a using a 1.5 Tesla scanner. Patients completed a symptom questionnaire and rated their pain intensity using the Visual Analogue Scale (VAS). The VAS scores ​​were categorized as mild, moderate, and severe using cutoff values of 3.8 and 5.7, based on the literature. Biometric data, including weight and height, were also recorded to calculate the body mass index (BMI). The ratios between intramuscular fat infiltration and net muscle area were also calculated. Patient sample included 94 patients with LBP underwent MRI of the lumbosacral spine. Results: The stepwise analysis revealed that increasing psoas net area was associated with lower VAS levels (odds ratio [OR]: 0.94: 95% confidence interval [CI]: 0.90–0.98; p=.005), and an increase of one square centimeter of total psoas area resulted in a greater probability of reporting a mild (+1.21%; 95% CI: 0.37, 2.05%) or a moderate VAS (+0.40%; 95% CI: -0.02, 0.82%), Furthermore, a more severe VAS was associated with a higher BMI (OR: 1.13; 95% CI: 1.00–1.27). Conclusion: Our study demonstrates a relationship between LBP and MRI parameters of paravertebral and psoas muscles status. The psoas muscle is extremely important for spine stabilization and is linked to clinical symptoms of patients affected by LBP. These findings could contribute to future studies and improve treatment options in patients with LBP, possibly reducing the impact on disability, quality of life and socioeconomical burden.

Background: Glomus is uncommon benign tumours presenting as a form of chronic pain syndrome that is often disabling with resultant poor quality of life. The lack of an imaging protocol further impedes timely diagnosis, especially when presenting atypically. The objective of this study aims to reflect on the efficacy of imaging modalities based on strong clinical suspicion for glomus tumours. Methods: This is an observational retrospective study recorded over a decade in 23 patients who presented with neuropathic pain and symptoms suspicious of glomus tumours to the outpatient departments of neurology and plastic surgery of a single hospital centre with quaternary level of health care. Results: Twenty-two patients had presented with the classical symptoms of pain and pinpoint tenderness while 7 had temperature sensitivity. Imaging studies were done in 19 patients. There was preponderance of thumb followed by the middle finger with regard to presenting site, the mean age of the patient was close to 39 years and mean duration of presentation was approximately 3.5 years. A rare case of disuse atrophy associated with the presence of glomus tumour in the midsole of the left foot in one of our patients diagnosed solely through MRI of the foot is detailed as well in this study. Conclusions: We emphasise on the need for a high index of clinical suspicion and vigilance aided by ultrasound and MRI when pain and disability are almost always disproportionate to the size of the affected area. Prompt diagnosis leads to early surgical excision providing relief. Level of evidence: Level IV, Diagnostic [ABSTRACT FROM AUTHOR]

Aortic stenosis (AS) is a critical cardiovascular condition that necessitates precise diagnosis for effective patient care. Despite a limited dataset comprising only 202 images, our study employs transfer learning to investigate the efficacy of five convolutional neural network (CNN) models, coupled with advanced computer vision techniques, in accurately classifying AS. The VGG16 model stands out among the tested models, achieving 95% recall and F1-score. To fortify the model's robustness and generalization, we implement various data augmentation techniques, including translation, rotation, flip, and brightness adjustment. These techniques aim to capture real-world image variations encountered in clinical settings. Validation, conducted using authentic data from Hospital Santa Maria, not only affirms the clinical applicability of our model but also highlights the potential to develop robust models with a limited number of images. The models undergo training after the images undergo a series of computer vision and data augmentation techniques, as detailed in this paper. These techniques augment the size of our dataset, contributing to improved model performance. In conclusion, our study illuminates the potential of AI-driven AS detection in MRI scans. The integration of transfer learning, CNN models, and data augmentation yields high accuracy rates, even with a small dataset, as validated in real clinical cases. [ABSTRACT FROM AUTHOR]

Background: Transverse myelitis (TM) is a rare, acquired neuro-immunological spinal cord disorder that occurs with rapid onset of motor weakness, sensory deficits with bowel and bladder dysfunction. Patients being treated with immune checkpoint inhibitors (ICIs) for advanced malignancy have a known higher propensity of developing neuro immune complications. With the advent of COVID-19 pandemic there have been reported cases of TM with COVID-19 immunization. The reported infrequency of TM with both of the aforementioned causes makes delineation of the etiology challenging. Methods: We present a patient with metastatic small cell lung cancer (SCLC) on maintenance Atezolizumab immunotherapy who developed longitudinal extensive transverse myelitis (LETM) after administration of second dose of COVID-19 mRNA vaccine one day prior to presenting symptoms of acute paralysis of the lower extremity, sensory loss from chest down with overflow incontinence. A clinical diagnosis of myelopathy was supported by MRI of the spine illustrating enhancing lesions from C7–T7 concerning for LETM. Results: A 5-day course of pulsed methylprednisolone followed by therapeutic plasma exchange for 3 days resulted in only minimal improvement in the neurologic exam with increased strength in his lower extremities while the sensory level remained unchanged. Conclusions: This case demonstrates the complication and symptomatology of TM in the setting of anti-PD-L1 monoclonal antibody with coincidental COVID-19 mRNA vaccine administration. The causal relationship between the vaccine and LETM is difficult to establish. However, the presence of a known inciting factor hints at a possible exaggeration of the existing neuro-inflammatory process. [ABSTRACT FROM AUTHOR]

IntroductionMagnetic resonance imaging (MRI) staging scans are critical for the diagnosis and treatment of patients with nasopharyngeal cancer (NPC). We aimed to evaluate the application value of LAVA-Flex and T1WI-IDEAL sequences in MRI staging scans.MethodsEighty-four newly diagnosed NPC patients underwent both LAVA-Flex and T1WI-IDEAL sequences during MRI examinations. Two radiologists independently scored the acquisitions of image quality, fat suppression quality, artifacts, vascular and nerve display. The obtained scores were compared using the Wilcoxon signed rank test. According to the signal intensity (SI) measurements, the uniformity of fat suppression, contrast between tumor lesions and subcutaneous fat tissue, and signal-to-noise ratio (SNR) were compared by the paired t-test.ResultsCompared to the T1WI-IDEAL sequence, LAVA-Flex exhibited fewer artifacts (P0.05). T1WI-IDEAL was superior to LAVA-Flex in the evaluation of fat suppression uniformity (P

Abstract Background Target volume definition for curative radiochemotherapy in head and neck cancer is crucial since the predominant recurrence pattern is local. Additional diagnostic imaging like MRI is increasingly used, yet it is usually hampered by different patient positioning compared to radiotherapy. In this study, we investigated the impact of diagnostic MRI in treatment position for target volume delineation. Methods We prospectively analyzed patients who were suitable and agreed to undergo an MRI in treatment position with immobilization devices prior to radiotherapy planning from 2017 to 2019. Target volume delineation for the primary tumor was first performed using all available information except for the MRI and subsequently with additional consideration of the co-registered MRI. The derived volumes were compared by subjective visual judgment and by quantitative mathematical methods. Results Sixteen patients were included and underwent the planning CT, MRI and subsequent definitive radiochemotherapy. In 69% of the patients, there were visually relevant changes to the gross tumor volume (GTV) by use of the MRI. In 44%, the GTV_MRI would not have been covered completely by the planning target volume (PTV) of the CT-only contour. Yet, median Hausdorff und DSI values did not reflect these differences. The 3-year local control rate was 94%. Conclusions Adding a diagnostic MRI in RT treatment position is feasible and results in relevant changes in target volumes in the majority of patients.

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists’ ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists’ scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists’ scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (

Abstract Background Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. Main text Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. Conclusions The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy. Graphical Abstract

Background: Target volume definition for curative radiochemotherapy in head and neck cancer is crucial since the predominant recurrence pattern is local. Additional diagnostic imaging like MRI is increasingly used, yet it is usually hampered by different patient positioning compared to radiotherapy. In this study, we investigated the impact of diagnostic MRI in treatment position for target volume delineation. Methods: We prospectively analyzed patients who were suitable and agreed to undergo an MRI in treatment position with immobilization devices prior to radiotherapy planning from 2017 to 2019. Target volume delineation for the primary tumor was first performed using all available information except for the MRI and subsequently with additional consideration of the co-registered MRI. The derived volumes were compared by subjective visual judgment and by quantitative mathematical methods. Results: Sixteen patients were included and underwent the planning CT, MRI and subsequent definitive radiochemotherapy. In 69% of the patients, there were visually relevant changes to the gross tumor volume (GTV) by use of the MRI. In 44%, the GTV_MRI would not have been covered completely by the planning target volume (PTV) of the CT-only contour. Yet, median Hausdorff und DSI values did not reflect these differences. The 3-year local control rate was 94%. Conclusions: Adding a diagnostic MRI in RT treatment position is feasible and results in relevant changes in target volumes in the majority of patients. [ABSTRACT FROM AUTHOR]

Vision transformers represent the cutting-edge topic in computer vision and are usually employed on two-dimensional data following a transfer learning approach. In this work, we propose a trained-from-scratch stacking ensemble of 3D-vision transformers to assess prostate cancer aggressiveness from T2-weighted images to help radiologists diagnose this disease without performing a biopsy. We trained 18 3D-vision transformers on T2-weighted axial acquisitions and combined them into two- and three-model stacking ensembles. We defined two metrics for measuring model prediction confidence, and we trained all the ensemble combinations according to a five-fold cross-validation, evaluating their accuracy, confidence in predictions, and calibration. In addition, we optimized the 18 base ViTs and compared the best-performing base and ensemble models by re-training them on a 100-sample bootstrapped training set and evaluating each model on the hold-out test set. We compared the two distributions by calculating the median and the 95% confidence interval and performing a Wilcoxon signed-rank test. The best-performing 3D-vision-transformer stacking ensemble provided state-of-the-art results in terms of area under the receiving operating curve (0.89 [0.61–1]) and exceeded the area under the precision–recall curve of the base model of 22% (p < 0.001). However, it resulted to be less confident in classifying the positive class. [ABSTRACT FROM AUTHOR]

Currently, the death rate of cancer diseases like brain cancer, lung cancer, skin cancer and breast cancer is increasing. To analyze the internal functionality of tumors, there are many modalities like Computer Tomography, Magnetic Resonance Imaging, Ultra Sonic, Mammography and Poisson Emission Tomography. They help radiologists ensure the presence of cancer and reduce the severity by early diagnosis. Our proposed approach extracts the tumor region of multimodal images of breast and brain using Contrast Limited Adaptive Histogram Equalization with Cuckoo Search Optimization. The extracted features are fed to K nearest neighbor classifier to codify the tumor as benign or malignant. Our approach produces 98.4% and 97.6% accuracy for segmentation and classification, respectively. The computational time for segmentation is also comparable with the existing approaches like SOMFCM and PSOFCM and classification with Form feed neural network and SVM. We utilize website like BRATS, Harvard brain dataset and RIDER for validation. [ABSTRACT FROM AUTHOR]

Venolymphatic malformations occur due to dysplasia of lymphatic and venous vessel structures. These tumour-like lesions cause tissue abnormality with impaired function and have aesthetic importance when involving the craniofacial region. Vascular lesions are classified as haemangiomas and other vascular malformations. They are named after the vessels involved in malformation like arterial, venous, lymphatic, or mixed types. Present study represents a rare case report of a 23-year-old patient who presented to our hospital with swelling over the left elbow and a lump over the left breast, which was diagnosed as venolymphatic malformation involving the left breast and left elbow joint, which is an unusually affected anatomical region by this congenital anomaly. Ultrasonography (USG), the primary tool for diagnosis, was performed. Later, the patient underwent Magnetic Resonance Imaging (MRI) for confirmation and to determine the extent of the lesion. Excision of the lesion was done over left elbow completely with a wide margin. Histopathology confirmed the final diagnosis. Venous malformations (VM) are the most common among these congenital malformations, with an incidence of over 50%. Lymphatic malformations (LM) have a much lower incidence than venous malformations, but the combination of venous and LM has a very low incidence.

Alzheimer’s disease is largely the underlying cause of dementia due to its progressive neurodegenerative nature among the elderly. The disease can be divided into five stages: Subjective Memory Concern (SMC), Mild Cognitive Impairment (MCI), Early MCI (EMCI), Late MCI (LMCI), and Alzheimer’s Disease (AD). Alzheimer’s disease is conventionally diagnosed using an MRI scan of the brain. In this research, we propose a fine-tuned convolutional neural network (CNN) classifier called AlzheimerNet, which can identify all five stages of Alzheimer’s disease and the Normal Control (NC) class. The ADNI database’s MRI scan dataset is obtained for use in training and testing the proposed model. To prepare the raw data for analysis, we applied the CLAHE image enhancement method. Data augmentation was used to remedy the unbalanced nature of the dataset and the resultant dataset consisted of 60000 image data on the 6 classes. Initially, five existing models including VGG16, MobileNetV2, AlexNet, ResNet50 and InceptionV3 were trained and tested to achieve test accuracies of 78.84%, 86.85%, 78.87%, 80.98% and 96.31% respectively. Since InceptionV3 provides the highest accuracy, this model is later modified to design the AlzheimerNet using RMSprop optimizer and learning rate 0.00001 to achieve the highest test accuracy of 98.67%. The five pre-trained models and the proposed fine-tuned model were compared in terms of various performance matrices to demonstrate whether the AlzheimerNet model is in fact performing better in classifying and detecting the six classes. An ablation study shows the hyperparameters used in the experiment. The suggested model outperforms the traditional methods for classifying Alzheimer’s disease stages from brain MRI, as measured by a two-tailed Wilcoxon signed-rank test, with a significance of

Abstract Background Dry synovitis (DS) is a rare entity as only a few cases have been reported to date. We describe the clinical features, radiological manifestations and course of DS in comparison with rheumatoid factor negative polyarticular juvenile idiopathic arthritis (RFneg-polyJIA). Methods We performed a multicenter retrospective collection of data of DS patients who presented with progressive joint limitations without palpable synovitis, absence of elevated acute phase reactants, negative ANA and RF, and imaging showing joint and/or osteochondral involvement. For comparative purposes, we included a cohort of RF neg-polyJIA patients. Results Twelve DS patients, 8F/4 M, with mean age at onset of 6.1 years, were included. Presenting signs comprised delayed motor development, functional limitations and/or progressive stiffness. Clinical examination showed symmetric polyarticular involvement with variable muscular atrophy. MRI showed mild, diffuse synovial involvement, without effusion. With time, signs of progressive osteochondral damage became evident, despite treatment. All patients were treated with low-dose corticosteroids and methotrexate. Anti-TNF agents were prescribed in five. The response was variable with limited joint mobility in 11/12, and need of joint replacement in 2. In comparison with a cohort of RFneg-polyJIA, DS patients presented higher number of joint involved (p = 0.0001) and contractures (p = 0.0001), less swelling (p = 0.0001) and prolonged diagnostic delay (p = 0.0001). Conclusion DS represents a unique juvenile-onset arthropathy, distinct from polyarticular JIA. Awareness among pediatricians is essential for early recognition and proper treatment. Further studies, including synovial pathology, immunology and genetics may contribute to a better understanding of this rare disorder of childhood.

Background: Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. Main text: Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. Conclusions: The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy. [ABSTRACT FROM AUTHOR]

The rheology of lavas and magmas exerts a strong control on the dynamics and hazards posed by volcanic eruptions. Magmas and lavas are complex mixtures of silicate melt, suspended crystals, and gas bubbles. To improve the understanding of the dynamics and effective rheology of magmas and lavas, we performed dam‐break flow experiments using suspensions of silicone oil, sesame seeds, and N2O bubbles. Experiments were run inside a magnetic resonance imaging (MRI) scanner to provide imaging of the flow interior. We varied the volume fraction of sesame seeds between 0 and 0.48, and of bubbles between 0 and 0.21. MRI phase‐contrast velocimetry was used to measure liquid velocity. We fit an effective viscosity to the velocity data by approximating the stress using lubrication theory and the imaged shape of the free surface. In experiments with both particles and bubbles (three‐phase suspensions), we observed shear banding in which particle‐poor regions deform with a lower effective viscosity and dominate flow propagation speed. Our observations demonstrate the importance of considering variations in phase distributions within magmatic fluids and their implications on the dynamics of volcanic eruptions. Plain Language Summary: Lavas and magmas are viscous liquids whose properties control the dynamics and associated hazards of volcanic eruptions. They are comprised of multiple phases, including silicate liquids, crystals, and gas bubbles, whose interactions control lava and magma behavior. To understand how these phases interact and determine lava properties, we ran a series of lava flow analog experiments in a medical magnetic resonance imaging scanner to measure the inside of flows. We explored a range of particle (0–0.48) and bubble (0–0.21) volume fractions to consider ranges relevant to natural flows. We measured the velocity of the liquid phase in imaging planes through the middle of the setup and used the velocity to invert for suspension viscosity. In experiments containing particles, we observed the development of structures in the flow, characterized by alternating bands of high and low particle concentration. The particle‐poor regions deform more easily than we would expect for the uniform suspension and allow for faster propagation of the flow. Key Points: MRI experiments of low‐concentration bubble‐bearing suspensions resemble liquid flows and adhere to bulk rheology approximationsThree‐phase mixtures show strong shear localization even at low total volume fraction of particlesShear bands dominate flow and allow for faster flow front propagation speeds than would be expected for a homogeneous suspension [ABSTRACT FROM AUTHOR]

Simple Summary: Organs which receive an irradiation dose because they are located in the vicinity of the irradiation target are considered organs of risk. Before a new irradiation technique is tested in clinical trials, it is important to make an assessment of the potential adverse effects in these organs of risk. Microbeam radiotherapy is an innovative radiotherapy technique suitable to control tumours which are considered radioresistant with conventional, already clinically established irradiation techniques. In a small animal model, we have conducted a risk assessment in the thoracic spinal cord as organ of risk during microbeam irradiation in the thoracic cavity and determined the upper dose limit beyond which neurological signs of temporary or permanent damage occur. Microbeam radiotherapy (MRT), a high dose rate radiotherapy technique using spatial dose fractionation at the micrometre range, has shown a high therapeutic efficacy in vivo in different tumour entities, including lung cancer. We have conducted a toxicity study for the spinal cord as organ of risk during irradiation of a target in the thoracic cavity. In young adult rats, the lower thoracic spinal cord was irradiated over a length of 2 cm with an array of quasi-parallel microbeams of 50 µm width, spaced at a centre-to-centre distance of 400 µm, with MRT peak doses up to 800 Gy. No acute or subacute adverse effects were observed within the first week after irradiation up to MRT peak doses of 400 Gy. No significant differences were seen between irradiated animals and non-irradiated controls in motor function and sensitivity, open field test and somatosensory evoked potentials (SSEP). After irradiation with MRT peak doses of 450–800 Gy, dose-dependent neurologic signs occurred. Provided that long-term studies do not reveal significant morbidity due to late toxicity, an MRT dose of 400 Gy can be considered safe for the spinal cord in the tested beam geometry and field size. [ABSTRACT FROM AUTHOR]

The current pandemic situation has made it important for everyone to wear masks. Digital image forensics plays an important role in preventing medical fraud and in object detection. It is helpful in avoiding the high-risk situations related to the health and security of the individuals or the society, including getting the proper evidence for identifying the people who are not wearing masks. A smart system can be developed based on the proposed soft computing technique, which can be helpful to detect precisely and quickly whether a person wears a mask or not and whether he/she is carrying a gun. The proposed method gave 100% accurate results in videos used to test such situations. The system was able to precisely differentiate between those wearing a mask and those not wearing a mask. It also effectively detects guns, which can be used in many applications where security plays an important role, such as the military, banks, etc. [ABSTRACT FROM AUTHOR]

Although sonodynamic therapy (SDT) is a promising non-invasive tumor treatment strategy due to its safety, tissue penetration depth and low cost, the hypoxic tumor microenvironment limits its therapeutic effects. Herein, we have designed and developed an oxygen-independent, ROS-amplifying chemo-sonodynamic antitumor therapy based on novel pH/GSH/ROS triple-responsive PEG-PPMDT nanoparticles. The formulated artemether (ART)/Fe3O4-loaded PEG-PPMDT NPs can rapidly release drug under the synergistic effect of acidic endoplasmic pH and high intracellular GSH/ROS levels to inhibit cancer cell growth. Besides, the ROS level in the NPs-treated tumor cells is magnified by ART via interactions with both Fe2+ ions formed in situ at acidic pH and external ultrasound irradiation, which is not affected by hypoxia tumor microenvironment. Consequently, the enriched intracellular ROS level can cause direct necrosis of ROS-stressed tumor cells and further accelerate the drug release from the ROS-responsive PEG-PPMDT NPs, achieving an incredible antitumor potency. Specifically, upon the chemo-sonodynamic therapy by ART/Fe3O4-loaded PEG-PPMDT NPs, all xenotransplants of human hepatocellular carcinoma (HepG2) in nude mice shrank significantly, and 40% of the tumors were completely eliminated. Importantly, the Fe3O4 encapsulated in the NPs is an efficient MRI contrast agent and can be used to guide the therapeutic procedures. Further, biosafety analyses show that the PEG-PPMDT NPs possess minimal toxicity to main organs. Thus, our combined chemo-sonodynamic therapeutic method is promising for potent antitumor treatment by controlled release of drug and facile exogenous generation of abundant ROS at target tumor sites.

Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arias supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the "BraTS 2017 challenge". The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management. [ABSTRACT FROM AUTHOR]

Background: Dry synovitis (DS) is a rare entity as only a few cases have been reported to date. We describe the clinical features, radiological manifestations and course of DS in comparison with rheumatoid factor negative polyarticular juvenile idiopathic arthritis (RFneg-polyJIA). Methods: We performed a multicenter retrospective collection of data of DS patients who presented with progressive joint limitations without palpable synovitis, absence of elevated acute phase reactants, negative ANA and RF, and imaging showing joint and/or osteochondral involvement. For comparative purposes, we included a cohort of RF neg-polyJIA patients. Results: Twelve DS patients, 8F/4 M, with mean age at onset of 6.1 years, were included. Presenting signs comprised delayed motor development, functional limitations and/or progressive stiffness. Clinical examination showed symmetric polyarticular involvement with variable muscular atrophy. MRI showed mild, diffuse synovial involvement, without effusion. With time, signs of progressive osteochondral damage became evident, despite treatment. All patients were treated with low-dose corticosteroids and methotrexate. Anti-TNF agents were prescribed in five. The response was variable with limited joint mobility in 11/12, and need of joint replacement in 2. In comparison with a cohort of RFneg-polyJIA, DS patients presented higher number of joint involved (p = 0.0001) and contractures (p = 0.0001), less swelling (p = 0.0001) and prolonged diagnostic delay (p = 0.0001). Conclusion: DS represents a unique juvenile-onset arthropathy, distinct from polyarticular JIA. Awareness among pediatricians is essential for early recognition and proper treatment. Further studies, including synovial pathology, immunology and genetics may contribute to a better understanding of this rare disorder of childhood. [ABSTRACT FROM AUTHOR]

Atherosclerosis, as a result of an inflammatory process, is the thickening and loss of elasticity of the walls of arteries that is associated with the formation of atherosclerotic plaques within the arterial intima, which present a double threat. A piece of vulnerable plaque can break off and be carried by the bloodstream until it gets stuck; and plaque that narrows an artery may lead to a thrombus that sticks to the blood vessel's inner wall. The purpose of the present article is to compare effects across different atheromatous plaque material assumptions on hemodynamics and biomechanics within a partly patient-specific computational domain representing an atherosclerotic artery. A full scale 3D ESI numerical model is implemented and different material hyperelastic assumptions are considered for comparison purposes. The 3D realistic geometry is reconstructed from a medical image. This technique may be useful, specially with the recent advances in computer-aided design (CAD), medical imaging, and 3D printing technologies that have provided a rapid and cost efficient method to generate arterial stenotic biomodels, making in vitro studies a valuable and powerful tool. To understand our results, hemodynamic parameters and structural stress analysis were performed. The results are consistent with previous findings. [ABSTRACT FROM AUTHOR]

Venolymphatic malformations occur due to dysplasia of lymphatic and venous vessel structures. These tumour-like lesions cause tissue abnormality with impaired function and have aesthetic importance when involving the craniofacial region. Vascular lesions are classified as haemangiomas and other vascular malformations. They are named after the vessels involved in malformation like arterial, venous, lymphatic, or mixed types. Present study represents a rare case report of a 23-year-old patient who presented to our hospital with swelling over the left elbow and a lump over the left breast, which was diagnosed as venolymphatic malformation involving the left breast and left elbow joint, which is an unusually affected anatomical region by this congenital anomaly. Ultrasonography (USG), the primary tool for diagnosis, was performed. Later, the patient underwent Magnetic Resonance Imaging (MRI) for confirmation and to determine the extent of the lesion. Excision of the lesion was done over left elbow completely with a wide margin. Histopathology confirmed the final diagnosis. Venous malformations (VM) are the most common among these congenital malformations, with an incidence of over 50%. Lymphatic malformations (LM) have a much lower incidence than venous malformations, but the combination of venous and LM has a very low incidence. [ABSTRACT FROM AUTHOR]

Jake Sellers,1 Rachel Wagstaff,1 Naseem Helo,2 Werner TW de Riese1 1Texas Tech University Health Sciences Center - School of Medicine, Department of Urology, Lubbock, TX, 79430-7260, USA; 2University Medical Center - Department of Radiology, Lubbock, TX, 79415, USACorrespondence: Werner TW de Riese, Texas Tech University Health Sciences Center – Department of Urology, 3601 4 th Street, Lubbock, TX, 79430-7260, USA, Tel +1 806-743-3862, Fax +1 806-743-3030, Email Werner.Deriese@ttuhsc.eduPurpose: Benign prostatic hyperplasia (BPH) and prostate cancer (PCa) are the two most prevalent and common urologic diseases impacting elderly men. The current literature has well documented an inverse relationship between prostate/BPH-size and incidence of PCa, but the exact interaction between these two disease entities is not well understood. The purpose of this study is to analyze prostatic zonal measurements with magnetic resonance imaging (MRI) in order to investigate the dynamic changes of the transition zone (TZ) and peripheral zone (PZ) in response to prostate/BPH growth.Methods: Multiparametric magnetic resonance imaging (mpMRI) scans of 430 consecutive male patients aged 18– 89 years were obtained to measure the different zonal areas of the prostate. The data were statistically analyzed to identify specific associations between the different measurement parameters and total prostate volume (TPV).Results: The Mann–Whitney U-test showed a significant decline of the average peripheral zone thickness (PZT) (z = − 4.5665, p < 0.0001) in larger prostates when compared to smaller prostates. The Spearman correlation between TPV and PZT demonstrated a significant negative correlation (− 0.20, p < 0.0001).Conclusion: The data revealed that PZT was significantly smaller in the subgroup of patients with higher TPV. This supports the hypothesis of PZ compression and thinning caused by the growing and expanding TZ in BPH prostates. This dynamic growth-related process in the different prostatic zones may explain the protective effect of BPH against PCa.Keywords: BPH, MRI imaging, peripheral zone, central gland, measurements

Nanodiamonds (NDs) have high potential as a drug carrier and in combination with nitrogen vacancies (NV centers) for highly sensitive MR-imaging after hyperpolarization. However, little remains known about their physiological properties in vivo. PET imaging allows further evaluation due to its quantitative properties and high sensitivity. Thus, we aimed to create a preclinical platform for PET and MR evaluation of surface-modified NDs by radiolabeling with both short- and long-lived radiotracers. Serum albumin coated NDs, functionalized with PEG groups and the chelator deferoxamine, were labeled either with zirconium-89 or gallium-68. Their biodistribution was assessed in two different mouse strains. PET scans were performed at various time points up to 7 d after i.v. injection. Anatomical correlation was provided by additional MRI in a subset of animals. PET results were validated by ex vivo quantification of the excised organs using a gamma counter. Radiolabeled NDs accumulated rapidly in the liver and spleen with a slight increase over time, while rapid washout from the blood pool was observed. Significant differences between the investigated radionuclides were only observed for the spleen (1 h). In summary, we successfully created a preclinical PET and MR imaging platform for the evaluation of the biodistribution of NDs over different time scales. [ABSTRACT FROM AUTHOR]