Current Medical Imaging - Current Issue

This study aimed to explore the diagnostic value of multiorgan (heart, lungs, blood vessels) point-of-care ultrasound (PoCUS) in patients with high-risk and medium-to-low-risk pulmonary embolism (PE).

Clinical data of 92 patients with suspected PE, admitted to Hangzhou TCM Hospital affiliated with Zhejiang Chinese Medical University from July 2021 to June 2023, were retrospectively analyzed. According to hemodynamic status, patients were divided into the high-risk (n=28) and the medium-to-low-risk groups (n=64). Using computed tomography (CT) and pulmonary angiography (CTPA) as the gold standard, all patients underwent multiorgan PoCUS examination. The sensitivity, specificity, and accuracy of different methods for diagnosing PE, as well as the time difference between multiorgan PoCUS examination and CTPA, were compared. Differences in measurement values of relevant indicators in all groups were analyzed.

In the high-risk group of patients, CTPA identified 15 cases of PE. In contrast, the PoCUS examination confirmed PE diagnosis in 14 cases (true positive), while 10 cases were diagnosed as true negative, one case as false negative, and three cases as false positive. In the medium-to-low-risk group, CTPA identified 50 patients with PE, while multiorgan PoCUS confirmed PE diagnosis in 33 cases (true positive), and identified 9 true negative, 17 false negative, and 5 false positive PE cases. Kappa test of the consistency between the results of multiorgan PoCUS and CTPA showed that multiorgan PoCUS had higher sensitivity, negative predictive value, and accuracy in the high-risk group compared to the medium-to-low-risk group (p<0.05). Cohen's Kappa value of the high-risk group was 0.710, indicating moderate consistency between PoCUS and CTPA results, while Cohen's Kappa value of 0.231 for the medium and low-risk group indicated poor consistency. There was a significant difference in ultrasound parameters between the high-risk and the medium-to-low-risk group (p<0.05). The time required for multiorgan PoCUS in both groups was significantly shorter than that for the CTPA. There was no significant difference in the time required for PoCUS between the two groups (p>0.05).

Multiorgan PoCUS has been found to have higher sensitivity and accuracy in diagnosing patients with high-risk PE compared to those with medium-to-low-risk PE, and a shorter imaging time compared to CTPA.

Pneumocephalus and pneumorrhachis are rare postoperative complications, commonly occurring within a few days to months after spinal surgery. They are very rarely reported after thoracic surgeries. This case highlights a unique presentation in the emergency department involving headache and vomiting caused by late complications following thoracic surgery with a titanium rib implant.

A 64-year-old male presented to the emergency department with headache and vomiting without fever since prior 1 week. He had a history of left lower lobectomy and thoracic wall reconstruction with a titanium rib implant 40 days earlier due to epidermoid lung cancer. Computed tomography imaging of head and thorax revealed bilateral pneumocephalus and extensive pneumorrhachis. After removal of the rib implant and dural repair, the patient fully recovered.

This case underscores the importance of early imaging and diagnosis in patients presenting with neurological symptoms following thoracic surgery and emphasizes the need for enhanced monitoring protocols for patients with titanium implants.

Primary thoracic lymphangioma is a rare disease. Most of the previous studies are comprised of individual case reports, with a very limited number of patients included.

This study aims to investigate the chest computed tomography (CT) imaging features and clinical manifestations of thoracic lymphangioma, thereby enhancing our understanding of the condition.

A retrospective analysis was conducted on 62 patients diagnosed with thoracic lymphangioma, comprising 32 males and 30 females. The study focused on analyzing the chest CT imaging features and the clinical manifestations observed in these patients.

The incidence rates of thoracic lymphangioma did not differ significantly between males and females; however, it was more frequently observed in children and adolescents. The most common clinical symptoms included cough, fever, chylothorax, chylous pericardium, and lymphedema. The mediastinum (82.3%) emerged as the most frequent location for thoracic lymphangioma, followed by the chest wall (62.9%), bone (40.3%), and pleura (32.3%). Pulmonary lymphangioma, the least prevalent subtype (19.4%), exhibited a propensity to induce respiratory symptoms, frequently manifesting as a generalized lymphatic anomaly (GLA). Furthermore, elevated levels of D-dimer were detected in 34 patients (54.8%) with thoracic lymphangioma.

Imaging examinations play a crucial role in assisting clinicians in making more accurate early diagnoses of thoracic lymphangioma. They are also helpful for assessing the extent of systemic infiltration and enhancing diagnostic precision. With radiological assessment, clinicians could more readily select appropriate therapeutic treatments and monitor the progression of follow-up care.

This study aims to develop an accurate image registration framework for personalized respiratory motion modeling.

The proposed framework incorporates longitudinal data through a two-stage transfer learning approach. In the first stage, transfer learning is employed on longitudinal data collected from the same device. In the second stage, a personalized model is constructed using the transfer learning approach, reusing the model from the first stage. A novel cross-error function is introduced to guide the customized adaptation stage.

The experiments demonstrate the effectiveness of the proposed framework in respiratory motion modeling. Integrating longitudinal data allows for improved accuracy for personalized respiratory motion modeling.

The study presents a novel approach that incorporates longitudinal data into a two-stage transfer learning process for personalized respiratory motion modeling. The framework demonstrates improved accuracy. The results highlight the potential of leveraging longitudinal data to provide personalized image registration solutions.

The neuroanatomical basis of white matter fiber tracts in gait impairments in individuals suffering from Parkinson’s Disease (PD) is unclear.

Twenty-four individuals living with PD and 29 Healthy Controls (HCs) were included. For each participant, two-shell High Angular Resolution Diffusion Imaging (HARDI) and high-resolution 3D structural images were acquired using the 3T MRI. Diffusion-weighted data preprocessing was performed using the orientation distribution function to trace the main fiber tracts in PD individuals. Clinical characteristics between the two groups were compared, and the correlation between the FA value and behavioral data was analyzed. Quantitative gait and clinical parameters were recorded in PD at ON and OFF states, respectively.

The mean tract-specific FA values of the right Cingulum Cingulate (rCC) were statistically different between the PD group and the HC group (p =0.047). The FA value of 34-58 equidistant nodes in rCC was positively correlated with Mini-Mental State Examination (MMSE) (r=0.527, p=0.024), Berg Balance Scale (BBS)-OFF (r=0.480, p =0.040), and BBS-ON (r=0.528, p =0.024) scores, while it was negatively correlated with the MDS-UPDRS-III-ON score (r=-0.502, p =0.030). Regarding the gait analysis, the FA value was significantly correlated with velocity, cadence, and stride time of the pace and rhythm domains in both ‘ON’ and ‘OFF’ states, respectively (p<0.05).

This study served as an initial exploration to establish that HARDI sequences could be employed as a robust tool for analyzing microstructural alterations in white matter fiber bundles among PD patients, although the sample size was small. We confirmed microstructural integrity impairment of rCC to be significantly associated with both gait and cognitive deficits in patients with PD. Early detection of microstructural changes in rCC and targeted treatment can help improve behavioral disorders. In the future, we intend to further integrate multimodal data with assessments of patient behavior both prior to and following intervention. We will validate our findings within an independent cohort to monitor disease progression and evaluate the efficacy of therapeutic interventions.

The main objective of this work was to investigate the effect of cavum septi pellucidi et vergae (CSPV) on the pathogenesis of ventriculomegaly (VM) cases detected during the fetal period.

The fetuses of 515 mothers who applied to the Department of Radiology between October 2011 and December 2022 and who had undergone fetal magnetic resonance imaging (fMRI) were evaluated retrospectively. 152 fetuses with CSPV were included in the study. The fetuses were separated into the following groups: those with right VM (n = 20), those with left VM (n = 56), and those with bilateral VM (n = 44). Fetuses with CSPV, but without VM (n = 32), were included in the study as the control group. For the group with CSPV, lines were drawn to divide the fetal cranium into two symmetrical parts at the interhemispheric line in the axial and coronal planes. The distances from these lines to the lateral leaves of the CSPV were measured. In addition, measurements of the CSPV (anteroposterior, transverse, and high) were taken. An evaluation of whether that was associated with ventricular width or maternal age and gestational week was conducted.

The left ventricular width was significantly higher in cases where the CSPV deviated more to the right, and the right ventricular width was significantly higher in cases where the CSPV deviated more to the left. When the VM rates in the VM group without CSPV and the VM rates in the VM group with CSPV were compared, the VM rates were found to be significantly higher in those with CSPV.

Fetuses with CSPV should be followed up for the possibility of developing VM. However, it should be remembered that VM may be a variation due to CSPV. There is an inverse relationship between the side where CSPV deviates and the side where VM is observed.

We examined the effectiveness of integrating three-dimensional (3D) visualization reconstruction technology with Problem-Based Learning (PBL) in the clinical teaching of resident physicians focusing on pheochromocytoma.

Fifty resident physicians specializing in urology at Peking Union Medical College Hospital were randomly divided into two groups over the period spanning January 2022 to January 2024: an experimental group and a control group. The experimental group underwent instruction utilizing a pedagogical approach that integrated 3D visualization reconstruction technology with PBL, while the control group used a traditional teaching model. A comparative analysis of examination performance and teaching satisfaction between both groups of resident physicians was conducted to assess the efficacy of the integrated 3D visualization and PBL teaching methods in clinical instruction.

The experimental group demonstrated superior performance in both theoretical assessment and clinical skills evaluation, along with heightened levels of teaching satisfaction compared to the control group, with statistically significant differences (p < 0.05). Additionally, the experimental group exhibited markedly higher scores in both theoretical examinations and practical assessments compared to their counterparts in the control group (p < 0.05). The results of theoretical examinations for the experimental group and the control group were 92.15±3.22 and 81.09±4.46, respectively (< 0.0001). The results of practical examinations for the experimental group and the control group were 90.17±3.48 and 70.75±5.11, respectively (< 0.0001).

In the clinical teaching of training resident physicians specializing in urology for the management of pheochromocytoma, the integration of 3D visualization reconstruction technology with the PBL method significantly enhanced the teaching efficacy, improving both the quality of instruction and the level of satisfaction among participants.

The BraTS Generalizability Across Tumors (BraTS-GoAT) initiative addresses the critical need for robust and generalizable models in brain tumor segmentation. Despite advancements in automated segmentation techniques, the variability in tumor characteristics and imaging modalities across clinical settings presents a significant challenge.

This study aims to develop an advanced CNN-based model for brain tumor segmentation that enhances consistency and utility across diverse clinical environments. The objective is to improve the generalizability of CNN models by applying them to large-scale datasets and integrating robust preprocessing techniques.

The proposed approach involves the application of advanced CNN models to the BraTS 2024 challenge dataset, incorporating preprocessing techniques such as standardization, feature extraction, and segmentation. The model's performance was evaluated based on accuracy, mean Intersection over Union (IOU), average Dice coefficient, Hausdorff 95 score, precision, sensitivity, and specificity.

The model achieved an accuracy of 98.47%, a mean IOU of 0.8185, an average Dice coefficient of 0.7, an average Hausdorff 95 score of 1.66, a precision of 98.55%, a sensitivity of 98.40%, and a specificity of 99.52%. These results demonstrate a significant improvement over the current gold standard in brain tumor segmentation.

The findings of this study contribute to establishing benchmarks for generalizability in medical imaging, promoting the adoption of CNN-based brain tumor segmentation models in diverse clinical environments. This work has the potential to improve outcomes for patients with brain tumors by enhancing the reliability and effectiveness of automated segmentation techniques.

Multiple sclerosis (MS) is one of the most common disabling central nervous system diseases affecting young adults. Magnetic resonance imaging (MRI) is an essential tool for diagnosing and following up multiple sclerosis. Over the years, many MRI techniques have been developed to improve the sensitivity of MS disease detection. In recent years synthetic MRI (sMRI) and quantitative MRI (qMRI) have gained traction in neuroimaging applications, providing more detailed information than traditional acquisition methods. These techniques enable the detection of microstructural changes in the brain with high sensitivity and robustness to inter-scanner and inter-observer variability. This study aims to evaluate the feasibility of using these techniques to avoid administering intravenous gadolinium-based contrast agents (GBCAs) for assessing MS disease activity and monitoring.

Forty-two known MS patients, aged 20 to 45, were scanned as part of their routine follow-up. MAGnetic resonance image Compilation (MAGiC) sequence, an implementation of synthetic MRI, was added to our institute's routine MS protocol to automatically generate quantitative maps of T1, T2, and PD. T1, T2, PD, and apparent diffusion coefficient (ADC) data were collected from regions of interest (ROIs) representing normal-appearing white matter (NAWM), enhancing, and non-enhancing MS lesions. The extracted information was compared, and statistically analyzed, and the sensitivity and specificity were calculated.

The mean R1 (the reciprocal of T1) value of the non-enhancing MS lesions was 0.694 s^-1 (T1=1440 ms), for enhancing lesions 1.015 s^-1 (T1=985ms), and for NAWM 1.514 s^-1 (T1=660ms). For R2 (the reciprocal of T2) values, the mean value was 6.816 s^-1 (T2=146ms) for non-enhancing lesions, 8.944 s⁻¹ (T2=112 ms) for enhancing lesions, and 1.916 s⁻¹ (T2=522 ms) for NAWM. PD values averaged 93.069% for non-enhancing lesions, 82.260% for enhancing lesions, and 67.191% for NAWM. For ADC, the mean value for non-enhancing lesions was 1216.60×10⁻⁶ mm²/s, for enhancing lesions 1016.66×10⁻⁶ mm²/s, and for NAWM 770.51×10⁻⁶ mm²/s.

Our results indicate that enhancing and non-enhancing MS lesions significantly decrease R1 and R2 values. Non-enhancing lesions have significantly lower R1 and R2 values compared to enhancing lesions.

Conversely, PD values are significantly higher in non-enhancing lesions than in enhancing lesions. For ADC, while NAWM has lower values, there was minimal difference between the mean ADC values of enhancing and non-enhancing lesions.

Leptomeningeal enhancement, visible on MRI, can indicate a variety of diseases, both neoplastic and non-neoplastic.

This comprehensive pictorial review aims to equip radiologists and trainees with a thorough understanding of the diverse imaging presentations of leptomeningeal disease.

Drawing from a retrospective analysis of MRI scans conducted between 1 January 2008 and 30 September 2022, at two tertiary teaching hospitals in Singapore, this review covers a wide range of conditions.

The main neoplastic conditions discussed include leptomeningeal carcinomatosis, myelomatosis, schwannoma, CNS lymphoma, and pineal region tumors. Additionally, the review addresses non-neoplastic enhancements such as meningoencephalitis, intracranial hypotension, cerebral ischemia/infarction, epidural lipomatosis, syringomyelia, Sturge-Weber syndrome, and subarachnoid hemorrhage.

By highlighting the imaging features and patterns associated with these conditions, the review underscores the critical role of accurate diagnosis and timely management in improving patient outcomes. Enhanced understanding of these conditions can significantly improve patient outcomes through timely and effective therapeutic interventions.

Studies specifically examining the sonographic features of juvenile fibroadenoma in the pediatric population have not been documented. We aimed to analyze sonograms of juvenile fibroadenoma in children.

Patients aged ≤ 18 years who underwent breast ultrasound examinations at our department and had pathologically proven juvenile fibroadenoma from September 2002 to January 2022 were included in this study. Demographic data, clinical findings, and sonograms were retrospectively analyzed. Patients were further divided into the puberty and post-puberty subgroups, and their results were compared.

A total of 24 girls aged 10-18 years with 27 masses diagnosed as juvenile fibroadenomas were identified. The diameter of the masses averaged 5.8 ± 3.3 cm, with a range of 1.5-13.6 cm. Twenty-one (87.5%) patients had a single mass and 3 had double lesions. Over 80% of the lesions were oval-shaped and encapsulated with circumscribed margins and parallel orientation. All masses showed internal hypoechogenicity, either uniform or heterogeneous. For masses that had a diameter > 5 cm, screening with high-frequency transducers revealed no posterior acoustic features or posterior shadowing. However, these features changed to posterior acoustic enhancement when the masses were re-evaluated using low-frequency transducers. Ultrasonic color Doppler showed blood flow in 24 (88.9%) masses. There were no significant differences in the incidence and sonographic features between the two subgroups.

Most juvenile fibroadenomas in children are oval, circumscribed, encapsulated masses with detectable blood flow. All juvenile fibroadenomas presented in this study exhibit internal hypoechogenicity with no posterior acoustic shadowing detected in any cases. Our findings suggest that screening with low-frequency transducers should be performed for a mass that has a diameter > 5 cm.

Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are slow and require high hardware specifications.

The aim of this study is to develop a detection model that achieves both high accuracy and real-time performance, ensuring effective and timely results.

In this study, based on YOLOv5s, a concentrated-comprehensive convolution (C3_ODC) module with multidimensional attention is designed in the convolutional layer of the original backbone network for enhancing the feature-extraction capabilities of the model. Moreover, lightweight convolution is combined with weighted bidirectional feature pyramid networks (BiFPNs) to form a GS-BiFPN structure that enhances the fusion of multiscale features while reducing the number of model parameters. Finally, Focal Loss is combined with the normalized Wasserstein distance (NWD) to optimize the loss function. Focal loss focuses on carcinoma-positive samples to mitigate class imbalance, whereas the NWD enhances the detection performance of small lung nodules.

In comparison experiments against the YOLOv5s, the proposed model improved the average precision by 8.7% and reduced the number of parameters and floating-point operations by 5.4% and 8.2%, respectively, while achieving 116.7 frames per second.

The proposed model balances high detection accuracy against real-time requirements.

Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the pursuit of the prevention and prediction of pneumonia, this work employs the development of an advanced deep-learning model by using a federated learning framework. The deep learning models rely on the utilization of a centralized system for disease prediction on the medical imaging data and pose risks of data breaches and exploitation; however, federated learning is a decentralized architecture which significantly reduces data privacy concerns.

The federated learning works in a distributed architecture by sending a global model to clients rather than sending the data to the model. The proposed federated deep learning-based FedPneu computer-aided diagnosis model has been implemented in 2, 3, 4, and 5 clients architecture for early pneumonia detection using X-ray images. The key parameters configuration include batch size, learning rate, optimizer, decay, momentum, epochs, rounds, and random-split as 32, 0.0001, SGD, 0.000001, 0.9, 10, 100, and 42, respectively.

The results of the proposed federated deep learning-based FedPneu model have been provided in terms of round-wise accuracy, loss, and computational time. The highest accuracy of 85.632% has been achieved with 2-clients federated deep learning architecture, whereas, 3, 4, and 5 clients architecture achieved 85.536%, 76.112%, and 74.123% accuracies, respectively.

In the proposed privacy-protected federated deep learning-based FedPneu model, the two-client architecture has been resulted as the most optimal framework for pneumonia detection among 3-clients, 4-clients, and 5-clients architecture. The model works in a collaborative and privacy-protected framework with a multi-silo dataset which could be highly beneficial for healthcare departments to maintain patient’s data privacy with improved prediction outcomes.

Understanding brain changes in older patients with depression and their relationship with cognitive abilities may aid in the diagnosis of depression in this population. This study aimed to explore the association between brain lesions and cognitive performance in older patients with depression.

We utilized magnetic resonance imaging data from a previous study, which included older adults with and without depression. Smoothed Regional Homogeneity (ReHo) and local brain Amplitude of Low-frequency Fluctuation (ALFF) values were assessed to examine brain activity.

The analysis revealed decreased ReHo in the left middle temporal gyrus, left middle frontal gyrus, and left precuneus, as well as increased local ALFF in the right middle occipital gyrus, left postcentral gyrus, and right precentral gyrus in older patients with depression. These alterations may contribute to behavioral and cognitive changes. However, no significant relationship was found between ReHo values and Montreal Cognitive Assessment (MoCA) scores. In contrast, increased local ALFF in the left postcentral gyrus and right precentral gyrus was negatively correlated with MoCA scores.

This study demonstrated a significant association between regional brain alterations in patients with depression and cognitive behavior. Thus, this work identified functional brain regions and cognitive performance in older adults with depression, highlighting specific brain regions that play a crucial role in cognitive abilities in this population.

Mucinous Cystadenocarcinoma (MCA) of the breast remains a relatively rare condition, and to date, there is no systematic summary of its imaging manifestations. Therefore, this report presents a detailed account of the diagnosis and treatment of mucinous cystadenocarcinoma in a 40-year-old woman, with a particular focus on imaging findings. Additionally, we conducted a comprehensive literature review on this disease and summarized its key imaging features. This manuscript provides valuable insights and methodologies for the accurate diagnosis of mucinous cystadenocarcinoma.

We report a 40-year-old premenopausal woman who discovered multiple cysts in her left breast five years ago. Over the past two years, the size of these tumors has increased. Ultrasound examination indicated that the cysts had grown to 27 x 17mm. Following a puncture, the cysts were confirmed to be benign and were not monitored regularly. A year later, the patient's mass in the left breast increased, and an ultrasound exam indicated a suspicious mixed echo area in the upper outer quadrant, suggestive of a malignant lesion. Mammography showed amorphous suspicious calcifications in the lesion area, distributed in segments. Contrast-enhanced magnetic resonance imaging displayed non-mass-type enhancement of the lesion, with a dynamic enhanced imaging time-signal intensity curve (TIC) showing a rapidly rising plateau pattern. Postoperative pathology confirmed invasive carcinoma of the left breast along with mucinous cystadenocarcinoma. Four months after surgery, the patient developed multiple abnormal lymph nodes in the left axilla, which were confirmed to be metastasis upon pathology examination. Following radiotherapy, the patient's condition remained stable during the follow-up period.

Most MCA lesions typically exhibit clear borders and irregular edges, with some displaying expansive growth and compression of surrounding tissues. Mammography can reveal calcified components in lesions. Ultrasound often reveals an isoechoic or hypoechoic mass with well-defined borders but irregular edges. Magnetic resonance imaging (MRI) can show clear boundaries and uneven enhancement of the lesions, and the time-intensity curve (TIC) of the mass area often shows an inflow enhancement pattern.

Cervical cancer is a prevalent malignancy among women, often asymptomatic in early stages, complicating detection.

This study aims to investigate innovative techniques for early cervical cancer detection using a novel U-RCNNS model.

Cervical epithelial cell images stained with hematoxylin and eosin (HE) were analyzed using the U-RCNNS model, which integrates U-Net for segmentation and R-CNN for object detection, incorporating dilated convolution techniques.

The U-RCNNS model significantly improved the accuracy of detecting and segmenting cervical cancer cells, with the enhanced Mask R-CNN showing notable advancements over the baseline model.

The U-RCNNS model presents a promising solution for early cervical cancer detection, offering improved accuracy compared to traditional methods and highlighting its potential for clinical application in early diagnosis.

The aim of this study was to evaluate renal artery embolization in patients with spontaneous renal artery bleeding based on detailed angiographic findings and a comprehensive analysis of its efficacy and clinical outcomes.

This retrospective study evaluated the outcomes of renal artery embolization in 18 cases among 15 patients (11 men and 4 women; mean age: 57.9 years) treated for spontaneous renal bleeding at our institution between March 2017 and October 2023. Data derived from abdominal computed tomography (CT) and arteriography were analyzed to assess the effectiveness of embolization.

Most patients had end-stage renal disease or renal atrophy, with common findings on CT scans, including signs of active bleeding in 66.7% (10/15) and hematoma extending to the retroperitoneal space in 53.3% (8/15). Microcoils were commonly used for embolization (n = 10), with a technical success rate of 100% and primary and final clinical success rates of 80% and 100%, respectively. No major complications were reported during the follow-up, and clinical improvement was observed in all patients who underwent total embolization, with few instances of reduced hematoma size and renal atrophy.

Transarterial embolization is safe and effective for controlling spontaneous renal hemorrhage.

Cytomegalovirus (CMV) infection is common in the digestive and central nervous systems and can infect the entire digestive tract from the mouth to the rectum. In immunocompromised patients, CMV infection is prone to develop into CMV disease, especially in Acquired Immune Deficiency Syndrome (AIDS) patients. Severe cases may accelerate the progression of AIDS patients and form systemic CMV infection. Timely diagnosis and treatment are very important for the prognosis of patients.

In this paper, we report a 36-year-old man with a Human Immunodeficiency Virus (HIV) infection complicated with CMV colitis. Three weeks ago, he developed abdominal pain with fresh blood in the stool, accompanied by anal pain. He was found to be HIV positive 8 years ago. An enhanced CT scan showed edema and irregular thickening of the rectal wall, obvious enhancement of the mucosa, and multiple enlarged lymph nodes around. ¹⁸F-FDG PET/CT imaging displayed diffuse rectum wall thickening and increased glucose metabolism, and the SUV max was 12.7. There were multiple enlarged lymph nodes around the rectum, glucose metabolism was increased, and the SUVmax was 4.6.

¹⁸F-FDG-PET imaging technology has potential value in the diagnosis of CMV colitis, especially in immunocompromised patients. Detection of FDG concentrations in the colon wall can help diagnose CMV infection and understand the extent of the lesion, which is essential for the timely initiation of antiviral therapy.

Type 2 diabetes Mellitus (T2DM) increases vulnerability to metabolic dysfunction-associated steatotic liver disease (MASLD). Therefore, this study aims to determine the prevalence and coexistence of MASLD in patients with T2DM using ultrasound.

This cross-sectional retrospective study included 168 patients with T2DM from multiple diabetes clinics in Abha City, Asir region, recruited between August 2023 and December 2023. Adult patients aged 18 and over with T2DM were included, and data was extracted from patient files. All patients were examined by ultrasound to determine the prevalence and coexistence of MASLD in patients with T2DM. Hepatic steatosis on B-mode ultrasound is qualitatively classified on a four-point scale: normal (0), mild (1), moderate (2), and severe (3).

Out of 168 patients, 68.4% were identified with MASLD, mostly with diffuse liver (97.4%) diagnosed through ultrasound. MASLD was significantly higher in individuals with uncontrolled diabetes (72.5%) than those with controlled diabetes (46.2%), with a significant difference (p=0.015) and an odds ratio (OR) of 3.081, indicating uncontrolled diabetics are over three times more likely to develop MASLD. The uncontrolled group had a statistically significant larger liver size than the control group (13.6cm ±1.43 vs. 13.0cm ±1.20, respectively: [p=0.032, 95%CI 0.053-1.12]). Furthermore, a notable association was observed between increased BMI and the prevalence of MASLD in individuals with T2DM. Furthermore, no significant association was found between the duration of diabetes and the severity of MASLD, nor between the grading of MASLD and gender.

This study highlights a crucial association between uncontrolled diabetes and increased MASLD prevalence, emphasizing the importance of diabetes management in reducing MASLD risk.

Primary intracranial Ewing Sarcoma/peripheral Primitive Neuroectodermal Tumor (EWS/pPNET) is exceedingly rare and easy to misdiagnose.

We present a case involving a 23-year-old male who presented with headaches and vomiting. The preoperative brain imaging revealed an irregular mass in the left parietal lobe, initially misdiagnosed as meningioma. However, the surgical specimen was ultimately diagnosed as primary intracranial EWS/pPNET. The patient underwent a total tumor resection, followed by adjuvant chemotherapy and radiotherapy. No recurrence or distant metastasis was observed 18 months after the surgery.

When the imaging features of young patients’ lesions are solid, aggressive, and unevenly enhanced masses, physicians should be aware of the possibility of primary intracranial EWS/pPNET, and if possible, Gross Total Resection (GTR) and intensive chemotherapy and radiotherapy are recommended.