Current Medical Imaging - Current Issue

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.

The skin, being the largest organ in the human body, plays a vital protective role. Skin lesions are changes in the appearance of the skin, such as bumps, sores, lumps, patches, and discoloration. If not identified and treated promptly, skin lesion diseases would become a serious and worrisome problem for society due to their detrimental effects. However, visually inspecting skin lesions during medical examinations can be challenging due to their similarities.

The proposed research aimed at leveraging technological advancements, particularly deep learning methods, to analyze dermoscopic images of skin lesions and make accurate predictions, thereby aiding in diagnosis.

The proposed study utilized four pre-trained CNN architectures, RegNetX, EfficientNetB3, VGG19, and ResNet-152, for the multi-class classification of seven types of skin diseases based on dermoscopic images. The significant finding of this study was the integration of attention mechanisms, specifically channel-wise and spatial attention, into these CNN variants. These mechanisms allowed the models to focus on the most relevant regions of the dermoscopic images, enhancing feature extraction and improving classification accuracy. Hyperparameters of the models were optimized using Bayesian optimization, a probabilistic model-based technique that efficiently uses the hyperparameter space to find the optimal configuration for the developed models.

The performance of these models was evaluated, and it was found that RegNetX outperformed the other models with an accuracy of 98.61%. RegNetX showed robust performance when integrated with both channel-wise and spatial attention mechanisms, indicating its effectiveness in focusing on significant features within the dermoscopic images.

The results demonstrated the effectiveness of attention-aware deep learning models in accurately classifying various skin diseases from dermoscopic images. By integrating attention mechanisms, these models can focus on the most relevant features within the images, thereby improving their classification accuracy. The results confirmed that RegNetX, integrated with optimized attention mechanisms, can provide robust, accurate diagnoses, which is critical for early detection and treatment of skin diseases.

Aneurysms, characterized by localized dilatation involving all three layers of the vascular wall, pose significant risks, with abdominal aortic aneurysm (AAA) being prevalent, particularly among the elderly. However, the cooccurrence of AAA with abdominal tortuous aorta (ATA) remains exceptionally rare.

We present the case of a 63-year-old male with an AAA extending into the iliac arteries, accompanied by ATA. Computed tomography revealed complex structural abnormalities, necessitating immediate surgical intervention. Due to the anatomical complexities, endovascular repair was not feasible, leading to a successful aortobifemoral bypass surgery using the Piljic method. The patient recovered well postoperatively, highlighting the efficacy of the chosen approach.

While AAA is often treated with endovascular repair, ATA complicates this approach, underscoring the need for open surgery in such cases. Research on aortic tortuosity's role in rupture prediction and stress alleviation shows varied findings, necessitating additional studies. ATA may also hinder vascular catheter insertion, requiring alternative routes for interventions. Future research is imperative to develop tailored treatment strategies for patients with concurrent AAA and ATA, ensuring optimal 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.

The purpose of this study was to establish the efficacy of ultrasound (US)-guided hydrodilatation of the glenohumeral joint, in conjunction with corticosteroid injection, in alleviating pain and improving shoulder joint adhesion among patients with primary frozen shoulder (FS).

FS, also known as adhesive capsulitis, is a pathological condition characterized by pain and potential functional impairment. The natural progression of FS involves three distinct stages: freezing, frozen, and thawing. Chronic pain in FS patients can lead to aseptic inflammation, thickening of fibroblasts, and an abundance of type I and III collagen fibers in the vicinity of the glenohumeral joint, ligaments, and tendons. This condition significantly impacts patients' quality of life.

A total of 200 FS patients were enrolled in this study. All participants underwent US-guided hydrodilatation of the glenohumeral joint, combined with corticosteroid injection, at our department. Pre- and post-treatment (1 year) ultrasound measurements were recorded for the thickness of the axillary recess capsule (ARC), coracohumeral ligament (CHL), and subacromial bursa. Additionally, the numerical rating scale (NRS) and Constant-Murley score (CMS) were assessed to evaluate pain intensity and shoulder function, respectively.

Prior to the commencement of treatment, the measurements indicated a thickness of 4.8±2.3 mm for the ARC, 4.2±1.7 mm for the CHL, and 3.9±1.4 mm for the subacromial bursa. Additionally, the preoperative assessment using the NRS scale for pain yielded a score of 6.4±2.0, while the CMS score for the joint function was 35.8±8.5. Following one year of treatment, a notable decrease was observed in the thickness of ARC, CHL, and subacromial bursa. Furthermore, significant improvements were recorded in both the pain NRS score and the CMS score.

US-guided hydrodilatation of the glenohumeral joint, in combination with corticosteroid injection, may help improve the symptom and function of FS.

The objective of this study was to summarize the findings of children’s intracranial congenital or developmental malformations found during imaging procedures in the Tibetan plateau.

We retrospectively reviewed the imaging data of the suspected patients who presented with the central nervous system (CNS) malformations and were enrolled either through the clinic or after ultrasound examinations between June 2019 and June 2023 in our institution. All imaging data were interpreted by two experienced radiologists through consensus reading.

In this study, we recruited 36 patients, including two neonates, 17 infants and 17 children. Seven cases underwent an MRI examination, while the others had a CT scan. Polygyria and pachygyria malformation were the most common type of congenital neurological malformations (7 cases, 31.8%), followed by cystic changes of the cerebral parenchyma (3 cases, 13.6%). Cerebral atrophy was the most common type of secondary CNS abnormality (8 cases, 57.1%), followed by communicative hydrocephalus (3 cases, 21.4%). Five patients in the congenital group and 4 patients in the secondary group had complex malformations. In the current study group, there were 8 deaths, 12 cases with neurological sequelae, 1 case with normal development, and 15 cases lost to follow-up. There were no significant differences between the primary and secondary CNS groups in terms of the outcome for both the infants and children groups.

CNS malformations in the Tibetan Plateau are associated with high mortality and morbidity rates. Better utilization of imaging modalities could help design tailored treatments as early as possible.

Perforation is one of the rarest effects of Meckel's diverticulum and may clinically resemble acute appendicitis.

A 34-year-old woman with pain in the right iliac fossa, nausea, and vomiting for three days was brought to the emergency department. An abdominal examination indicated rebound tenderness in the area of the right iliac fossa. Abdominal ultrasound showed a heterogeneous lesion in the left iliac fossa measuring 5 cm × 3.5 cm × 4 cm with no internal vascularity. Abdominal Computed Tomography (CT) demonstrated a hypodense lesion located left of the midline of the abdomen, which was inseparable from the small bowel at the antimesenteric border. Laparoscopic exploration was performed, and an intraoperative diagnosis of perforated Meckel’s diverticulum with phlegmon formation was made. The patient had an uneventful recovery.

Radiologists should be aware of the possibility of complicated Merkel's diverticulum when encountering cases of acute abdominal pain. If there is a lower abdominal inflammatory process and a normal appendix is identified, there should be a high degree of suspicion when examining the CT scan.

Retroperitoneal nerve sheath tumors are uncommon, representing a small fraction of all primary retroperitoneal neoplasms. Accurate differentiation between benign and malignant forms is essential for optimal clinical management. This study assessed the clinical profiles and sonographic traits of retroperitoneal nerve sheath tumors with the goal of enhancing diagnostic precision and developing therapeutic strategies.

A retrospective analysis of patients diagnosed with retroperitoneal nerve sheath tumors who completed surgical treatment and underwent ultrasound imaging was carried out. Tumors were classified based on sonographic features and blood flow characteristics as per Adler's grading system. Statistical analysis was performed using SPSS 25.0. ROC curve analysis was carried out to determine the optimal diagnostic cutoff values.

A total of 57 patients were included in the study. There were no significant variances in age, gender, or tumor localization among the groups. However, pronounced disparities were observed in tumor number, size, shape, definition of borders, internal echo pattern, structural composition, presence of calcification, and blood flow signals between the classic and malignant groups. Notably, malignant tumors tended to manifest as larger masses with indistinct margins and irregular shapes. The maximum tumor diameter emerged as a discriminating factor for malignancy, with a diagnostic cutoff of 9.9 cm, yielding an AUC of 0.754 from the ROC curve analysis.

This study outlines the distinctive clinical and sonographic features of retroperitoneal nerve sheath tumors, with a particular focus on malignant subtypes. Ultrasonography emerges as a valuable diagnostic tool, contributing to the differentiation of tumor categories and potentially to the development of targeted treatment strategies. The identification of specific sonographic markers may facilitate the early detection and detailed characterization of these tumors, which could contribute to improved patient outcomes.

Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM) into a hybrid model (SVMVGGNet-16), leveraging the strengths of both models for high accuracy and reliability in classifying lung cancer types in different 4 classes such as adenocarcinoma (ADC), large cell carcinoma (LCC), Normal, and squamous cell carcinoma (SCC).

Using the LIDC-IDRI dataset, we pre-processed images with a median filter and histogram equalization, segmented lung tumors through thresholding and edge detection, and extracted geometric features such as area, perimeter, eccentricity, compactness, and circularity. VGGNet-16 and SVM employed for feature extraction and classification, respectively. Performance matrices were evaluated using accuracy, AUC, recall, precision, and F1-score. Both VGGNet-16 and SVM underwent comparative analysis during the training, validation, and testing phases.

The SVMVGGNet-16 model outperformed both, with a training accuracy (97.22%), AUC (99.42%), recall (94.22%), precision (95.28%), and F1-score (94.68%). In testing, our SVMVGGNet-16 model maintained high accuracy (96.72%), with an AUC (96.87%), recall (84.67%), precision (87.40%), and F1-score (85.73%).

Our experimental results demonstrate the potential of SVMVGGNet-16 in improving diagnostic performance, leading to earlier detection and better treatment outcomes. Future work includes refining the model, expanding datasets, conducting clinical trials, and integrating the system into clinical practice to ensure practical usability.

Axitinib is a selective inhibitor of vascular endothelial growth factor receptors and is used in the treatment of many malignancies. Herein, we reported a rare case with axitinib-induced retinal changesand associated toxicity.

A forty-five-year-old female presented with blurred vision who had been taking 7 mg of Axitinib bid for 5 months. Initial Best Corrected Visual Acuity (BCVA) was 20/32 at the right and counting fingers at the left eye. Funduscopic examination revealed bilaterally widespread intraretinal hemorrhages, cotton-wool spots, and hard exudates with a star-like appearance at the macula. The optical coherence tomography revealed central macular edema. There was hyperreflective edema in the inner layers, exudates in the middle retinal layers, and subfoveal subretinal fluid. Fundus fluorescein angiography revealed localized ischaemic findings in the early phase and multifocal perivascular ink-blot fluorescein leakage in the middle and late phases. Axitinib treatment was discontinued immediately, and at the third month of follow-up, the macular edema and fundus findings improved with a final BCVA of 20/20 at the right and 20/32 at the left eye.

Considering the ocular side effects of the patients receiving axitinib is crucial to prevent any potentially persistent visual loss.

Spinal image denoising plays a vital role in the accurate diagnosis of disc herniation (DH).

Traditional denoising algorithms perform less due Limited Directional Selectivity problem and do not adequately capture directional information in pixels. Traditional algorithms' edge representation and texture details are insufficient for the earlier detection of DH. Limited Directional Selectivity leads to inaccurate diagnosis and classification of Disc Herniation (DH) stages. The DH stages are (i) Degeneration (ii) Prolapse (iii) Extrusion and (iv) Sequestration. Moreover, detection of DH size below 2mm using MR image is the major problem.

To solve the above problem, spinal cord MR images fed to the proposed Parrot optimization tuned Denoising Convolutional Neural Network (Po-DnCNN) algorithm for perspective enhancement of nucleus pulposus region in the spinal cord, vertebrae. The perspective enhancement of Spinal cord image led to the accurate classification of stages and earlier detection of DH by using the proposed Hippopotamus optimization- Fast Hybrid Vision Transformer (Ho–FastViT) algorithm. For this study, spinal cord MR images are obtained from the Grand Challenge website – SPIDER dataset.

The proposed Po-DnCNN method and Ho-FastViT results are analysed quantitatively and qualitatively based on the edge, contrast, classification of the stage, and enhancement of the projected nucleus pulposus region in the spinal cord and vertebrae. The predicted DH results using the proposed method are compared with the manual Pfirrman Grade value of the spinal card method.

Proposed method is better than traditional methods for earlier detection of DH. Po-DnCNN and Ho-FastViat methods give high accuracy of about 98% and 97% compared to traditional methods.

Patients with Turner syndrome need hormone replacement therapy for puberty induction. However, it is not known whether inadequate hormone replacement therapy affects the pituitary.

Patients with Turner syndrome (n=35) and healthy control (n=20) (age/gender matched) subjects were included. MRI imaging of the pituitary was used to calculate pituitary volumes. According to the estradiol regimen, patients were divided into two groups; (i) those treated with low-dose conjugated oestrogen (CE, 0.625 mg) and (ii) those treated with combination therapy (ethinyl estradiol+sipropterone acetate; 35 mcg/2 mg). Pituitary measurements were calculated according to pituitary borders and their distances to each other via pituitary MRI.

Pituitary hyperplasia (0.58±0.15 cm³vs. 0.40±0.17 cm³) was determined in patients with low dose conjugated estrogen compared to the other patients or healthy control subjects (0.42±0.16 cm³) (p=0.005). Serum FSH levels of the patients treated with low dose CE were also higher compared to the patients who received combination therapy (p=0.001).

Inadequate hormone replacement therapy can cause devastating effects on the bones and uterine health and disrupts the pituitary structure.

Hypoxic-ischemic encephalopathy (HIE) is a brain injury that is caused by improper oxygen/blood flow. None of the existing works have concentrated on detecting HIE based on the sub-acute injury in the brain.

To enhance the accuracy and specificity of HIE detection, a comprehensive approach that includes SAI identification, BGT segmentation, and volume calculation will be utilized.

This study addresses the critical challenge of detecting hypoxic-schemic encephalopathy (HIE) through advanced image processing techniques applied to brain MRI data. The primary research questions focus on the effectiveness of the proposed CO-GW-RNN method in accurately identifying HIE and the impact of incorporating segmentation and clustering processes on detection performance.

The proposed method achieved remarkable results, demonstrating an accuracy of 98.98% and a specificity of 98.17%, significantly outperforming existing techniques such as the RUB classifier (84.6% accuracy) and the DTL method (94.00% accuracy).

These findings validate the effectiveness of the proposed methodology in improving HIE detection in brain MRI images.

This study aimed to compare automated three-dimensional Intrapulmonary Vessel Volume (IPVV) differences between lung and mediastinal windows in healthy individuals using quantitative measurements obtained from chest Computed Tomography (CT) plain scans.

A total of 258 participants (aged 21–83 years) with negative chest CT scans from routine physical examinations conducted between January to November 2023 were retrospectively enrolled. For each healthy participant, an algorithm was used to automatically extract total lung IPVVs as well as IPVVs for vessels of specific diameter. Differences in IPVVs were then compared between those extracted using the lung window and those extracted using the mediastinal window.

The IPVVs for the entire lung, intrapulmonary arteries, intrapulmonary veins, and small pulmonary vessels (categorized by different diameters) extracted from the lung window were significantly higher than those extracted from the mediastinal window (p<0.01). No significant sex-based differences in IPVV were observed for pulmonary arteries and veins with diameters between 0.8 and 1.6 mm, as well as pulmonary veins with diameters between 2.4 and 3.2 mm. However, in pulmonary arteries and veins with diameters between 1.6 and 2.4 mm, females had significantly higher IPVVs than males. In all other cases, IPVVs were larger in males than in females.

This method of automatic IPVV extraction and quantitative assessment has been proven to be feasible. Automated IPVV expression effectively identified morphological characteristics of intrapulmonary vessels. The study has concluded IPVVs extracted from the lung window to be generally larger than those extracted from the mediastinal window.

Early diagnosis of prostate cancer can improve the survival rate of patients on the premise of high-quality images. The prerequisite for early diagnosis is high-quality images. ZOOMit is a method for high-resolution, zoomed FOV imaging, allowing diffusion-weighted images with high contrast and resolution in short acquisition times. RESOLVE DWI is an advanced MRI technique developed to obtain high-resolution diffusion-weighted images with reduced susceptibility-related artifacts.

This study aimed to compare the image quality of conventional single-shot Echo-planar Imaging (ss-EPI) Diffusion-weighted Imaging (DWI), zoomed FOV imaging (ZOOMit) DWI, and readout segmentation of long variable echo-trains (RESOLVE) DWI sequences for prostate imaging, and optimize the strategy to obtain high-quality Magnetic Resonance Imaging (MRI) in order to discriminate malignant and benign prostate diseases.

Fifty-one patients were enrolled, including 31 with prostate cancer, 11 with prostate benign disease, and 9 with bladder cancer. Patients underwent MRI scans using T2-weighted (T2W), ss-EPI DWI, ZOOMit DWI, and RESOLVE DWI (b = 0, 50, 1400 s/mm²) sequences using a 3.0T MRI scanner. Subjective scores of image quality were evaluated by two independent radiologists. Differences in the subjective scores and objective parameters among the three sequences were compared. The agreement and consistency between the findings of the two raters were evaluated with Kappa or Intra-class Correlation Coefficient (ICC). Receiver Operating Characteristic (ROC) curves were used to distinguish malignant and benign prostate disease.

The agreement of subjective scores of 51 patients was high or moderate between the two radiologists (kappa: 0.529–0.880). ZOOMit displayed the highest clarity and the lowest distortion and artifacts compared to ss-EPI and RESOLVE. The two radiologic technicians obtained moderate or high consistency of objective measurement (ICC: 0.527–0.924). In the ROC analysis, ADCmean and Prostate Imaging Reporting and Data System (PI-RADS) scores for three sequences were comparable in differentiating prostate cancer from benign prostate disease (all p>0.05), in which ZOOMit indicated the highest Area Under the Curve (AUC) (0.930 and 0.790, respectively).

Compared to the other two sequences, ZOOMit can be deemed preferable to improve prostate MRI diffusion imaging as it has exhibited the highest AUC in identifying prostate cancer.

Screening ultrasound proves to be remarkably beneficial in pre-hospital settings, particularly in geographically remote areas with technological constraints and no medical specialties. Urological pathology has a high frequency of occurrence in the emergency department and is part of the wide range of occurrences that can benefit from this ultrasound screening as a clinical guide for patients.

In this case, a patient experiencing lower abdominal pain and symptoms of renal colic sought assistance at a basic emergency service facility. Utilizing a renal screening ultrasound executed by a sonographer, the clinical team identified images indicative of a significant bladder calculus. Subsequently, the patient was referred to a referral hospital for a comprehensive evaluation by medical specialties.

The images obtained in both health units exhibited congruence, indicating that the screening ultrasound, while not intended to replace the specialized orthodox ultrasound executed by a radiologist, served as a crucial tool for diagnostic presumption, providing consistency in clinical decision-making for referring patients. This capability allowed emergency physicians to promptly transfer a patient requiring urgent further investigation to a referral hospital with compelling and substantiated data. This shift in the approach to patient triage in a remote setting could enhance patient safety.

This prospective study aimed to establish the typical viscosity range of the thyroid gland in healthy individuals using a new method called the Sound Touch Viscosity (STVi) technique with a linear array transducer.

Seventy-eight healthy volunteers were enrolled between March, 2023 and April, 2023. Thyroid viscosity was measured using the Resona R9 ultrasound system equipped with a linear array transducer (L15-3WU). Each patient had three valid viscosity measurements taken for each thyroid lobe, and the average values were analyzed. Thyroid gland stiffness was measured and analyzed simultaneously.

The study included 51 women and 27 men with an average age of 48 years. The mean viscosity measurement for a normal thyroid gland was 1.10 ± 0.41 Pa.s (ranging from 0.38 to 2.25 Pa.s). There were no significant differences in viscosity between the left and right lobes of the thyroid gland. We found no significant variations in viscosity based on gender, age, or body mass index (BMI). There was a notable positive correlation between thyroid viscosity and stiffness measurements (r = 0.717, p < 0.001).

Our findings suggest that STVi is a highly reliable method for assessing the thyroid. This technique holds promise as a new, non-invasive approach to evaluating thyroid parenchyma viscosity.

Patients with abdominal Pheochromocytoma and Paraganglioma (PPGL) are prone to a hypertensive crisis during surgery, which may endanger their lives. This study aimed to develop and validate a Computed Tomography (CT) radiomics nomogram for the prediction of intraoperative hypertensive crisis in patients with PPGL.

In this retrospective study, 212 patients with abdominal PPGL underwent abdominal-enhanced CT and surgical resection. Radiomic features were extracted from arterial and venous phases. Multivariable logistic regression models were developed using an internal validation and an external test set. The performance of the nomograms was determined by their discrimination, calibration, and clinical usefulness.

A total of 212 patients with PPGL were included, involving 44 with hypertensive crises. The patients were divided into training (n = 117), validation (n = 51), and test (n = 44) sets. Eighteen radiomics-relevant radiomic features were selected. A history of coronary heart disease and the CT radiomics score were included in the prediction model, which achieved an area under the curve of 0.91 [95% Confidence Interval (CI) 0.85-0.97] in the training set, 0.93 (95% CI 0.84-0.99) in the validation set, and 0.85 (95% CI 0.72-0.97) in the test set. The decision curve analysis demonstrated the radiomics nomogram to be clinically useful.

Our study has developed and validated a CT radiomics nomogram that has demonstrated remarkable potential in predicting intraoperative hypertensive crisis in patients with abdominal pheochromocytoma and paraganglioma. This non-invasive, straightforward approach has exhibited high accuracy, ease of use, and predictive power.

This paper examines the impact of transfer learning and CLAHE (Contrast Limited Adaptive Histogram Equalization) optimization on the classification of medical images, specifically brain images.

Four different setups were tested: normal images without data augmentation, normal images with data augmentation, CLAHE-processed images without data augmentation, and CLAHE-processed images with data augmentation.

The results show that using CLAHE combined with data augmentation significantly improves classification accuracy. Specifically, the combination of CLAHE and data augmentation achieved a precision of 0.90, a recall of 0.87, an F1-score of 0.89, and an accuracy of 0.86, outperforming the other setups.

These findings highlight the effectiveness of CLAHE optimization in the context of transfer learning, particularly when data augmentation techniques are also applied, leading to an overall improvement in the performance of brain image classification models.

Extrahepatic Common Bile Duct Obstruction (EHBDO) is a serious condition that requires accurate diagnosis for effective treatment. Magnetic Resonance Cholangiopancreatography (MRCP) is a widely used noninvasive imaging technique for visualizing bile ducts, but its interpretation can be complex.

This study aimed to develop a deep learning-based classification model that integrates MRCP images and clinical parameters to assist radiologists in diagnosing EHBDO more accurately.

A total of 465 patients with clinical data were included, of whom 143 also had MRCP images. Missing clinical values were addressed through data imputation. Object detection techniques were used to isolate the common bile duct region in the MRCP images. A multimodal deep learning fusion model was developed by combining the extracted imaging features with selected clinical parameters. To account for the varying significance of different features, a weighted loss function was applied. The performance of the fusion model was compared to that of single-modality approaches (using only MRCP images or clinical data), specifically the accuracy, sensitivity, specificity, and Area Under The Curve (AUC).

The performance of the proposed deep learning fusion model was superior to that of models using only MRCP images or clinical parameters. The fusion model achieved an accuracy of 89.8%, AUC of 90.4%, sensitivity of 81.8%, and specificity of 95.7% in diagnosing EHBDO. By integrating MRCP imaging data and clinical parameters, the proposed deep learning model significantly enhanced the accuracy of EHBDO diagnosis.

This proposed multimodal approach outperformed traditional single-modality methods, presenting a valuable tool for improving the diagnostic accuracy of bile duct obstruction.

This study aims to evaluate the clinical efficacy of spectral dual-energy detector computed tomography (SDCT) and its associated parameters in diagnosing acute pulmonary embolism (APE).

Retrospective analysis of imaging data from 86 APE-diagnosed patients using SDCT was conducted. Virtual monoenergetic images (VMIs) at 40, 70, and 100 KeV, Iodine concentration (IC) maps, Electron Cloud Density Map (ECDM), Effective atomic number (Z-eff) maps, and Hounsfield unit attenuation plots (VMI slope) were reconstructed from pulmonary artery phase CT images. The subtraction (SUB) and ratios of VMIs were calculated, and two experienced radiologists evaluated the patients. The Mann-Whitney U test assessed the parameter ability to differentiate between normal and obstructed lung fields and detect emboli in the pulmonary artery. Receiver Operating Characteristic Curves (ROC) were generated for performance evaluation.

Significant differences (p<0.001) in 40KeV, Ratio, SUB, and Z-eff were found between normal and embolized lung fields. Logistic regression demonstrated good detection performance for Z-eff (AUC=0.986), SUB (AUC=0.975), and IC (AUC=0.974). Parameters such as 40KeV (AUC=0.990), 70KeV (AUC=0.980), 100KeV (AUC=0.962), SUB (AUC=0.990), Z-eff (AUC=0.999), and IC (AUC=1.000) exhibited good detection capabilities for identifying emboli in the pulmonary artery.

SDCT facilitates the identification of diseased lung fields and localization of emboli in the pulmonary artery, thereby improving diagnostic efficiency in APE.

Laparoscopically Harvested Pedicled Omental Flap (LHPOF) has become a viable option for breast reconstruction due to advancements in minimally invasive techniques, offering benefits like reduced postoperative pain and minimal scarring.

This study examines the imaging findings in eight patients who underwent breast reconstruction using a LHPOF. Imaging modalities, including mammography, ultrasonography, MRI, and CT, consistently showed reconstructed breasts with fat replacing glandular tissue and numerous internal vessels. One case of recurrence was detected, demonstrating the efficacy of conventional surveillance imaging studies in facilitating the detection of recurrences.

This is the first report detailing imaging findings of breast reconstruction using an LHPOF, including a recurrence case. Understanding these imaging results is crucial for effective surveillance in breast cancer patients with omental flap reconstruction.