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FedPneu: Federated Learning for Pneumonia Detection across Multiclient Cross-Silo Healthcare Datasets
Authors: Shagun Sharma, Kalpna Guleria and Ayush DograAvailable online: 02 January 2025More LessBackground: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.
Methods: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.
Results: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.
Conclusion: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.
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Challenges in Diagnosing Primary Intracranial Ewing Sarcoma/Peripheral Primitive Neuroectodermal Tumor: A Case Report
Authors: Shigang Luo, Feifei Wang, Huan Haung and GuangCai TangAvailable online: 02 January 2025More LessBackground:Primary intracranial Ewing Sarcoma/peripheral Primitive Neuroectodermal Tumor (EWS/pPNET) is exceedingly rare and easy to misdiagnose.
Case Presentation: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.
Conclusion: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.
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Sound Touch Viscosity (STVi) for Thyroid Gland Evaluation in Healthy Individuals: A Pilot Study : STVi for Thyroid Gland Evaluation
Authors: Feng Mao, Yuemingming Jiang, Yunzhong Wang, Zhenbin Xu, Zhuo Wei, Xueli Zhu, Libin Chen and Shengmin ZhangAvailable online: 02 January 2025More LessObjective: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.
Methods: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.
Results: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).
Conclusion: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.
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Advanced CNN Architecture for Brain Tumor Segmentation and Classification using BraTS-GOAT 2024 Dataset
Authors: Vaidehi Satushe, Vibha Vyas, Shilpa Metkar and Davinder Paul SinghAvailable online: 02 January 2025More LessBackground: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.
Objective: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.
Methods: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.
Results: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.
Conclusion: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.
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Imaging Characteristics of Primary Mucinous Cystadenocarcinoma of the Breast: A Case Report and Literature Review
Authors: Yizhong Bian, Lei Xu, Yibo Zhou and Jizhen LiAvailable online: 02 January 2025More LessIntroduction: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.
Case Presentation: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.
Conclusion: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.
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Clinical Features and Ultrasonographic Manifestations of Retroperitoneal Nerve Sheath Tumors
Authors: Xiaoqing Wang, Xiaoying Zhang, Rui Zhao, Yan Liu, Chaoyang Wen and Haining ZhengAvailable online: 02 January 2025More LessObjectives: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.
Methods:A retrospective analysis of patients diagnosed with benign 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.
Results:A total of 49 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.
Conclusion: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.
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Displaced Femoral Neck Fractures Treated with Percutaneous Compression Plates in Elderly Individuals: An Effect Analysis Based on Imaging
Authors: Huli Liu, Kai Zhao, Ying Yang, Liansheng Dai, Sanjun Gu, Haifeng Li and Yu LiuAvailable online: 02 January 2025More LessBackground:The effects of percutaneous compression plate (PCP) internal fixation for femoral neck fractures (FNFs) in elderly individuals have rarely been reported. Therefore, this study aimed to investigate the efficacy of PCCP internal fixation for displaced FNFs in elderly individuals based on imaging.
Methods:The clinical data of 32 elderly patients with FNFs treated with PCCP from January 2015 to December 2022 were retrospectively analyzed. The average age of the participants was 68.7 ± 4.8 years (range, 65–80 years). Nineteen patients had Garden type III, and 13 patients had Garden type IV. Six patients had Pauwels type I, 15 patients had type II, and 11 patients had type III. Twelve patients had Singh index level IV, 14 patients had level V, and 6 patients had level VI. The time from injury to operation ranged from 3–14 days, with an average of 5.8 days. A radiological assessment was conducted. The relationships between efficacy and age, Pauwels classification, the Singh index, and the Garden alignment index were analyzed.
Results:At postoperative week 1, fracture reduction was acceptable in 31 patients. The time to start walking was 5.7 ± 3.7 days. The follow-up time ranged from 2.1 to 4 years, with an average of 2.7 years. There were 2 cases of delayed healing and no cases of nonunion or internal fixation failure. The healing time ranged from 4–8 months, with an average of 4.9 months. Fifteen patients (46.9%) showed healing with shortening of the femoral neck, and 3 patients (9.4%) had avascular necrosis (AVN). Correlation analysis revealed that healing with shortening of the femoral neck was positively correlated with age and the Singh index and that AVN was positively correlated with the Pauwels classification (p < 0.05).
Conclusion:The efficacy of PCCPs for internal fixation of displaced FNFs in elderly individuals without severe osteoporosis is satisfactory, especially for patients who can ambulate early postoperatively. The main complications are healing with shortening of the femoral neck and AVN, which are prone to occur in patients with severe osteoporosis and Pauwels type III FNFs, respectively.
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Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images
Authors: Rakesh Kumar, Vatsala Anand, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem and Ateeq Ur RehmanAvailable online: 02 January 2025More LessBackground:Colon diseases are major global health issues that often require early detection and correct diagnosis to be effectively treated. Deep learning approaches and recent developments in medical imaging have demonstrated promise in increasing diagnostic accuracy.
Objective:This work suggests that a Convolutional Neural Network (CNN) model paired with other models can detect different gastrointestinal (GI) abnormalities or diseases from endoscopic images via the fusion of residual blocks, including alpha dropouts (αDO) and auxiliary fusing layers.
Methods:To automatically diagnose colon disorders from medical images, this work explores the use of a fused deeplearning model that incorporates the EfficientNetB0, MobileNetV2, and ResNet50V2 architectures. By integrating these features, the fused model aims to improve the classification accuracy and robustness for various colon diseases. The proposed model incorporates an auxiliary fusion layer and a fusion residual block. By combining diverse features through an auxiliary fusion layer, the network can create more comprehensive and richer representations, capturing intricate patterns that might be missed by single-source processing. The fusion residual block incorporates residual connections, which help mitigate the vanishing gradient problem. By adding the input of the block directly to its output, these connections facilitate better gradient flow during backpropagation, allowing for deeper and more stable training. A wide range of endoscopic images are used to assess the proposed model, offering an accurate depiction of various disease scenarios.
ResultsThe proposed model, with an auxiliary fusion layer and residual blocks, exhibited an enormous reduction in overfitting and performance saturation. The proposed model achieved an impressive 98.03% training accuracy and 97.90% validation accuracy after evaluation, outperforming the majority of typically trained DCNNs in terms of efficiency and accuracy.
Conclusion:The proposed method developed a lightweight model that correctly identifies disorders of the gastrointestinal (GI) tract by combining advanced techniques, including feature fusion, residual learning, and self-normalization.
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Exploring the Prevalence and Coexistence of Metabolic Dysfunction-associated Steatotic Liver Disease in Type 2 Diabetes Mellitus Patients Using Ultrasound: A Cross-sectional Study
Available online: 02 January 2025More LessBackground: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.
Methods: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).
Results: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.
Conclusion:This study highlights a crucial association between uncontrolled diabetes and increased MASLD prevalence, emphasizing the importance of diabetes management in reducing MASLD risk.
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Breast Reconstruction Using Laparoscopically Harvested Pedicled Omental Flap: Imaging Findings and a Case of Recurrence Among Eight Patients
Authors: Jung Hee Byon, Soyeoun Lim, Kyoungkyg Bae and Minseo BangAvailable online: 02 January 2025More LessBackground: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.
Case Presentation: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.
Conclusion: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.
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A Retrospective Study of Ultrasound-guided Hydrodilatation of Glenohumeral Joint Combined with Corticosteroid Injection in Patients with Frozen Shoulder
Available online: 13 December 2024More LessObjectiveThe 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).
Background: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.
MethodsA 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.
ResultsPrior 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.
ConclusionUS-guided hydrodilatation of the glenohumeral joint, in combination with corticosteroid injection, may help improve the symptom and function of FS.
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"An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow"
Authors: Taukir Alam, Wei-Cheng Yeh, Fang Rong Hsu, Wei-Chung Shia, Robert Singh, Taimoor Hassan, Wenru Lin, Hong-Ye Yang and Tahir HussainAvailable online: 02 October 2024More LessIntroduction:In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy.
Methods:Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity.
Results:These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViT-B-16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95.
Conclusion:This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.
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Prenatal Three-Dimensional Ultrasound Diagnosis of Dural Sinus Arteriovenous Malformation: An Unusual Case Report
Authors: Li Qiu, Huizhu Chen, Ni Chen and Hong LuoAvailable online: 02 October 2024More LessBackground:Dural sinus arteriovenous malformation is an uncommon intracranial vascular malformation. The affected cases may suffer from severe neurological injury. Prenatal ultrasound has been used to diagnose fetal intracranial vascular abnormality, but prenatal three-dimensional (3D) ultrasound presents a very rare anomaly; an arteriovenous malformation of the dural sinus has not been reported.
Objective:This study aimed to emphasize the diagnostic value of 3D ultrasound in the fetus with dural sinus arteriovenous malformation.
Case Presentation:A 38-year-old woman was referred for targeted fetal ultrasonography at 37 weeks of gestation due to an ultrasound that showed a cystic lesion in the posterior cranial fossa. The fetus demonstrated obvious dilatation of the torcular herophili, bilateral transverse sinuses, and bilateral sigmoid sinuses, appearing as a novel bull's horn sign on 3D ultrasound. After birth, cerebral angiography confirmed the diagnosis of dural arteriovenous fistula (DAVF) in the occipital sinus region.
Conclusion:3D ultrasound is an appealing method for prenatal diagnosis of dural sinus arteriovenous malformation.
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Whether the Liver-to-Portal Vein Ratio is Applicable for Evaluating the European Society of Gastrointestinal and Abdominal Radiology Hepatobiliary Phase in Gd-EOB-DTPA-Enhanced MRI?
Authors: Chao Wang, Yancheng Song, Zhibin Pan, Guoce Li, Fenghai Liu and Xiaodong YuanAvailable online: 02 October 2024More LessPurpose:This study aimed to verify whether the Liver-to-portal Ratio (LPR) can assess the adequacy of the Hepatobiliary Phase (HBP) for patients with different liver functions.
Methods:A total of 125 patients were prospectively enrolled in the study and graded into the non-cirrhosis group (45), Child-Pugh A group (40), and Child-Pugh B/C group (40). The LPR on HBP was calculated after eight HBPs were obtained within 5-40 minutes. The adequate HBP was determined according to the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus statement. The differences in LPR and lesions’ conspicuity between 10-min HBP and adequate HBP were analyzed by paired t-test and Wilcoxon signed-rank test, respectively. The chi-square test was used to test the difference in proportion with LPR larger than 1.462 between 10-min HBP and adequate HBP.
Results:The differences in LPR and lesions’ conspicuity between 10-min HBP and adequate HBP were significant in Child-Pugh A and Child-Pugh B/C groups (P < 0.05), except for the non-cirrhosis group (P > 0.05). The differences in proportion with LPR larger than 1.462 between 10-min HBP and adequate HBP were not statistically significant in all groups (all P > 0.05).
Conclusion:The adequate HBP obtained according to the 2016 ESGAR consensus statement could provide larger LPR and better lesions’ conspicuity than 10-min HBP, especially for cirrhotic patients; however, the efficacy of using an LPR cutoff of 1.462 as the standard of the adequate HBP may be compromised in patients with cirrhosis.
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Muscle CT Radiomics is Feasible in the Identification of Gout
Authors: Ye Zeng, Chunlin Xiang and Gang WuAvailable online: 02 September 2024More LessObjective:The aim of this study was to investigate the feasibility of muscle CT radiomics in identifying gout.
Materials and Methods:A total of 30 gout patients and 20 non-gout cases with CT examinations of ankles were analyzed by using the methods of CT radiomics. CT radiomics features of the soleus muscle were extracted using the software of a 3D slicer, and then gout cases and non-gout cases were compared. The radiomics features that were significantly different between the two groups were then processed with machine learning methods. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance.
Results:Five CT radiomics features were significantly different between gout cases and non-gout cases (P < 0.05). In the logic regression, the AUC, sensitivity, specificity, and accuracy were 0.738, 77% (46/60), 70% (28/40), and 74% (74/100), respectively. In the Random forest, Xgboost, and support vector machine analysis, the accuracy was 0.901, 0.833, and 0.875, respectively.
Conclusion:From this study, it can be concluded that muscle CT radiomics is feasible in identifying gout.
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