Mingxia Zhang, Ling Li, Meng Huo, Lei Sun, Chunyan Zhang, Ying Sun, Rengui Wang
{"title":"Imaging and Clinical Features of Primary Thoracic Lymphangioma","authors":"Mingxia Zhang, Ling Li, Meng Huo, Lei Sun, Chunyan Zhang, Ying Sun, Rengui Wang","doi":"10.2174/0115734056346925241226125948","DOIUrl":"10.2174/0115734056346925241226125948","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056346925"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuang Zhang, Yuping Qin, Meng Ding, Jining Yang, Tao Zhang
{"title":"Magnetic Resonance Imaging Study on Older Patients with Cognitive Impairment and Depression.","authors":"Shuang Zhang, Yuping Qin, Meng Ding, Jining Yang, Tao Zhang","doi":"10.2174/0115734056281104241220113235","DOIUrl":"10.2174/0115734056281104241220113235","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056281104"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Robust Approach to Early Glaucoma Identification from Retinal Fundus Images using Dirichlet-based Weighted Average Ensemble and Bayesian\u0000Optimization","authors":"Mohamed Mouhafid, Yatong Zhou, Chunyan Shan, Zhitao Xiao","doi":"10.2174/0115734056335762250128095107","DOIUrl":"10.2174/0115734056335762250128095107","url":null,"abstract":"<p><strong>Objective: </strong>Glaucoma is a leading cause of irreversible visual impairment and blindness worldwide, primarily linked to increased intraocular pressure (IOP). Early detection is essential to prevent further visual impairment, yet the manual diagnosis of retinal fundus images (RFIs) is both time-consuming and inefficient. Although automated methods for glaucoma detection (GD) exist, they often rely on individual models with manually optimized hyperparameters. This study aims to address these limitations by proposing an ensemble-based approach that integrates multiple deep learning (DL) models with automated hyperparameter optimization, with the goal of improving diagnostic accuracy and enhancing model generalization for practical clinical applications.</p><p><strong>Materials and methods: </strong>The RFIs used in this study were sourced from two publicly available datasets (ACRIMA and ORIGA), consisting of a total of 1,355 images for GD. Our method combines a custom Multi-branch convolutional neural network (CNN), pretrained MobileNet, and DenseNet201 to extract complementary features from RFIs. Moreover, to optimize model performance, we apply Bayesian Optimization (BO) for automated hyperparameter tuning, eliminating the need for manual adjustments. The predictions from these models are then combined using a Dirichlet-based Weighted Average Ensemble (Dirichlet-WAE), which adaptively adjusts the weight of each model based on its performance.</p><p><strong>Results: </strong>The proposed ensemble model demonstrated state-of-the-art performance, achieving an accuracy (ACC) of 95.09%, precision (PREC) of 95.51%, sensitivity (SEN) of 94.55%, an F1-score (F1) of 94.94%, and an area under the curve (AUC) of 0.9854. The innovative Dirichlet-based WAE substantially reduced the false positive rate, enhancing diagnostic reliability for GD. When compared to individual models, the ensemble method consistently outperformed across all evaluation metrics, underscoring its robustness and potential scalability for clinical applications.</p><p><strong>Conclusion: </strong>The integration of ensemble learning (EL) and advanced optimization techniques significantly improved the ACC of GD in RFIs. The enhanced WAE method proved to be a critical factor in achieving well-balanced and highly accurate diagnostic performance, underscoring the importance of EL in medical diagnosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056335762"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}