Current Medical Imaging Reviews最新文献

{"title":"Diagnostic Value of Dual Energy Technology of Dual Source CT in Differentiation Grade of Colorectal Cancer.","authors":"Sudhir K Yadav, Nan Deng, Jikong Ma, Yixin Liu, Chunmei Zhang, Ling Liu","doi":"10.2174/0115734056360004250828115402","DOIUrl":"https://doi.org/10.2174/0115734056360004250828115402","url":null,"abstract":"<p><strong>Introduction: </strong>Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality. Accurate differentiation of tumor grade is crucial for prognosis and treatment planning. This study aimed to evaluate the diagnostic value of dual-source CT dual-energy technology parameters in distinguishing CRC differentiation grades.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 87 surgically and pathologically confirmed CRC patients (64 with medium-high differentiation and 23 with low differentiation) who underwent dual-source CT dual-energy enhancement scanning. Normalized iodine concentration (NIC), spectral curve slope (K), and dual-energy index (DEI) of the tumor center were measured in arterial and venous phases. Differences in these parameters between differentiation groups were compared, and ROC curve analysis was performed to assess diagnostic efficacy.</p><p><strong>Results: </strong>The low-differentiation group exhibited significantly higher NIC, K, and DEI values in both arterial and venous phases compared to the mediumhigh differentiation group (P < 0.01). In the arterial phase, NIC, K, and DEI yielded AUC values of 0.920, 0.770, and 0.903, respectively, with sensitivities of 95.7%, 65.2%, and 91.3%, and specificities of 82.8%, 75.0%, and 75.0%, respectively. In the venous phase, AUC values were 0.874, 0.837, and 0.886, with sensitivities of 91.3%, 82.6%, and 91.3%, and specificities of 68.75%, 75.0%, and 73.4%. NIC in the arterial phase showed statistically superior diagnostic performance compared to K values (P < 0.05).</p><p><strong>Discussion: </strong>Dual-energy CT parameters, particularly NIC in the arterial phase, demonstrate high diagnostic accuracy in differentiating CRC grades. These findings suggest that quantitative dual-energy CT metrics can serve as valuable non-invasive tools for tumor characterization, aiding in clinical decision-making. Study limitations include its retrospective design and relatively small sample size.</p><p><strong>Conclusion: </strong>NIC, K, and DEI values in dual-energy CT scans are highly effective in distinguishing CRC differentiation grades, with arterial-phase NIC showing the highest diagnostic performance. These parameters may enhance preoperative assessment and personalized treatment strategies for CRC patients.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066306","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":"Diffusion Model-based Medical Image Generation as a Potential Data Augmentation Strategy for AI Applications.","authors":"Zijian Cao, Jueye Zhang, Chen Lin, Tian Li, Hao Wu, Yibao Zhang","doi":"10.2174/0115734056401610250827114351","DOIUrl":"https://doi.org/10.2174/0115734056401610250827114351","url":null,"abstract":"<p><strong>Introduction: </strong>This study explored a generative image synthesis method based on diffusion models, potentially providing a low-cost and high-efficiency training data augmentation strategy for medical artificial intelligence (AI) applications.</p><p><strong>Methods: </strong>The MedMNIST v2 dataset was utilized as a small-volume training dataset under low-performance computing conditions. Based on the characteristics of existing samples, new medical images were synthesized using the proposed annotated diffusion model. In addition to observational assessment, quantitative evaluation was performed based on the gradient descent of the loss function during the generation process and the Fréchet Inception Distance (FID), using various loss functions and feature vector dimensions.</p><p><strong>Results: </strong>Compared to the original data, the proposed diffusion model successfully generated medical images of similar styles but with dramatically varied anatomic details. The model trained with the Huber loss function achieved a higher FID of 15.2 at a feature vector dimension of 2048, compared with the model trained with the L2 loss function, which achieved the best FID of 0.85 at a feature vector dimension of 64.</p><p><strong>Discussion: </strong>The use of the Huber loss enhanced model robustness, while FID values indicated acceptable similarity between generated and real images. Future work should explore the application of these models to more complex datasets and clinical scenarios.</p><p><strong>Conclusion: </strong>This study demonstrated that diffusion model-based medical image synthesis is potentially applicable as an augmentation strategy for AI, particularly in situations where access to real clinical data is limited. Optimal training parameters were also proposed by evaluating the dimensionality of feature vectors in FID calculations and the complexity of loss functions.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001937","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":"Artificial Intelligence-based Liver Volume Measurement Using Preoperative and Postoperative CT Images.","authors":"Kwang Gi Kim, Doojin Kim, Chang Hyun Lee, Jong Chan Yeom, Young Jae Kim, Yeon Ho Park, Jaehun Yang","doi":"10.2174/0115734056394257250818060804","DOIUrl":"https://doi.org/10.2174/0115734056394257250818060804","url":null,"abstract":"<p><strong>Introduction: </strong>Accurate liver volumetry is crucial for hepatectomy. In this study, we developed and validated a deep learning system for automated liver volumetry in patients undergoing hepatectomy, both preoperatively and at 7 days and 3 months postoperatively.</p><p><strong>Methods: </strong>A 3D U-Net model was trained on CT images from three time points using a five-fold cross-validation approach. Model performance was assessed with standard metrics and comparatively evaluated across the time points.</p><p><strong>Results: </strong>The model achieved a mean Dice Similarity Coefficient (DSC) of 94.31% (preoperative: 94.91%; 7-day post-operative: 93.45%; 3-month postoperative: 94.57%) and a mean recall of 96.04%. The volumetric difference between predicted and actual volumes was 1.01 ± 0.06% preoperatively, compared to 1.04 ± 0.03% at other time points (p < 0.05).</p><p><strong>Discussion: </strong>This study demonstrates a novel capability to automatically track post-hepatectomy regeneration using AI, offering significant potential to enhance surgical planning and patient monitoring. A key limitation, however, was that the direct correlation with clinical outcomes was not assessed due to constraints of the current dataset. Therefore, future studies using larger, multi-center datasets are essential to validate the model's clinical and prognostic utility.</p><p><strong>Conclusion: </strong>The developed artificial intelligence model successfully and accurately measured liver volumes across three critical post-hepatectomy time points. These findings support the use of this automated technology as a precise and reliable tool to assist in surgical decision-making and postoperative assessment, providing a strong foundation for enhancing patient care.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001903","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":"Smartphone-Based Anemia Screening <i>via</i> Conjunctival Imaging with 3D-Printed Spacer: A Cost-Effective Geospatial Health Solution.","authors":"A M Arunnagiri, M Sasikala, N Ramadass, G Ramya","doi":"10.2174/0115734056389602250826081355","DOIUrl":"https://doi.org/10.2174/0115734056389602250826081355","url":null,"abstract":"<p><strong>Introduction: </strong>Anemia is a common blood disorder caused by a low red blood cell count, reducing blood hemoglobin. It affects children, adolescents, and adults of all genders. Anemia diagnosis typically involves invasive procedures like peripheral blood smears and complete blood count (CBC) analysis. This study aims to develop a cost-effective, non-invasive tool for anemia detection using eye conjunctiva images.</p><p><strong>Method: </strong>Eye conjunctiva images were captured from 54 subjects using three imaging modalities such as a DSLR camera, a smartphone camera, and a smartphone camera fitted with a 3D-printed spacer macro lens. Image processing techniques, including You Only Look Once (YOLOv8) and the Segment Anything Model (SAM), and K-means clustering were used to analyze the image. By using an MLP classifier, the images were classified as anemic, moderately anemic, and normal. The trained model was embedded into an Android application with geotagging capabilities to map the prevalence of anemia in different regions.</p><p><strong>Results: </strong>Features extracted using SAM segmentation showed higher statistical significance (p < 0.05) compared to K-Means. Comparing high resolution(DSLR modality) and the proposed 3D-printed spacer macrolens shows statistically significant differences (p < 0.05). The classification accuracy was 98.3% for images from a 3D spacer-equipped smartphone camera, on par with the 98.8% accuracy obtained from DSLR camerabased images.</p><p><strong>Conclusion: </strong>The mobile application, developed using images captured with a 3D spacer-equipped modality, provides portable, cost-effective, and user-friendly non-invasive anemia screening. By identifying anemic clusters, it assists healthcare workers in targeted interventions and supports global health initiatives like Sustainable Development Goal (SDG) 3.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001956","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":"Exploring the Predictive Value of Grading in Regions Beyond Peritumoral Edema in Gliomas Based on Radiomics.","authors":"Jie Pan, Jun Lu, Shaohua Peng, Minhai Wang","doi":"10.2174/0115734056387494250823132119","DOIUrl":"https://doi.org/10.2174/0115734056387494250823132119","url":null,"abstract":"<p><strong>Introduction: </strong>Accurate preoperative grading of adult-type diffuse gliomas is crucial for personalized treatment. Emerging evidence suggests tumor cell infiltration extends beyond peritumoral edema, but the predictive value of radiomics features in these regions remains underexplored.</p><p><strong>Method: </strong>A retrospective analysis was conducted on 180 patients from the UCSF-PDGM dataset, split into training (70%) and validation (30%) cohorts. Intratumoral volumes (VOI_I, including tumor body and edema) and peritumoral volumes (VOI_P) at 7 expansion distances (1-5, 10, 15 mm) were analyzed. Feature selection involved Levene's test, t-test, mRMR, and LASSO regression. Radiomics models (VOI_I, VOI_P, and combined intratumoral-peritumoral models) were evaluated using AUC, accuracy, sensitivity, specificity, and F1 score, with Delong tests for comparisons.</p><p><strong>Results: </strong>The combined radiomics models established for the intratumoral and peritumoral 1-5mm ranges (VOI_1-5mm) showed better predictive performance than the VOI_I model (AUC=0.815/0.672), among which the VOI_1 model performed the best: in the training cohort, the AUC was 0.903 (accuracy=0.880, sensitivity=0.905, specificity=0.855, F1=0.884); in the validation cohort, the AUC was 0.904 (accuracy=0.852, sensitivity=0.778, specificity=0.926, F1=0.840). This model significantly outperformed the VOI_I model (p<0.05) and the 10/15mm combined models (p<0.05).</p><p><strong>Discussion: </strong>The peritumoral regions within 5 mm beyond the edematous area contain critical grading information, likely reflecting subtle tumor infiltration. Model performance declined with larger peritumoral distances, possibly due to increased normal tissue dilution.</p><p><strong>Conclusion: </strong>The radiomics features of the intratumoral region and the peritumoral region within 5 mm can optimize the preoperative grading of gliomas, providing support for surgical planning and prognostic evaluation.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001895","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":"Classifiers Combined with DenseNet Models for Lung Cancer Computed Tomography Image Classification: A Comparative Analysis.","authors":"Menna Allah Mahmoud, Sijun Wu, Ruihua Su, Yanhua Wen, Shuya Liu, Yubao Guan","doi":"10.2174/0115734056399377250818100506","DOIUrl":"https://doi.org/10.2174/0115734056399377250818100506","url":null,"abstract":"<p><strong>Introduction: </strong>Lung cancer remains a leading cause of cancer-related mortality worldwide. While deep learning approaches show promise in medical imaging, comprehensive comparisons of classifier combinations with DenseNet architectures for lung cancer classification are limited. The study investigates the performance of different classifier combinations, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Multi-Layer Perceptron (MLP), with DenseNet architectures for lung cancer classification using chest CT scan images.</p><p><strong>Methods: </strong>A comparative analysis was conducted on 1,000 chest CT scan images comprising Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and normal tissue samples. Three DenseNet variants (DenseNet-121, DenseNet-169, DenseNet-201) were combined with three classifiers: SVM, ANN, and MLP. Performance was evaluated using accuracy, Area Under the Curve (AUC), precision, recall, specificity, and F1- score with an 80-20 train-test split.</p><p><strong>Results: </strong>The optimal model achieved 92% training accuracy and 83% test accuracy. Performance across models ranged from 81% to 92% for training accuracy and 73% to 83% for test accuracy. The most balanced combination demonstrated robust results (training: 85% accuracy, 0.99 AUC; test: 79% accuracy, 0.95 AUC) with minimal overfitting.</p><p><strong>Discussion: </strong>Deep learning approaches effectively categorize chest CT scans for lung cancer detection. The MLP-DenseNet-169 combination's 83% test accuracy represents a promising benchmark. Limitations include retrospective design and a limited sample size from a single source.</p><p><strong>Conclusion: </strong>This evaluation demonstrates the effectiveness of combining DenseNet architectures with different classifiers for lung cancer CT classification. The MLP-DenseNet-169 achieved optimal performance, while SVM-DenseNet-169 showed superior stability, providing valuable benchmarks for automated lung cancer detection systems.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978405","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":"Indocyanine Green and Fluorescein Videoangiography for the Assessment of Collateral Circulation in Posterior Circulation Aneurysm Clipping: A Case Report and Review.","authors":"Alejandro Serrano-Rubio, Carlos-Fernando Nicolas-Cruz, Sharon Trujillo, Brenda-Susana Hernández-Barrera, Ambar-Elizabeth Riley-Moguel, Julian-Moises Enriquez-Alvarez, Ana-Margarita Martinez-Caceres, Rafael Sánchez-Mata, Daniel Figueroa-Zelaya, Ernesto Roldan-Valadez, Edgar Nathal","doi":"10.2174/0115734056256001250812075213","DOIUrl":"https://doi.org/10.2174/0115734056256001250812075213","url":null,"abstract":"<p><strong>Background: </strong>Microsurgical treatment of posterior circulation aneurysms remains challenging due to their deep location, complex anatomical exposure, and close proximity to critical neurovascular structures. Ensuring adequate collateral circulation is paramount for preventing ischemic complications. Indocyanine Green (ICG) and Fluorescein Video Angiography (FL-VAG) have emerged as effective intraoperative tools for assessing cerebral perfusion and guiding surgical decision-making.</p><p><strong>Case presentation: </strong>We report the case of a 29-year-old male presenting with a thunderclap headache, nausea, and vomiting, subsequently diagnosed with a fusiform aneurysm at the P2-P3 junction of the left posterior cerebral artery. The patient underwent a subtemporal approach with partial posterior petrosectomy for aneurysm clipping and remodeling. Initially, an STA-P3 and PITA-P3 bypass were considered; however, intraoperative ICG and FL-VAG confirmed sufficient retrograde collateral flow, allowing the bypass procedure to be avoided. Postoperative imaging demonstrated patent circulation in the occipitotemporal region without ischemic compromise.</p><p><strong>Conclusion: </strong>This case highlights the crucial role of intraoperative fluorescence imaging in refining surgical strategies for complex aneurysm clipping. ICG and FL-VAG enhance surgical precision by providing real-time perfusion assessment, reducing the need for additional vascular interventions, and improving patient outcomes.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978382","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":"Diagnostic Efficacy of PET/CT-Aided <i>versus</i> Conventional CT-guided Lung Biopsy: A Systematic Review and Meta-Analysis.","authors":"Yeonhee Lee, Sowon Jang, Minseon Kim, Junghoon Kim","doi":"10.2174/0115734056394487250702094607","DOIUrl":"https://doi.org/10.2174/0115734056394487250702094607","url":null,"abstract":"<p><strong>Introduction: </strong>Unlike its well-established role in lung cancer staging, positron emission tomography /computed tomography (PET/CT)'s role in guiding lung biopsies remains unclear and underutilized, despite its potential to distinguish metabolically active regions from areas of necrosis or fibrosis within lesions.</p><p><strong>Objective: </strong>This study aims to assess the diagnostic efficacy of PET/CT-aided <i>versus</i> conventional CT-guided lung biopsy by comparing the incidences of non-diagnostic results, false results, and complications.</p><p><strong>Methods: </strong>Studies comparing PET/CT-aided and conventional CT-guided lung biopsy were identified through an intensive search of PubMed, Embase, and the Cochrane Library. Data on nondiagnostic results, false results, and complications were extracted. Risk ratios (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model.</p><p><strong>Results: </strong>Seven studies involving 1,661 procedures were included. PET/CT-aided lung biopsy significantly reduced nondiagnostic results compared to conventional CT-guided biopsy (2.8% vs. 9.1%; pooled RR: 0.38, 95% CI: 0.20-0.70, P = 0.002). False results were also significantly fewer in the PET/CT-aided group (6.5% vs. 17.0%; pooled RR: 0.48, 95% CI: 0.35-0.65, P < 0.001). There was no statistically significant difference in overall complication rates (28.1% vs. 32.5%; pooled RR: 0.92, 95% CI: 0.77-1.10, P = 0.352), while PET/CT-aided biopsy showed a slight tendency toward fewer major complications (0.9% vs. 1.7%; pooled RR: 0.67, 95% CI: 0.30-1.44, P = 0.303).</p><p><strong>Conclusion: </strong>PET/CT-aided CT-guided lung biopsy offers advantages over conventional CT-guided lung biopsy by significantly reducing nondiagnostic and false results, without significant differences in the risk of complications.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651180","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":"Noninvasive Evaluation of the Rat Adenomyosis Model Constructed by Autologous Endometrial Implantation Using Magnetic Resonance Imaging.","authors":"Qi Zhang, Qianwen Zhu, Linghui Xu, Yujia Shen, Junhai Zhang","doi":"10.2174/0115734056375955250703115947","DOIUrl":"https://doi.org/10.2174/0115734056375955250703115947","url":null,"abstract":"<p><strong>Introduction: </strong>Dynamic changes in adenomyotic lesions in animal models have been difficult to observe and evaluate in vivo on a regular basis. Therefore, this study aims to investigate the feasibility of establishing a rat model of adenomyosis through autologous endometrial implantation and to assess the value of magnetic resonance imaging (MRI) for noninvasive evaluation of the model.</p><p><strong>Methods: </strong>Forty rats were randomly divided into two groups (20 rats in the control group, 20 rats in the model group). A rat adenomyosis model was constructed through autologous endometrial implantation. Three months after the modeling surgery, the rats underwent MRI examination, including T2-weighted axial imaging and T1-weighted axial imaging. The thickness of the uterine myometrium and junctional zone was measured. Following the MRI, the rat uterus was sliced for hematoxylin-eosin (HE) staining.</p><p><strong>Results: </strong>In the model group, lesions of adenomyosis were successfully established in all surviving rats. The myometrium of the rat uterus showed uneven thickening accompanied by scattered spotty T2 hypersignal. The junctional zone appeared as a low-signal band between the endometrium with high signal and the myometrium. The average thicknesses of both the myometrium and the junctional zone were significantly greater in the model group compared to the control group, with the differences reaching statistical significance. Ectopic endometrium can lead to hyperplasia of the peripheral muscle cells in the myometrium, which is manifested on T2-weighted images as localized thickening and hypo-intensity of the myometrium interspersed with punctiform hyperintensity. Histologically, regions of low signal intensity refer to hyperplasia of smooth muscle, while bright foci on T2-weighted images correspond to ectopic endometrial tissue and cystic dilation of glands. This study proved the noninvasive evaluation of a rat adenomyosis model and described the junctional zone in rats using MRI techniques. Histological examination using HE staining confirmed a higher nuclear-to-cytoplasmic ratio and a more compact cell arrangement in the junctional zone region of rats compared to the outer myometrium, which could explain its hypointensity.</p><p><strong>Conclusion: </strong>MRI is a valuable method for evaluating the rat adenomyosis model non-invasively. Furthermore, the successful visualization of the junctional zone in the rat uterus using MRI may have potential applications in further evaluating the progression of adenomyosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638677","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":"Non-Invasive Assessment of Rheumatoid Arthritis Cardiac Involvement: A Systematic Review of Echocardiography.","authors":"Huang Xingxing, Chen Tianyi, Yu Xiaolong","doi":"10.2174/0115734056375528250701165335","DOIUrl":"https://doi.org/10.2174/0115734056375528250701165335","url":null,"abstract":"<p><strong>Background: </strong>Rheumatoid arthritis (RA) is a systemic autoimmune disorder primarily characterized by joint degradation, with consequential cardiovascular ramifications significantly impacting patient mortality rates.</p><p><strong>Methods: </strong>We systematically searched for full-text English-language journal articles from 1973 to 2025 in the PubMed and Web of Science databases. Utilizing keywords such as \"Rheumatoid Arthritis,\" \"Autoimmune Diseases,\" \"Pathophysiology,\" \"Heart,\" \"Cardiac,\" and \"Echocardiography\" to narrow the search results. Articles related to the evaluation of heart diseases in rheumatoid arthritis by echocardiography were included, while those with insufficient data or low data quality were excluded. Study quality was assessed using the CASP Quantitative Checklist (2018 version), and data were synthesized through thematic content analysis.</p><p><strong>Results: </strong>We included 52 studies in this review after the primary analysis. The results show that traditional echocardiography can identify organic changes in the heart and ventricular function impairment of patients with rheumatoid arthritis. New ultrasound techniques, such as speckle tracking and pressure-strain loops, can detect ventricular function impairment earlier than traditional echocardiography.</p><p><strong>Discussion: </strong>Echocardiography provides complementary diagnostic information for rheumatoid arthritis cardiac involvement through structural and functional assessment, yet limitations remain. Future work should establish multimodal ultrasound frameworks and develop AI-driven analytical platforms to enhance early detection and precision management.</p><p><strong>Conclusion: </strong>The continuous progress of ultrasound technology has significantly improved the accuracy of assessing cardiac damage in patients with rheumatoid arthritis, and it has become an essential examination method for screening heart diseases in such patients, providing strong support for early diagnosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638676","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}