Current Medical Imaging Reviews最新文献

{"title":"Evaluation of Unsupervised Deformable Image Registration Using CNN and ViT on 4D-CT.","authors":"Peizhi Chen, Jialan Wang, Yifan Guo, Yan Wang","doi":"10.2174/0115734056385097251010051841","DOIUrl":"https://doi.org/10.2174/0115734056385097251010051841","url":null,"abstract":"<p><strong>Introduction: </strong>Deformable image registration is essential in medical image analysis. The state-of-the-art approaches are unsupervised methods based on convolutional neural networks (CNN) and vision transformers (ViT). While CNNs perform well in extracting local features, ViTs perform better in extracting global features.</p><p><strong>Objective: </strong>This study aimed to compare the performance of CNN and ViT in unsupervised deformable image registration.</p><p><strong>Method: </strong>We have proposed a unified registration framework and evaluated both architectures. Experiments have been conducted using 4D-CT.</p><p><strong>Results: </strong>The results have shown ViT-based registration to achieve superior performance compared to CNN-based methods.</p><p><strong>Conclusion: </strong>The findings have indicated vision transformer architectures to be more effective than convolutional networks for unsupervised deformable registration on 4D-CT data. Foundation Item: This work is supported by the National Natural Science Foundation of China (No.61801413).</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356724","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":"Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis from COVID-19 Pneumonia Using a CT-based Radiomics Nomogram.","authors":"Fengfeng Yang, Zhengyang Li, Di Yin, Yang Jing, Yang Zhao","doi":"10.2174/0115734056399950251003114150","DOIUrl":"https://doi.org/10.2174/0115734056399950251003114150","url":null,"abstract":"<p><strong>Introduction: </strong>We developed and validated a novel CT-based radiomics nomogram aimed at improving the differentiation between checkpoint inhibitor-related pneumonitis (CIP) and COVID-19 pneumonia, addressing the persistent clinical uncertainty in pneumonia diagnosis.</p><p><strong>Methods: </strong>A total of 97 patients were enrolled. CT image segmentation was performed, extracting 1,688 radiomics features. Feature selection was conducted using variance thresholding, the least absolute shrinkage and selection operator (LASSO) regression, and the Select K Best methods, resulting in the identification of 33 optimal features. Several classification models (K-Nearest Neighbors [KNN], Support Vector Machine [SVM], and Stochastic Gradient Descent [SGD]) were trained and validated using a 70:30 split and fivefold cross-validation. A radiomics nomogram was subsequently developed, incorporating the radiomics signature (Rad-score) alongside clinical factors. It was assessed based on area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA).</p><p><strong>Results: </strong>The SVM classifier exhibited the highest performance, achieving an AUC of 0.988 in the training cohort and 0.945 in the validation cohort. The constructed radiomics nomogram demonstrated a markedly improved predictive accuracy compared to the clinical model alone (AUC: 0.853 vs. 0.810 in training; 0.932 vs. 0.924 in validation). Calibration curves indicated a strong alignment of the model with observed outcomes, while DCA confirmed a greater net benefit across various threshold probabilities.</p><p><strong>Discussion: </strong>A radiomics nomogram integrated with radiomics signatures, demographics, and CT findings facilitates CIP differentiation from COVID-19, improving diagnostic efficacy.</p><p><strong>Conclusion: </strong>Radiomics acts as a potential modality to supplement conventional medical examinations.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145350019","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":"Quantitative Assessment of Liver Fibrosis: B1 Inhomogeneity-Corrected VFA T1 Mapping on Gadobenate Dimeglumine-Enhanced MRI.","authors":"Lianbang Wang, Hui Ma, Xiao Feng, Zijian Shen, Jin Cui, Ximing Wang, Gongzheng Wang, Xinya Zhao","doi":"10.2174/0115734056393310250929071043","DOIUrl":"https://doi.org/10.2174/0115734056393310250929071043","url":null,"abstract":"<p><p>Accurate early diagnosis and assessment of liver fibrosis are important for patient treatment and prognosis. This study explored the value of Gd- BOPTA-enhanced T1 mapping via the B1 inhomogeneity-corrected Variable Flip Angle (VFA) method for staging liver fibrosis in rats.</p><p><strong>Methods: </strong>Sprague‒Dawley rats were divided into one control group (n = 6) and four carbon tetrachloride-induced liver fibrosis groups (n = 6 each group). T1 mapping via B1 inhomogeneity-corrected VFA was performed before and 90 minutes after Gd-BOPTA administration. Precontrast T1 values (T1_pre), postcontrast T1 values (T1_post), and the reduction rate of T1 values (ΔT1%) were quantified on T1 mapping images. The diagnostic performance was evaluated by the Area Under the Receiver Operating Characteristic Curve (AUC). The correlations between T1pre, T1post, ΔT1% values, and the expression levels of hepatocyte transporters (Oatp1a1 and Mrp2) were evaluated.</p><p><strong>Results: </strong>T1_post and ΔT1% were significantly correlated with liver fibrosis stage (r = 0.832, p < 0.001; r = -0.798, p < 0.001, respectively), whereas T1_pre was not significantly correlated with fibrosis stage (r = 0.357, p = 0.062). The AUCs of T1_post and ΔT1% were greater than those of postcontrast signal intensity for diagnosing stages F2-F4 (0.936, 0.941 vs. 0.791; p = 0.043, 0.038, respectively), F3-F4 (0.928, 0.861 vs. 0.660; p = 0.003, 0.028, respectively) and F4 (0.965, 0.896 vs. 0.761; p = 0.021, 0.049, respectively). Oatp1a1 and Mrp2 expression levels correlated significantly with T1_post (r = -0.859, p = 0.001; r = -0.697, p = 0.017) and ΔT1% (r = 0.891, p < 0.001; r = 0.685, p = 0.020), respectively.</p><p><strong>Discussion: </strong>T1_post and ΔT1% were significantly correlated with liver fibrosis stages, and have good diagnostic performance for staging liver fibrosis. The protein expression levels of Oatp1a1 and Mrp2 correlated significantly with T1_post and ΔT1%.</p><p><strong>Conclusion: </strong>Gd-BOPTA-enhanced T1 mapping via the B1 inhomogeneity-corrected VFA shows promise as a potentially accurate and reliable tool for quantifying liver fibrosis stages.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145350005","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":"Uterine Broad Ligament Perivascular Epithelioid Cell Tumors (PEComa): A Case Report with 1-Year Follow-Up.","authors":"Siying Zhang, Chunhong Yan, Feng Chen","doi":"10.2174/0115734056397543251006112439","DOIUrl":"https://doi.org/10.2174/0115734056397543251006112439","url":null,"abstract":"<p><p><p> Introduction: This article presents a case of a patient with a broad ligament perivascular epithelioid cell tumor (PEComa), focusing on the analysis of its imaging features in CT and MRI to enhance understanding and awareness of this rare tumor. </p> <p> Case Presentation: This article reports a case of a 27-year-old married woman who was found to have a pelvic mass two years ago. After detailed examination at our hospital, imaging studies, including enhanced CT and MRI, revealed a cystic-solid lesion in the left adnexal area, with preoperative considerations of ovarian cystadenoma or uterine leiomyoma. She was referred to a specialized obstetrics and gynecology hospital for surgery, and the postoperative pathology was diagnosed as PEComa. She has been undergoing regular follow-up at our hospital post-surgery. One year after the operation, her laboratory tests showed no significant abnormalities, and imaging studies did not reveal any signs of metastasis. </p> <p> Conclusion: Uterine broad ligament PEComa is a rare tumor, and accurate imaging features and classification criteria can aid in improving preoperative diagnosis. A deeper understanding of the clinical and imaging characteristics of this rare disease is significant for enhancing diagnostic accuracy and treatment outcomes. </p>.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145337890","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":"Sentiment Analysis of Radiological Narratives and Intracranial MRI Hemorrhage Images for the Prediction of Dementia and Alzheimer's Disease.","authors":"K Balasaranya, P Ezhumalai, N R Shanker","doi":"10.2174/0115734056420799250902080814","DOIUrl":"https://doi.org/10.2174/0115734056420799250902080814","url":null,"abstract":"<p><p><p> Introduction: Intracranial hemorrhage (IH) causes dementia and Alzheimer's disease in the later stages. Until now, the accurate, early detection of IH, its prognosis, and therapeutic interventions have been a challenging task. Objective: A Multimodal Joint Fusion Sentiment Analysis (MJFSA) framework is proposed for the early detection and classification of IH, as well as sentiment analysis to support prognosis and therapeutic report generation. </p> <p> Methodology: MJFSA integrates radiological images and the radiological clinical narrative reports (RCNRs). In the proposed MJFSA model, MRI brain images are enhanced using the modified Contrast Limited Adaptive Histogram Equalization (M-CLAHE) algorithm. Enhanced images are processed with the proposed Tuned Temporal-GAN (Tuned-T-GAN) algorithm to generate temporal images. RCNRs are generated for temporal images using the Microsoft-Phi2 language model. Temporal images are processed with the Tuned-Vision Image Transformer (T-ViT) model to extract image features. On the other hand, the Bio-Bidirectional Encoder Representation Transformer (Bio-BERT) processes the RCNR texts for text feature extraction. Temporal image and RCNR text features are used for IH classification, such as intracerebral hemorrhage (ICH), epidural hemorrhage (EDH), subdural hemorrhage (SDH), and intraventricular hemorrhage (IVH), resulting in sentiment analysis for prognosis and therapeutic reports. </p> <p> Results: The MJFSA model has achieved an accuracy of 96.5% in prognosis sentiment analysis and 94.5% in therapeutic sentiment analysis. </p> <p> Discussion: The Multimodal Joint Fusion Sentiment Analysis (MJFSA) framework detects IH and classifies it using sentiment analysis for prognosis and therapeutic report generation. </p> <p> Conclusion: The MJFSA model's prognosis and therapeutic sentiment analysis report aims to support the early identification and management of risk factors associated with dementia and Alzheimer's disease. </p>.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145337924","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":"The Value of Multimodal Ultrasound Based on Machine Learning Algorithms in the Diagnosis of Benign and Malignant Thyroid Nodules of TI-RADS Category 4: A Single-Center Retrospective Study.","authors":"Minglei Ren, Zengdi Yang, Ying Fu, Zhichun Chen, Ying Shi, Yongyan Lv","doi":"10.2174/0115734056404285251006113649","DOIUrl":"https://doi.org/10.2174/0115734056404285251006113649","url":null,"abstract":"<p><p><p> Introduction: Ultrasound is routinely used for thyroid nodule diagnosis, yet distinguishing benign from malignant TI-RADS category 4 nodules remains challenging. This study has integrated two-dimensional ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) features via machine learning to improve diagnostic accuracy for these nodules. </p> <p> Methods: A total of 117 TI-RADS 4 thyroid nodules from 108 patients were included and classified as benign or malignant based on pathological results. Two-dimensional ultrasound, CEUS, and SWE were compared. Predictive features were selected using LASSO regression. Feature importance was further validated using Random Forest, SVM, and XGBoost algorithms. A logistic regression model was constructed and visualized as a nomogram. Model performance was assessed using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). </p> <p> Results: Malignant nodules exhibited significantly elevated serum FT3, FT4, FT3/FT4, TSH, and TI-RADS scores compared to benign lesions. Key imaging discriminators included unclear boundaries, aspect ratio ≥1, low internal echo, microcalcifications on ultrasound; enhancement degree, circumferential enhancement, and excretion on CEUS; and elevated SWE values (Emax, Emean, Esd, etc.) and altered CEUS quantitative parameters (PE, WiR, WoR, etc.) (all P< 0.05). A nomogram integrating four optimal predictors, including Emax, FT4, TI-RADS, and ΔPE, demonstrated robust predictive performance upon validation by ROC, calibration, and DCA curve analysis. </p> <p> Discussion: The nomogram incorporating Emax, FT4, TI-RADS, and ΔPE showed high predictive accuracy, particularly for papillary carcinoma in TI-RADS 4 nodules. Its applicability may, however, be constrained by the single-center retrospective design and limited pathological coverage. </p> <p> Conclusion: The multimodal ultrasound-based machine learning model effectively predicted malignancy in TI-RADS category 4 thyroid nodules. </p>.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145337951","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":"Multi-Scale Fusion for Real-Time Image Observation and Data Analysis of Athletes after Soft Tissue Injury.","authors":"Jinhui Li, Yang Yu, Jiaxing Han","doi":"10.2174/0115734056403181250925102654","DOIUrl":"https://doi.org/10.2174/0115734056403181250925102654","url":null,"abstract":"<p><p><p> Objective: To address insufficient segmentation accuracy in athletes' soft tissue injury analysis, this study proposes an enhanced Swin-Unet model with multiscale feature fusion via the FPN (Feature Pyramid Network) and an adaptive window selection mechanism for dynamic receptive field adjustment. </p> <p> Methods: A weighted hybrid loss function integrating Dice Loss, Cross-Entropy Loss, and boundary auxiliary loss optimizes segmentation precision and boundary recognition. </p> <p> Results: Evaluated on the OAI-ZIB dataset using 10-fold cross-validation, the model achieves a DSC (Dice Similarity Coefficient) of 0.978, outperforming baseline Swin-Unet and mainstream architectures. Superior performance is demonstrated in IoU (Intersection over Union) (0.968) and boundary Hausdorff distance (3.21), alongside significantly reduced diagnosis time (6.0 minutes vs. 16.8 minutes manually). </p> <p> Conclusion: This framework enhances real-time medical imaging analysis for athlete injuries, offering improved accuracy, efficiency, and clinical utility in soft tissue segmentation tasks. </p>.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145337928","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":"ImageJ Analysis for Transabdominal Endometrial Sonography of Single Saudi Females: A Cohort Study.","authors":"Mahasin G Hassan, Huda Alnafa, Nourah Alsofyan, Reham Alsulimi, Sumayah Alsllal, Noura Abaalkhail, Mona Alzurayr, Hessah Alamr, Tasneem Sa Elmahdi, Asma Aldahes, Mayson Wanasi, Halima Hawesa","doi":"10.2174/0115734056434787251008053407","DOIUrl":"https://doi.org/10.2174/0115734056434787251008053407","url":null,"abstract":"<p><strong>Background: </strong>Gynecological assessment of single females in some countries, where transvaginal ultrasound can not be performed, presents a challenge. This study proposed using computer-assisted analysis (ImageJ software) to assess its feasibility for endometrial analysis and consequently enhance the diagnostic value of transabdominal ultrasound.</p><p><strong>Materials and methods: </strong>This pilot normative cohort study was conducted among 20 single healthy volunteers recruited at Princess Nourah University (PNU) ultrasound lab from November 2022 to April 2023. Participants were followed throughout their entire menstrual cycle and underwent a transabdominal ultrasound in the 4 menstrual phases. Sonographs were analyzed using the ImageJ program, and the data were analyzed with SPSS software.</p><p><strong>Results: </strong>The mean age of the participants was 21 years (± 0.9), and the average menstrual cycle length was 29.65 days (±2.18). The endometrium measured 0.33 cm (±0.137), 0.63 cm (±0.172), 0.89 cm (±0.167), and 1.06 cm (±0.19) in the menstrual, early proliferative, late proliferative, and secretory phases, respectively. At the same time, the intensity score was 96.735 (±26.24), 117.4 (±27.8), 145.37 (±30.0137), and 157.3 (±21.3) in these phases. Endometrial thickness also showed a moderate positive correlation with the intensity score (r=0.545, p=0.000).</p><p><strong>Discussion: </strong>These findings, which demonstrate a correlation between the intensity score and endometrial thickness, underscore the importance of this study in providing a basis for using ImageJ software to analyze transabdominal ultrasound.</p><p><strong>Conclusion: </strong>This pilot study generated preliminary reference values for endometrial thickness and intensity score using transabdominal ultrasound. It also demonstrated a correlation between these measurements, underscoring the potential utility of ImageJ analysis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309883","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":"Application Value of Intelligent Quick Magnetic Resonance in Accelerating Brain MR Scanning Speed and Improving Image Quality for Acute Ischemic Stroke.","authors":"Bo Xue, Dengjie Duan, Junbang Feng, Zhenjun Zhao, Jinkun Tan, Jinrui Zhang, Chao Peng, Chang Li, Chuanming Li","doi":"10.2174/0115734056418245250912095159","DOIUrl":"https://doi.org/10.2174/0115734056418245250912095159","url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to evaluate the effectiveness of intelligent quick magnetic resonance (IQMR) for accelerating brain MRI scanning and improving image quality in patients with acute ischemic stroke.</p><p><strong>Methods: </strong>In this prospective study, 58 patients with acute ischemic stroke underwent head MRI examinations between July 2023 and January 2024, including diffusion-weighted imaging and both conventional and accelerated T1-weighted, T2-weighted, and T2 fluid-attenuated inversion recovery fat-saturated (T2-FLAIR) sequences. Accelerated sequences were processed using IQMR, producing IQMR-T1WI, IQMR-T2WI, and IQMR-T2-FLAIR images. Image quality was assessed qualitatively by two readers using a five-point Likert scale (1 = non-diagnostic to 5 = excellent). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of lesions and surrounding tissues were quantitatively measured. The Alberta Stroke Program Early CT Score (ASPECTS) was used to evaluate ischemia severity.</p><p><strong>Results: </strong>Total scan time was reduced from 5 minutes 9 seconds to 2 minutes 40 seconds, accounting for a reduction of 48.22%. IQMR significantly improved SNR/CNR in accelerated sequences (P < .05), achieving parity with routine sequences (P > .05). Qualitative scores for lesion conspicuity and internal display improved post-IQMR (P < .05).. ASPECTS showed no significant difference between IQMR and routine images (P = 0.79; ICC = 0.91-0.93).</p><p><strong>Discussion: </strong>IQMR addressed MRI's slow scanning limitation without hardware modifications, enhancing diagnostic efficiency. The results have been found to align with advancements in deep learning. Limitations included the small sample size and the exclusion of functional sequences.</p><p><strong>Conclusion: </strong>IQMR could significantly reduce brain MRI scanning time and enhance image quality in patients with acute ischemic stroke.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294375","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":"Sonographic and Clinicopathological Characterization of Struma Ovarii: A Retrospective Analysis for Enhanced Preoperative Diagnosis.","authors":"Jianlin Cao, Zhaowei Meng, Cuimei Li","doi":"10.2174/0115734056393689250929185251","DOIUrl":"https://doi.org/10.2174/0115734056393689250929185251","url":null,"abstract":"<p><strong>Introduction: </strong>Struma ovarii (SO) is a rare ovarian teratoma composed predominantly of thyroid tissue, often misdiagnosed due to its non-specific clinical manifestations and low prevalence.</p><p><strong>Methods: </strong>The ultrasound and clinical features of 16 histologically confirmed cases of SO (mean age 45 ± 10 years) were analyzed. Key ultrasound parameters evaluated included tumor size, internal echo patterns, calcification, blood flow (Adler grading), and pelvic effusion.</p><p><strong>Results: </strong>Half of patients with SO have been found to be postmenopausal women over 50 years of age, and that most tumors are discovered incidentally during routine examination. The large cystic components with regular margins, accompanied by calcified and vascularized solid elements, are ultrasound characteristics of SO. In particular, the presence of calcification and distinct vascular patterns on Doppler imaging (as per Adler classification) has been identified as a critical marker distinguishing SO from other adnexal masses.</p><p><strong>Discussion: </strong>Compared to existing SO research, this study has found the ultrasound characteristics of SO to mostly manifest as a large cystic echo, regular boundaries, and calcification. At the same time, compared to the existing imaging techniques, such as CT and MRI, characteristic ultrasonography has been found to be a good complement to the diagnosis of SO.</p><p><strong>Conclusion: </strong>When an adnexal tumor is classified as O-RADS 3-5 and exhibits features, such as a large cystic echo, regular boundaries, and calcification, SO should be considered in the differential diagnosis. These findings can enhance the accuracy of preoperative assessment, facilitate individualized surgical planning, and contribute to improved clinical management by reducing the likelihood of misdiagnosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294393","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}