BMC Medical Imaging最新文献

{"title":"Magnetic resonance imaging features of epididymal and/or testicular tuberculosis: a case series.","authors":"Bowen Yang, Renbing Zhou, Xiaohong Wang, Yan Li, Panxia Wang, Yue Hao, Wenwen Li, Lei Zhang, Wenjing Su, Jie Qin, Ya Qiu, Junyang Luo","doi":"10.1186/s12880-025-01699-w","DOIUrl":"10.1186/s12880-025-01699-w","url":null,"abstract":"<p><strong>Background: </strong>Tuberculosis (TB) is a global health burden, and extrapulmonary TB, particularly urogenital TB, is a significant concern in males. Given the nonspecific clinical manifestations of epididymal and/or testicular TB, this study characterizes the MRI features of this condition to facilitate earlier and more accurate diagnosis.</p><p><strong>Methods: </strong>This retrospective study was approved by the ethics committee. We included 14 patients with epididymal and/or testicular TB (diagnosed between January 2015 and September 2024) who underwent contrast-enhanced MRI scans on a 1.5-T scanner. MRI features and clinical characteristics were analyzed by two experienced radiologists.</p><p><strong>Results: </strong>Among these 14 patients (median age, 44.5 years), 78.6% of them had epididymal TB with or without testicular involvement, while 21.4% had isolated testicular TB. The most common local symptom was a painful scrotal mass (85.7%), and 64.3% reported fever. TB in other sites was identified in 71.4% patients. T lymphocyte spot test was positive in 57.1% patients, and pathological confirmation was obtained in 42.9%. Most lesions (71.4%) were unilateral. On T1-weighted images, 50% of lesions were isointense and 42.9% were mildly hyperintense. T2-weighted imaging showed hypointense signals in 64.3% of cases. All lesions appeared hyperintense on diffusion-weighted imaging, with 92.9% showing restricted diffusion. Heterogeneous or annular enhancement was observed in 85.7% of lesions. Hydrocele was present in all patients, and 21.4% had abscess formation or fistula.</p><p><strong>Conclusions: </strong>MRI provides valuable soft-tissue characterization for diagnosing epididymal and/or testicular TB.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"157"},"PeriodicalIF":2.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12067958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.","authors":"Hafsa Laçi, Kozeta Sevrani, Sarfraz Iqbal","doi":"10.1186/s12880-025-01701-5","DOIUrl":"https://doi.org/10.1186/s12880-025-01701-5","url":null,"abstract":"<p><p>Medical images occupy the largest part of the existing medical information and dealing with them is challenging not only in terms of management but also in terms of interpretation and analysis. Hence, analyzing, understanding, and classifying them, becomes a very expensive and time-consuming task, especially if performed manually. Deep learning is considered a good solution for image classification, segmentation, and transfer learning tasks since it offers a large number of algorithms to solve such complex problems. PRISMA-ScR guidelines have been followed to conduct the scoping review with the aim of exploring how deep learning is being used to classify a broad spectrum of diseases diagnosed using an X-ray, MRI, or Ultrasound image modality.Findings contribute to the existing research by outlining the characteristics of the adopted datasets and the preprocessing or augmentation techniques applied to them. The authors summarized all relevant studies based on the deep learning models used and the accuracy achieved for classification. Whenever possible, they included details about the hardware and software configurations, as well as the architectural components of the models employed. Moreover, the models that achieved the highest accuracy in disease classification were highlighted, along with their strengths. The authors also discussed the limitations of the current approaches and proposed future directions for medical image classification.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"156"},"PeriodicalIF":2.9,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid method for automatic initialization and segmentation of ventricular on large-scale cardiovascular magnetic resonance images.","authors":"Ning Pan, Zhi Li, Cailu Xu, Junfeng Gao, Huaifei Hu","doi":"10.1186/s12880-025-01683-4","DOIUrl":"https://doi.org/10.1186/s12880-025-01683-4","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular diseases are the number one cause of death globally, making cardiac magnetic resonance image segmentation a popular research topic. Existing schemas relying on manual user interaction or semi-automatic segmentation are infeasible when dealing thousands of cardiac MRI studies. Thus, we proposed a full automatic and robust algorithm for large-scale cardiac MRI segmentation by combining the advantages of deep learning localization and 3D-ASM restriction.</p><p><strong>Material and methods: </strong>The proposed method comprises several key techniques: 1) a hybrid network integrating CNNs and Transformer as a encoder with the EFG (Edge feature guidance) module (named as CTr-HNs) to localize the target regions of the cardiac on MRI images, 2) initial shape acquisition by alignment of coarse segmentation contours to the initial surface model of 3D-ASM, 3) refinement of the initial shape to cover all slices of MRI in the short axis by complex transformation. The datasets used are from the UK BioBank and the CAP (Cardiac Atlas Project). In cardiac coarse segmentation experiments on MR images, Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) are used to evaluate segmentation performance. In SPASM experiments, Point-to-surface (P2S) distances, Dice score are compared between automatic results and ground truth.</p><p><strong>Results: </strong>The CTr-HNs from our proposed method achieves Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) of 0.95, 0.10 and 1.54 for the LV segmentation respectively, 0.88, 0.13 and 1.94 for the LV myocardium segmentation, and 0.91, 0.24 and 3.25 for the RV segmentation. The overall P2S errors from our proposed schema is 1.45 mm. For endocardium and epicardium, the Dice scores are 0.87 and 0.91 respectively.</p><p><strong>Conclusions: </strong>Our experimental results show that the proposed schema can automatically analyze large-scale quantification from population cardiac images with robustness and accuracy.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"155"},"PeriodicalIF":2.9,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a CT-based radiomics nomogram for predicting progression-free survival in patients with small cell lung cancer.","authors":"Nan Yang, Zhuang Xuan Ma, Xin Wang, Li Xiao, Liang Jin, Ming Li","doi":"10.1186/s12880-025-01691-4","DOIUrl":"https://doi.org/10.1186/s12880-025-01691-4","url":null,"abstract":"<p><strong>Purpose: </strong>Small cell lung cancer (SCLC) is a highly aggressive form of lung cancer, representing about 15% of cases worldwide. Despite advances in imaging, such as low-dose CT, which have increased diagnostic rates, survival outcomes for SCLC patients have remained stagnant. Recent studies have only focused on radiomics, which extracts detailed quantitative features from imaging, with clinical risk factors to improve prognostic models. Therefore, this study aimed to develop a clinical-radiomics fusion nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients diagnosed with SCLC. By integrating radiomics features extracted from CT with clinical data, this model provides personalized prognostic assessment for clinicians. Its clinical utility lies in aiding treatment decision-making by offering more accurate prognostic evaluation, optimizing therapeutic strategies, and identifying high-risk patients at an early stage, ultimately improving overall survival and quality of life.</p><p><strong>Methods: </strong>To develop the nomogram model, 95 patients diagnosed with pathologically confirmed SCLC between January 1, 2013, and December 31, 2023, were included in the study cohort. Participants were randomly divided into training and validation cohorts in a 7:3 ratio. Radiomics features associated with PFS were generated using the least absolute shrinkage and selection operator (LASSO) along with univariate and multivariate analyses. Additionally, in the training cohort, both univariate and multivariate analyses using Cox regression were conducted to identify the significant clinical risk factors influencing PFS. The predictive performance of the clinical and clinical-radiomics fusion nomogram were evaluated using the concordance index, calibration plots, and decision curve analysis (DCA).</p><p><strong>Results: </strong>Five radiomics features were selected and used to calculate the radiomics score (Rad-score). The radiomics features were significantly associated with PFS (hazard ratio: 0.5765, 95% confidence interval: 0.3641-0.9128, p < 0.05). Three clinical risk factors significantly associated with PFS were identified: neuron-specific enolase (NSE), carbohydrate antigen 125 levels (CA125), and treatment type, such as surgery. The clinical-radiomics fusion nomogram model (C-index:0.744) demonstrated superior performance compared to the clinical nomogram model (C-index: 0.718) in the training cohort. DCA indicated that the clinical-radiomics fusion nomogram outperformed the clinical nomogram in terms of clinical usefulness.</p><p><strong>Conclusions: </strong>A CT-based clinical-radiomics fusion nomogram was developed to predict PFS in patients with SCLC, which is useful in providing individualized information.</p><p><strong>Advances in knowledge: </strong>A clinical-radiomics fusion nomogram was constructed to estimate the probability of PFS based on clinical risk factors and the rad-score.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"154"},"PeriodicalIF":2.9,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of glycemic control on brain microstructure in type 2 diabetes mellitus: insights from diffusion tensor imaging.","authors":"Guanye Zhang, Huanhua Wu, Qian Cao, Jiabin Mo, Xiaozheng Cao, Hong Luo, Xiangyu Tan, Hongru Ou","doi":"10.1186/s12880-025-01696-z","DOIUrl":"https://doi.org/10.1186/s12880-025-01696-z","url":null,"abstract":"<p><strong>Background: </strong>Type 2 diabetes mellitus (T2DM) has been associated with brain microstructural alterations, potentially contributing to cognitive decline and neurodegeneration. Diffusion tensor imaging (DTI) provides a non-invasive method to assess these changes. However, the relationship between glycemic control and brain microstructural integrity remains unclear. This study aims to investigate the association between glycemic control and brain microstructural changes in T2DM using DTI.</p><p><strong>Methods: </strong>This retrospective study included 90 participants (30 healthy controls, 60 T2DM patients) who underwent 1.5T MRI DTI at The Affiliated Shunde Hospital of Jinan University between January 2023 and May 2024. T2DM patients were categorized into well-controlled (HbA1c < 7%, n = 30) and poorly controlled (HbA1c ≥ 7%, n = 30) groups. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were analyzed across multiple white matter regions. Pearson's correlation was used to assess associations between HbA1c and DTI metrics, while group differences were evaluated using Bayesian effect size estimation.</p><p><strong>Results: </strong>HbA1c negatively correlated with ADC values in the right hippocampus (r = -0.33, p = 0.0013), suggesting a relationship between poor glycemic control and increased tissue diffusivity. A weak but significant positive correlation between HbA1c and FA in the right hippocampus (r = 0.23, p = 0.03) was observed. ADC values were higher in the poorly controlled T2DM group, indicating potential diabetes-related microstructural changes. No significant FA or ADC differences were found in other brain regions (p > 0.05).</p><p><strong>Conclusions: </strong>Poor glycemic control in T2DM is associated with microstructural alterations in the right hippocampus, potentially reflecting early neurodegenerative processes. Longitudinal studies are needed to further investigate these findings.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"153"},"PeriodicalIF":2.9,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143974810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal ultrasound radiomics model combined with clinical model for differentiating follicular thyroid adenoma from carcinoma.","authors":"Qianqian Zhao, Shiyan Guo, Yan Zhang, Jinguang Zhou, Ping Zhou","doi":"10.1186/s12880-025-01685-2","DOIUrl":"https://doi.org/10.1186/s12880-025-01685-2","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop a nomogram integrating radiomics features derived from contrast-enhanced ultrasound (CEUS) and B-mode ultrasound (B-US) with clinical features to improve preoperative differentiation between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA). Accurate preoperative diagnosis is critical for guiding appropriate treatment strategies and reducing unnecessary interventions.</p><p><strong>Methods: </strong>We retrospectively included 201 patients with histopathologically confirmed FTC (n = 133) or FTA (n = 68). Radiomics features were extracted from B-US and CEUS images, followed by feature selection and machine-learning model development. Five models were evaluated, and the one with the highest area under the curve (AUC) was used to construct a radiomics signature. A Clinical Risk model was developed using statistically significant clinical features, which outperformed the conventional Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) in both training and test groups. The radiomics signature and Clinical Risk model were integrated into a nomogram, whose diagnostic performance, calibration and clinical utility were assessed.</p><p><strong>Results: </strong>The Clinical Risk model achieved superior diagnostic performance compared to the C-TIRADS model, with AUCs of 0.802 vs. 0.719 in the training group and 0.745 vs. 0.703 in the test group. The nomogram further improved diagnostic efficacy, with AUCs of 0.867 (95% CI, 0.800-0.933) in the training group and 0.833 (95% CI, 0.729-0.937) in the test group. It also demonstrated excellent calibration. Decision curve analysis (DCA) also indicated that the nomogram showed good clinical utility.</p><p><strong>Conclusion: </strong>By combining CEUS and B-US radiomics features with clinical data, we developed a robust nomogram for distinguishing FTC from FTA. The model demonstrated superior diagnostic performance compared to existing methods and holds promise for enhancing clinical decision-making in thyroid nodule management.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"152"},"PeriodicalIF":2.9,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143973982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of cerebral vessels morphological alteration and white matter hyperintensities burden on the one-year risk of ischemic stroke recurrence.","authors":"Hao Wang, Guoqing Wu, Bei Wang, Ying Liu, Lan Zheng, He Wang, Jing Ding","doi":"10.1186/s12880-025-01687-0","DOIUrl":"https://doi.org/10.1186/s12880-025-01687-0","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the associations between cerebral vessels morphological features, white matter hyperintensities (WMHs), and the one-year risk of ischemic stroke recurrence.</p><p><strong>Methods: </strong>A total of 677 patients diagnosed with acute ischemic stroke from January 2018 to April 2021 were consecutively enrolled. Head computed tomography angiography (CTA) and magnetic resonance imaging including fluid-attenuated inversion recovery (FLAIR), were obtained on admission. Cerebral vessels morphological features such as volume, length, radius, density, tortuosity, branch complexity, and degree of stenosis were extracted and calculated using CTA data. Additionally, automated segmentation was employed for delineating WMHs lesions based on FLAIR images. By incorporating clinical characteristics, six predictive models were developed using Cox proportional hazards analysis to estimate the one-year risk of stroke recurrence. The performance of these models was evaluated by comparing the concordance index (C-index).</p><p><strong>Results: </strong>The study found significant associations between the lack of antiplatelet therapy at discharge, reduced length and branching of cerebral vessels, and increased burden of WMHs, with a higher one-year risk of recurrent ischemic stroke (all P < 0.05). The integrated model demonstrated superior prognostic capability (C-index: 0.750; 95% CI: 0.684-0.817), outperforming models based solely on clinical characteristics (C-index: 0.636; 95% CI: 0.555-0.717), cerebral vessels morphology (C-index: 0.601; 95% CI: 0.526-0.676), and WMHs burden (C-index: 0.680; 95% CI: 0.603-0.757).</p><p><strong>Conclusion: </strong>The quantitative assessment of cerebral vessels morphological features and WMHs provides a promising neuroimaging tool for estimating the one-year risk of ischemic stroke recurrence. The incorporation of cerebral vessels morphological features enhances the predictive accuracy.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"150"},"PeriodicalIF":2.9,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143964150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation.","authors":"K Radha, Yepuganti Karuna","doi":"10.1186/s12880-025-01694-1","DOIUrl":"https://doi.org/10.1186/s12880-025-01694-1","url":null,"abstract":"<p><p>Diabetes is a widespread condition that can lead to serious vision problems over time. Timely identification and treatment of diabetic retinopathy (DR) depend on accurately segmenting retinal vessels, which can be achieved through the invasive technique of fundus imaging. This methodology facilitates the systematic monitoring and assessment of the progression of DR. In recent years, deep learning has made significant steps in various fields, including medical image processing. Numerous algorithms have been developed for segmenting retinal vessels in fundus images, demonstrating excellent performance. However, it is widely recognized that large datasets are essential for training deep learning models to ensure they can generalize well. A major challenge in retinal vessel segmentation is the lack of ground truth samples to train these models. To overcome this, we aim to generate synthetic data. This work draws inspiration from recent advancements in generative adversarial networks (GANs). Our goal is to generate multiple realistic retinal fundus images based on tubular structured annotations while simultaneously creating binary masks from the retinal fundus images. We have integrated a latent space auto-encoder to maintain the vessel morphology when generating RGB fundus images and mask images. This approach can synthesize diverse images from a single tubular structured annotation and generate various tubular structures from a single fundus image. To test our method, we utilized three primary datasets, DRIVE, STARE, and CHASE_DB, to generate synthetic data. We then trained and tested a simple UNet model for segmentation using this synthetic data and compared its performance against the standard dataset. The results indicated that the synthetic data offered excellent segmentation performance, a crucial aspect in medical image analysis, where smaller datasets are often common. This demonstrates the potential of synthetic data as a valuable resource for training segmentation and classification models for disease diagnosis. Overall, we used the DRIVE, STARE, and CHASE_DB datasets to synthesize and evaluate the proposed image-to-image translation approach and its segmentation effectiveness.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"149"},"PeriodicalIF":2.9,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143973863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nomogram-based prediction of the prognosis in patients with free floating venous thrombus after closed traumatic fracture.","authors":"Yao Wei, Changxu Guo, Xiaoyu Chen, Yu Yuan","doi":"10.1186/s12880-025-01695-0","DOIUrl":"https://doi.org/10.1186/s12880-025-01695-0","url":null,"abstract":"<p><strong>Background: </strong>Free-floating venous thrombosis (FFVT), a distinct subtype of deep vein thrombosis (DVT), is associated with pulmonary thromboembolism (PTE) and carries a high mortality risk.</p><p><strong>Objective: </strong>This study aimed to develop a nomogram to predict the prognosis of FFVT in patients with closed traumatic fractures.</p><p><strong>Materials and methods: </strong>A retrospective analysis of clinical and ultrasound data from 326 patients with FFVT post-closed traumatic fractures was conducted. Patients were divided into training (n = 240, January 2019-June 2023) and validation (n = 86, June 2023-June 2024) sets. Prognostic risk factors were identified using LASSO and multivariable logistic regression. A nomogram was constructed using R Studio, and its predictive accuracy was validated via calibration curves, receiver operating characteristic (ROC) analysis, and external validation.</p><p><strong>Results: </strong>Independent risk factors for FFVT progression to closed thrombus included D-dimer levels, FFVT location, collateral blood flow volume around the thrombus, and thrombus margins (P < 0.05). The model demonstrated high discriminative ability, with a C-index of 0.945. ROC analysis revealed areas under the curve (AUC) of 0.949 (training set) and 0.924 (validation set). Calibration curves confirmed strong agreement between predicted and observed outcomes.</p><p><strong>Conclusion: </strong>The nomogram provides an accurate prognostic tool for FFVT in patients with closed traumatic fractures, aiding clinical decision-making to improve patient outcomes.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"151"},"PeriodicalIF":2.9,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter-reader agreement of RECIST and mRECIST criteria for assessing response to transarterial chemoembolization in hepatocellular carcinoma.","authors":"Saeed Mohammadzadeh, Alisa Mohebbi, Ali Abdi, Afshin Mohammadi","doi":"10.1186/s12880-025-01688-z","DOIUrl":"https://doi.org/10.1186/s12880-025-01688-z","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the reproducibilities of Response Evaluation Criteria in Solid Tumors (RECIST) and modified RECIST (mRECIST) for hepatocellular carcinoma (HCC) patients treated with transarterial chemoembolization (TACE) using contrast-enhanced computed tomography (CT).</p><p><strong>Methods: </strong>This retrospective study included 105 consecutive patients with confirmed HCC recruited from November 2002 to June 2012. The study protocol has been pre-registered at ( https://osf.io/nxg4q/ ) on the Open Science Framework (OSF) platform. Patients with pre-procedural and follow-up CT scans who had solely received TACE were included. The tumor response evaluation to TACE was conducted using RECIST 1.1 and mRECIST guidelines. Three experienced board-certified abdominal radiologists interpreted CT scans.</p><p><strong>Results: </strong>For pre-procedure CT, the agreement was more excellent when using RECIST guidelines with a \"marginally significant\" p-value of 0.056. This trend continued for post-procedural CT scans, with RECIST again showing significantly higher agreement with a p-value of 0.001. When evaluating the four categories of response, Gwet's coefficient was 0.90 (CI = 0.83 to 0.97) for RECIST and 0.80 (CI = 0.63 to 0.90) for mRECIST. Conversely, the Fleiss Kappa analysis demonstrated a higher agreement for the mRECIST guideline. There was an insignificant difference in RECIST and mRECIST guidelines inter-reader agreement when categorizing the tumor response with a p-value of 0.101.</p><p><strong>Conclusion: </strong>Both guidelines' inter-reader reproducibility in assessing tumor response through CT after the TACE procedure was excellent, with RECIST's reproducibility being very slightly better.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"148"},"PeriodicalIF":2.9,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143953768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}