BMC Medical Imaging最新文献

{"title":"A deep learning-based study on automated CT diagnosis of kidney stones, hydronephrosis and pyonephrosis.","authors":"Hanlin Gao, Hongyao Chen, Yihe Wang, Weiping Wang, Chao Yao, Xiaobin Zhu, Chenjing Guo","doi":"10.1186/s12880-026-02581-z","DOIUrl":"10.1186/s12880-026-02581-z","url":null,"abstract":"<p><p>Automated identification of urinary system diseases on non-contrast computed tomography (NCCT) can facilitate early diagnosis and inform treatment decisions. However, kidney stones, hydronephrosis, and pyonephrosis share overlapping NCCT appearances and require diagnostic cues at different spatial scales, making accurate identification within a unified framework challenging. To address this, we constructed an integrated CT-based framework for automated identification of urinary system diseases. First, the collected CT data were split into training, validation, and test sets and processed using a unified preprocessing pipeline for resolution standardization and normalization. Subsequently, we evaluated multiple image enhancement strategies, including histogram equalization, CLAHE, Laplacian sharpening, and brightness enhancement. We conducted a systematic comparison of these enhancement methods to quantify their effects on disease identification performance. To support unified automated diagnosis of kidney stones, hydronephrosis, and pyonephrosis, we developed a multi-disease renal diagnostic network, MSF-TEA Net. Through multi-scale feature fusion and multi-evidence collaborative modeling, it effectively represents the image features of different pathological states. This model introduces Tri-Evidence Attention (TEA), which models the high-density features of small calculi targets, the global morphological changes of fluid accumulation, and the inflammatory texture features of pus accumulation. Through adaptive weighted fusion, the network enhances discrimination and diagnostic performance when complex lesions coexist. Experimental results show that, within the unified MSF-TEA Net framework, Laplacian sharpening achieves the best performance among the tested enhancement methods, with a test accuracy of 94.40% ± 1.13% and an AUC of 99.30%, outperforming the other enhancement strategies. Ablation studies further confirm the contributions of PPM, ASPP, and TEA. Overall, the proposed unified framework enables reliable identification of kidney stones, hydronephrosis, and pyonephrosis, supporting clinical decision-making.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692967/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148454438","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":"Comparing and combining O-RADS ultrasound, serum CA125, and HE4 for the preoperative diagnosis of ovarian tumors.","authors":"Jingjing Gong, Keting Li, Yujie Liu, Min Ren","doi":"10.1186/s12880-026-02560-4","DOIUrl":"10.1186/s12880-026-02560-4","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative differentiation between benign and malignant ovarian tumors is critical for optimal patient management. The Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound risk stratification and serum biomarkers like CA125 and HE4 are key diagnostic tools, but their comparative and combined efficacy, especially when stratified by menopausal status, warrants further investigation.</p><p><strong>Purpose: </strong>This study aimed to evaluate and compare the individual diagnostic performance of O-RADS US, CA125, HE4, and the ROMA index, and to assess the added value of combining O-RADS US with serum biomarkers for preoperative ovarian tumor diagnosis.</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted on 367 patients with pathologically confirmed ovarian tumors. All patients underwent preoperative transvaginal ultrasound for O-RADS classification and serum testing for CA125 and HE4. The ROMA score was calculated. Multivariable logistic regression analysis was performed to identify independent predictors of ovarian malignancy. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of the models across a range of threshold probabilities. Patients were stratified into premenopausal (n = 228) and postmenopausal (n = 139) subgroups for comparative analysis.</p><p><strong>Results: </strong>Malignant tumors (n = 163) were associated with significantly older age, larger lesion diameter, higher O-RADS categories, and elevated levels of CA125, HE4, and ROMA compared to benign tumors (n = 204) (all p < 0.001). O-RADS classification showed a strong correlation with malignancy (χ²=278.335, p < 0.001). Multivariable logistic regression identified O-RADS score and HE4 as independent predictors of ovarian malignancy. In stratified analysis, age was not a significant discriminator in postmenopausal women (p = 0.743), unlike in premenopausal women. ROC analysis revealed that in premenopausal women, O-RADS had superior diagnostic accuracy (AUC = 0.971) compared to CA125 (AUC = 0.798), HE4 (AUC = 0.838), and ROMA (AUC = 0.843). Conversely, in postmenopausal women, HE4 demonstrated the highest accuracy among serum biomarkers (AUC = 0.971), comparable to O-RADS (AUC = 0.939). Critically, the combination of O-RADS and HE4 yielded the most balanced and robust diagnostic performance overall (AUC = 0.959, Sensitivity = 94.95%, Specificity = 92.50%, PPV = 96.91%).Decision curve analysis confirmed clinical utility across threshold probabilities.</p><p><strong>Conclusion: </strong>O-RADS dominates in premenopausal women, while HE4 performs comparably to O-RADS in postmenopausal women, highlighting the need for menopausal status-specific diagnostic strategies. Integrating O-RADS with HE4 further enhances preoperative diagnostic certainty.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13651113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148454511","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":"An explainable multimodal 2.5D deep learning-radiomics model for predicting extranodal extension in lung adenocarcinoma using preoperative CT: a multicenter retrospective cohort study.","authors":"Jianyu Wang, Yuhang Wang, Xiaojiang Zhao, Han Zhang, Qiuqiao Mu, Yanbo Wang, Xin Li, Meilin Xu, Zhenchun Song, Jie Ren, Zixiao Wang, Daqiang Sun","doi":"10.1186/s12880-026-02584-w","DOIUrl":"10.1186/s12880-026-02584-w","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop the Radiomics-Assembled ENE system (RAIEm), a multimodal preoperative computed tomography (CT) radiomics model, for identifying extranodal extension (ENE) in lung adenocarcinoma (LUAD) and to examine its utility in risk stratification and individualized treatment planning.</p><p><strong>Methods: </strong>We retrospectively assembled preoperative thin-slice chest CT scans and postoperative hematoxylin-eosin-stained histology from patients with LUAD. Tumors were segmented automatically using a 3D U-Net with virtual adversarial training. Radiomic features were derived from the intratumoral region and from concentric peritumoral rings expanded by 2, 4, 6, and 8 mm, and 2.5D deep-learning features were extracted from five axial CT slices centered on the largest tumor section using a convolutional neural network. Subsequently, we constructed an integrated Radiomics-ENE model (RAIEm), whose performance was assessed in separate training and validation cohorts.</p><p><strong>Results: </strong>RAIEm outperformed conventional unimodal models, yielding higher area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both the training and external validation cohorts. In the validation cohort, AUC was 0.78 (95% CI, 0.66-0.89), and decision-curve analysis indicated greater net benefit across most threshold probabilities. RAIEm demonstrated significant discriminative power for recurrence-free survival risk stratification in both the training and external validation cohorts (both p < 0.01). ENE-positive patients showed significantly shorter recurrence-free survival than ENE-negative patients in both cohorts. Shapley Additive Explanations (SHAP) analysis highlighted key radiomic drivers, with deep-learning features (one 2D and two 2.5D) ranking among the top three contributors.</p><p><strong>Conclusion: </strong>RAIEm - a multimodal, preoperative CT radiomics-based model - showed encouraging performance for identifying ENE in LUAD and may support preoperative risk assessment; however, prospective multicenter validation is required before routine clinical implementation.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13651311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148454430","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":"A comprehensive survey of multi-modality medical image fusion: methods, challenges, and applications.","authors":"Sa I Ibrahim","doi":"10.1186/s12880-026-02555-1","DOIUrl":"10.1186/s12880-026-02555-1","url":null,"abstract":"<p><p>Medical image fusion (MIF) is the process of fusing two medical pictures from different modalities into one image. This technology tries to produce a fused output image from two source images that contain more effective and relevant information. This image is used in the healthcare industry, specifically for disease diagnosis. The main challenge is using a single image modality to diagnose diseases accurately. The fused image includes spectral and structural information for the source images to help doctors with disease diagnosis problems. Positron emission tomography (PET), magnetic resonance imaging (MRI), computed tomography (CT), and single photon emission computed tomography (SPECT) are some of the medical imaging modalities. Each modality has its benefits and drawbacks. Researchers have presented different MIF techniques that obtain high fusion results in the MIF field. This paper is a comprehensive survey of multiple state-of-the-art MIF techniques in the spatial and transform domains. It also discusses the main MIF evaluation metrics. Finally, quantitative and qualitative evaluations for some of these techniques are obtained.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13371370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148444296","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":"MRI-based radiomics interpretable model predicts microsatellite instability status in endometrioid adenocarcinoma.","authors":"Ying Wang, Qinqin Yi, Xia Feng, Yan Luo, Xue Tang, Jingshan Gong","doi":"10.1186/s12880-026-02585-9","DOIUrl":"10.1186/s12880-026-02585-9","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate a multiparametric MRI-based radiomics model for noninvasive preoperative prediction of microsatellite instability (MSI) in patients with endometrioid adenocarcinoma (EC), and to improve model interpretability with SHapley Additive exPlanations (SHAP) analysis.</p><p><strong>Methods: </strong>We enrolled 241 patients with pathologically confirmed EC and divided them into training (n = 193) and test (n = 48) cohorts at an 8:2 ratio. Radiomic features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and late-phase T1-weighted contrast-enhanced (T1CE) sequences. Feature screening was performed sequentially via intraclass correlation coefficient (ICC) filtering, Pearson collinearity elimination, and 10-fold cross-validated least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models were constructed and evaluated via area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. The optimal model was validated using decision curve analysis (DCA), and SHAP analysis was applied to interpret feature contributions.</p><p><strong>Results: </strong>The MSI rate was 29% (70/241). Four features were retained after selection. The XGBoost model achieved the best performance (training AUC = 0.983, test AUC = 0.870), with test set accuracy of 0.812, sensitivity of 0.929, and specificity of 0.765. DCA confirmed clinical utility, SHAP analysis identified T2WI-GLSZM GrayLevelVariance as the most impactful feature and visualized the differences in feature contributions between MSI and MSS patients at the individual level, clarifying the model's decision-making logic.</p><p><strong>Conclusion: </strong>The multiparametric MRI-based radiomics model enables noninvasive prediction of MSI in EC, potentially guiding personalized immunotherapy selection.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148444258","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":"Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.","authors":"Min Wei, Zhenyu Shu, Maohua Lyu, Yanting Liang, Yuguo Wei, Ying Wang, Guangying Zheng","doi":"10.1186/s12880-026-02578-8","DOIUrl":"10.1186/s12880-026-02578-8","url":null,"abstract":"<p><strong>Objective: </strong>Develop a multimodal fusion model combining MRI radiomics and deep learning (DL) to predict pathologic complete response (pCR) in breast cancer patients post-neoadjuvant chemotherapy (NACT).</p><p><strong>Methods: </strong>Patients with locally advanced breast cancer from two centers (Center 1: 404 training, Center 2: 174 testing) were enrolled. Pretreatment MRI radiomic features and DL-derived features were extracted, selected via Minimum Redundancy Maximum Relevance and Least Absolute Shrinkage and Selection Operator, and integrated with clinical risk factors into a joint multimodal fusion model using XGBoost. The model was validated internally on an independent test cohort and externally on the Duke Breast Imaging Dataset (284 patients) to assess generalizability across diverse populations and imaging protocols.</p><p><strong>Results: </strong>The fusion model achieved AUCs of 0.935 (training), 0.916 (testing), and 0.83 (external validation), surpassing standalone radiomics and DL models. External validation confirmed robustness to institutional variability, with strong calibration and clinical utility.</p><p><strong>Conclusion: </strong>The XGBoost-integrated radiomics-DL-clinical model accurately predicts pCR, validated across independent cohorts, and may optimize personalized treatment strategies.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148444324","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":"A novel face-level blast mechanism: CT findings of neuro-ocular injury from a mass-casualty event.","authors":"Christel Tamer, Diana Abou Ltaif, Taima Ghalayini, Omar El Sardouk, Joe Rizkallah, Farah Abou Zeid, Aya El Harake, Hassan Cherri, Alain Abi Ghanem, Roula Hourani","doi":"10.1186/s12880-026-02570-2","DOIUrl":"10.1186/s12880-026-02570-2","url":null,"abstract":"<p><strong>Background: </strong>On September 17, 2024, thousands of handheld communication devices detonated simultaneously across Lebanon, producing a mass-casualty event with an unprecedented close-proximity blast mechanism. Because the devices were held at face level, victims sustained highly localized craniofacial and hands, and to a lesser extent, neck injuries. This article aims to characterize the spectrum, severity, and distribution of intracranial, craniofacial, orbital, and neck injuries among victims presenting to a tertiary trauma center. To our knowledge, this study represents the first radiological characterization of neuro-ocular injury patterns resulting from synchronized handheld device detonations.</p><p><strong>Methods: </strong>This retrospective study included all patients presenting to the American University of Beirut Medical Center (AUBMC) within 24 hours of the event who underwent head, neck, and/or orbital imaging. Patients without blast-related injuries or without neuro-ocular imaging were excluded. Computed Tomography (CT) findings were recorded using standardized neuro-ocular trauma categories.</p><p><strong>Results: </strong>Of 177 patients presenting after the blast, 153 had blast-related injuries, and 103 underwent brain and orbit CT scan. Head injuries were highly prevalent (93.2%). Intracranial hemorrhage (ICH) occurred in 39.8%, most commonly hemorrhagic contusions (22.3%), and subarachnoid hemorrhage (SAH) (21.4%). Skull and facial fractures were present in 65%. Orbital injuries occurred in 82.5%, including orbital wall fractures (54.4%). Ocular trauma was severe, with globe rupture in 56.3% of patients (24.1% bilateral), penetrating globe injuries in 28.2%. Vitreous hemorrhage was the most common posterior segment injury (35.9%). Neck injuries occurred in 13.6% of patients, most commonly retained shrapnel and subcutaneous emphysema, with occasional extension into deep neck spaces and one case of laryngeal injury. Shrapnel was predominantly localized to the face (89.3%) and orbits (74.8%). Incidental intracranial findings were identified in approximately 1-2% of imaged patients.</p><p><strong>Conclusions: </strong>The pager detonations produced an extraordinary concentration of craniofacial, orbital, and neck trauma, with rates of ICH, skull fractures, and globe rupture exceeding those reported in previous civilian mass-casualty events. The face-level, close-contact blast mechanism explains the intense neuro-ocular injury burden. Rapid CT imaging proved essential for identifying injury severity, guiding surgical intervention, and supporting coordinated ophthalmologic-neurosurgical management. These findings underscore the need for specialized preparedness in future unconventional blast scenarios.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434836","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":"A clinical and head CT-based scoring system for predicting high cerebral microbleeds burden in hypertensive patients.","authors":"Xinbin Wang, Yonggang Qiu, Qinbin Wang, Hao Dong, Zhihua Xu, Dihong Chen, Deyun Huang, Sheng Chen, Yicheng Hu, Zebin Yang","doi":"10.1186/s12880-026-02576-w","DOIUrl":"10.1186/s12880-026-02576-w","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to establish and validate a scoring system utilizing clinical and head CT features to predict high cerebral microbleeds (CMBs) burden (> 10 CMBs) in hypertensive patients.</p><p><strong>Methods: </strong>A retrospective review was conducted on 458 hypertensive patients from two centers, including 244 patients in the training cohort, 101 patients in the internal validation cohort, and 113 patients in the external validation cohort. Clinical data, as well as head CT and MRI findings, were collected. Head MRI results were used to classify patients into two groups: those with > 10 CMBs and those with ≤ 10 CMBs. Statistically significant clinical and CT features distinguishing the groups were identified through univariate and multivariate logistic regression analyses. These features were subsequently weighted and scored to establish a scoring system. The model's effectiveness was assessed through receiver operating characteristic (ROC) curves and decision curve analysis (DCA). To enhance clinical applicability, the scoring system was stratified into three score ranges.</p><p><strong>Results: </strong>Four features were ultimately incorporated into the scoring system: hypertension duration, lacunar infarcts count grade, lacunar infarcts location grade, and leukoaraiosis grade. The area under the curve (AUC) values for the training, internal validation, and external validation cohorts were 0.898 (95% confidence interval [CI]: 0.857-0.938), 0.840 (95% CI: 0.760-0.920), and 0.810 (95% CI: 0.725-0.877), respectively. At a cutoff value of 4.5 points, the sensitivity and specificity were 85.7% and 76.9%, respectively. The DCA further demonstrated the clinical utility of the model. The scoring system was categorized into three ranges: 0-1, 2-4, and 5-12. As the score increased, the incidence of high CMBs burden (> 10 CMBs) in the training, internal validation, and external validation cohorts progressively increased.</p><p><strong>Conclusion: </strong>The scoring system, which incorporates clinical and head CT features, has proven to be a valuable tool for predicting high CMBs burden in hypertensive patients and provides significant support for clinical decision-making.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148444287","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":"Liver findings in newly diagnosed, treatment-naive patients with Crohn's disease: a preliminary study of MRI proton density fat fraction and extracellular volume fraction.","authors":"Luanxin Zhu, Zhiheng Zhang, Yuping Ma, Bota Cui, Faming Zhang, Melat Adugna Asnake, Jinfa Zhou, Jianguo Zhu","doi":"10.1186/s12880-026-02572-0","DOIUrl":"10.1186/s12880-026-02572-0","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the applicability of MRI proton density fat fraction (MRI-PDFF) and extracellular volume fraction (ECV) in detecting liver injury in patients with intestinal Crohn's disease (CD). Additionally, it evaluated the relationships among multi-parametric imaging indicators, clinical scores, and serological markers.</p><p><strong>Methods: </strong>A total of 107 patients with CD were enrolled in this retrospective study conducted from December 2021 to October 2024. All subjects underwent MRI scans with Iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) sequence to generate the MRI-PDFF values. Moreover, pre- and post-contrast liver T1 mapping was performed at the level of the liver hilum. The ECV value was calculated by inputting the patient's hematocrit level (HCT). Based on the Harvey-Bradshaw index (HBI), the clinical score was assessed along with serological markers, including alkaline phosphatase (ALP), alanine transaminase (ALT), aspartate aminotransferase (AST), erythrocyte sedimentation rate (ESR), fecal calprotectin (FC), and γ-glutamyl transferase (GGT). Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the application value of ECV and MRI-PDFF value compared with ALP, ALT, AST, ESR, FC, and GGT. The correlation of the ECV and MRI-PDFF values with clinical score and serological markers was assessed using correlation coefficients.</p><p><strong>Results: </strong>In examining different levels of intestinal inflammation activity, significant statistical differences were observed in MRI-PDFF and ECV values (p < 0.001). ROC curve analysis indicated that MRI-PDFF and ECV exhibited superior discriminatory abilities, with AUCs of 0.773 and 0.843, respectively (ALP, ALT, AST, GGT, ESR, and FC of 0.512, 0.566, 0.530, 0.551, 0.693, 0.716). Both MRI-PDFF values (r = 0.520, r = 0.246, r = 0.339, all p < 0.001) and ECV (r = 0.534, r = 0.435, r = 0.433, all p < 0.001) were positively correlated with HBI, ESR, and FC values.</p><p><strong>Conclusion: </strong>This study suggests that chronic systemic inflammation may lead to liver injury in patients with CD. Liver MRI can help assess and monitor liver involvement that may not be visually detectable in these patients.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434923","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":"Determinants of successful midsagittal visualization of the fetal corpus callosum using 2D transabdominal ultrasound.","authors":"Aylin Yılmaz, Didar Kurt","doi":"10.1186/s12880-026-02580-0","DOIUrl":"10.1186/s12880-026-02580-0","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate factors influencing visualization of the fetal corpus callosum (CC) during mid-trimester ultrasound examinations, with particular focus on gestational age, fetal presentation, and maternal body mass index (BMI).</p><p><strong>Methods: </strong>This observational study included 860 singleton pregnancies undergoing mid-trimester ultrasound examination between 20 + 0 and 28 + 0 weeks' gestation. Fetuses with central nervous system or major structural anomalies were excluded. Assessment of the corpus callosum was performed using two-dimensional (2D) transabdominal ultrasound. Visualization was defined as clear identification of the corpus callosum in the true midsagittal plane. Associations between CC visualization and gestational age, fetal presentation, and maternal BMI were analyzed using chi-square tests and multivariate logistic regression.</p><p><strong>Results: </strong>The corpus callosum was successfully visualized in 748 of 860 examinations (87.0%). Visualization rates decreased significantly with advancing gestational age (p < 0.001). Successful visualization was lowest in cephalic presentation (79.8%) and highest in breech presentation (97.7%) (p < 0.001). Maternal obesity (BMI ≥ 30 kg/m²) was associated with reduced visualization rates compared with BMI < 30 kg/m² (80.2% vs. 89.5%, p < 0.001). In multivariate analysis, breech presentation remained an independent predictor of successful visualization compared with cephalic presentation (adjusted OR 9.35, 95% CI 4.25-20.57, p < 0.001). Maternal obesity independently reduced the likelihood of visualization (adjusted OR 0.49, 95% CI 0.32-0.76, p = 0.001). Each one-week increase in gestational age was associated with a 15% decrease in visualization likelihood (adjusted OR 0.85, 95% CI 0.78-0.92, p < 0.001). Gentle hand manipulation was required in 423 examinations (49.2%) and was significantly more frequent in cephalic presentation than in breech or transverse presentation (62.7% vs. 30.2% and 39.0%, respectively; p < 0.001).</p><p><strong>Conclusion: </strong>Transabdominal ultrasound enables midsagittal visualization of the fetal corpus callosum in the majority of mid-trimester examinations. Fetal presentation, gestational age, and maternal obesity exert a significant influence on visualization rates; in particular, maternal obesity and increasing gestational age are associated with a reduced probability of successful assessment. Gentle hand manipulation was frequently required, highlighting the technical challenges of midsagittal corpus callosum imaging during routine examinations.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434943","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}