Abdominal Radiology最新文献

{"title":"Imaging review of aortic fistulas","authors":"Divij Agarwal,&nbsp;Christine O. Menias,&nbsp;Mahan Mathur,&nbsp;Sumit Dhital,&nbsp;Archana Laroia,&nbsp;Hardik U. Shah,&nbsp;Yashant Aswani","doi":"10.1007/s00261-026-05544-8","DOIUrl":"10.1007/s00261-026-05544-8","url":null,"abstract":"<div><p>Aortic fistulas represent rare but life-threatening communications between the aorta and the adjacent structures, most commonly the gastrointestinal tract. They are classified as primary, arising spontaneously in the setting of a native, diseased aorta, or secondary to prior aortic surgery or endovascular repair. Clinical presentation is often variable and nonspecific—ranging from gastrointestinal bleeding, sepsis, abdominal pain to hemodynamic collapse—making imaging pivotal for diagnosis. Computed tomography angiography is the preferred imaging modality. Imaging features such as visualization of the fistula tract and active contrast extravasation into the fistulized hollow organ are definitive signs of aortic fistulas, but are rarely encountered. On the contrary, nonspecific indirect imaging features such as loss of fat planes and/or ectopic foci of gas are more frequently seen. Prompt recognition of these indirect imaging features is crucial, as delayed diagnosis significantly increases mortality. This review article summarizes the types, clinical features, and imaging findings of aortic fistulas, emphasizing the radiologist’s pivotal role in timely detection and management of aortic fistulas.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5402 - 5414"},"PeriodicalIF":2.3,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00261-026-05544-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147864888","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 doppler ultrasound–based model for predicting blood pressure response after revascularization in unilateral severe renal artery stenosis","authors":"Fuzhou Yi,&nbsp;Zhifang Yang,&nbsp;Enheng Cai,&nbsp;Yifei Yu,&nbsp;Xiaofeng Ni,&nbsp;Yunyun Hu,&nbsp;Xiaoyu Li,&nbsp;Zhe Zhang,&nbsp;Yuanyuan Kang,&nbsp;Jianzhong Xu,&nbsp;Ri Ji","doi":"10.1007/s00261-026-05479-0","DOIUrl":"10.1007/s00261-026-05479-0","url":null,"abstract":"<div><h3>Purpose</h3><p>This study aimed to develop a prediction model to preoperatively identify patients with unilateral severe renal artery stenosis (RAS) most likely to experience blood pressure improvement after revascularization.</p><h3>Methods</h3><p>In this retrospective study, 193 patients with unilateral severe RAS who underwent revascularization between 2018 and 2024 were analyzed. LASSO regression was used for variable selection, and the selected predictors were entered into a multivariable logistic regression model to develop the nomogram based on clinical and Doppler ultrasound parameters. Model performance was evaluated through internal validation and temporal validation.</p><h3>Results</h3><p>The final model included acceleration time (AT) ≥ 70 ms, estimated glomerular filtration rate (eGFR) ≥ 30 mL/min/1.73 m², resistive index (RI) of the contralateral kidney and new-onset hypertension. The model demonstrated a C-statistic of 0.907 and a Brier score of 0.12. Its discriminative ability was confirmed by internal validation (C-statistic 0.888, 95% CI 0.797–0.961), and it achieved a C-statistic of 0.826 with a Brier score of 0.17 in the temporal validation cohort. Decision curve analysis confirmed clinical utility across a threshold probability range of 22–82%.</p><h3>Conclusion</h3><p>This nomogram may serve as an effective tool for estimating the probability of blood pressure response to revascularization and support preprocedural risk stratification in patients with unilateral severe RAS.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5768 - 5777"},"PeriodicalIF":2.3,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multicenter study of automatic segmentation-based multimodal fusion integrating radiomics, deep learning, and clinical parameters for prostate cancer detection","authors":"Ning Ding,&nbsp;Long Jin,&nbsp;Shengnan Yin,&nbsp;Yiding Ji,&nbsp;Ximing Wang,&nbsp;Mengjuan Li","doi":"10.1007/s00261-026-05548-4","DOIUrl":"10.1007/s00261-026-05548-4","url":null,"abstract":"<div><h3>Objective</h3><p>To develop and validate an interpretable machine learning model integrating radiomics, deep learning (DL), and clinical features based on automated MRI segmentation for detecting prostate cancer (PCa).</p><h3>Methods</h3><p>This retrospective multicenter study included 433 prostate patients. The internal cohort comprised 346 patients, who were randomly divided into a training set (<i>n</i> = 242) and an internal validation set (<i>n</i> = 104) at a 7:3 ratio. Automated prostate segmentation was performed on T2-weighted imaging and apparent diffusion coefficient maps using TotalSegmentator. Radiomics features were extracted and selected via mutual information, mRMR, LASSO, and Pearson correlation analysis. The DL labels were derived from a DenseNet-121 convolutional neural network. Using the eXtreme Gradient Boosting (XGBoost) algorithm, we constructed four types of prediction models: clinical models, radiomics models, DL models, and combined models integrating all three feature types (clinical, radiomics, and DL features). Model performance was evaluated using area under the curve (AUC) and related metrics. An external test set of 87 patients was used to validate the predictive performance of each model. Finally, SHapley Additive exPlanations (SHAP) were applied to enhance model interpretability and quantify the impact of individual features.</p><h3>Results</h3><p>The combined model integrating radiomics, DL, and clinical features achieved the highest AUC of 0.902 (95% CI 0.841–0.962) in the external test set, outperforming individual models. SHAP analysis revealed prostate-specific antigen density and DL label as dominant predictors and provided transparent global and local interpretations of model decisions.</p><h3>Conclusion</h3><p>The combined model integrating clinical, radiomics, and DL features based on automated MRI segmentation, achieved high accuracy and promising clinical utility in PCa detection.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5759 - 5767"},"PeriodicalIF":2.3,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Jejunal and ileal gastrointestinal stromal tumors: differences in clinicopathologic and computed tomography features","authors":"Bo Tang,&nbsp;Wei-dong Zhang,&nbsp;Guang-hui Xie,&nbsp;Hui Xu","doi":"10.1007/s00261-026-05547-5","DOIUrl":"10.1007/s00261-026-05547-5","url":null,"abstract":"<div><h3>Purpose</h3><p>To summarize and compare the clinicopathologic and computed tomography (CT) features of jejunal and ileal gastrointestinal stromal tumors (GISTs).</p><h3>Methods</h3><p>A total of 108 patients with pathologically confirmed small bowel GISTs were retrospectively enrolled and categorized into jejunal and ileal groups according to anatomic sites. Two experienced radiologists independently reviewed CT images, with discrepancies resolved by consensus. Clinicopathologic and CT features were compared between the two groups. Multivariate logistic regression analysis was used to identify independent predictors for differentiating jejunal and ileal GISTs, and receiver operating characteristic (ROC) curve analysis was performed to evaluate predictive performance.</p><h3>Results</h3><p>Among 108 GISTs, 63 were jejunal and 45 were ileal. Ileal GISTs more frequently presented with abdominal discomfort, high-risk stratification, increased mitotic count, and larger tumor size (all adjusted <i>p</i> &lt; 0.05), whereas jejunal GISTs were more prone to present with gastrointestinal bleeding (adjusted <i>p</i> = 0.014). Regarding CT features, jejunal GISTs tended to exhibit mixed growth pattern, ulceration, tumor vessels, early venous return, larger draining vein diameter, and higher attenuation in the arterial phase (AP) and portal venous phase (all adjusted <i>p</i> &lt; 0.05). In contrast, ileal GISTs were more likely to show exophytic growth pattern and hemorrhage (all adjusted <i>p</i> &lt; 0.05). Multivariate analysis identified tumor size (<i>p</i> = 0.031), diameter of draining vein (<i>p</i> = 0.023), and AP (<i>p</i> = 0.008) as independent predictors. ROC curve analysis showed that AP achieved the best predictive performance (area under the curve [AUC] = 0.912), followed by tumor size (AUC = 0.710) and diameter of draining vein (AUC = 0.640).</p><h3>Conclusion</h3><p>Jejunal GISTs exhibit distinct clinicopathologic and CT features compared with ileal GISTs. These findings may aid in clinical differentiation and potentially improve diagnostic accuracy.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5749 - 5758"},"PeriodicalIF":2.3,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intra-ablational and peri-ablational radiomics based on post-RFA gadoxetic acid disodium-enhanced MRI for predicting short-term prognosis in hepatocellular carcinoma","authors":"Yaxuan Pang,&nbsp;Jing Zhang,&nbsp;Yanan Mou,&nbsp;Yanan Zhai,&nbsp;Nianjun Liu,&nbsp;Liang Yin","doi":"10.1007/s00261-026-05542-w","DOIUrl":"10.1007/s00261-026-05542-w","url":null,"abstract":"<div><h3>Objective</h3><p>To evaluate the predictive value of intra-ablational and peri-ablational radiomics features extracted from the first post-RFA follow-up gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced MRI for short-term poor prognosis after radiofrequency ablation (RFA) in hepatocellular carcinoma (HCC) patients, particularly for local tumor progression.</p><h3>Methods</h3><p>A total of 181 HCC patients who underwent ultrasound-guided RFA at Lanzhou University First Hospital between 2017 and 2025 were retrospectively enrolled. Gd-EOB-DTPA-enhanced MRI and clinical data from the first post-RFA follow-up were collected. Short-term poor prognosis was defined as the appearance of new lesions within 6 months after RFA. Radiomics models based on MRI (arterial phase, portal venous phase, hepatobiliary phase) across various regions (entire ablation zone, Peri 3 mm, 5 mm, 7 mm) were developed using the LASSO approach and logistic regression. A combined model was constructed using best radiomics model features and clinical-radiological predictors. The performance, calibration and clinical utility were evaluated by ROC curve, calibration curve, and decision curve analysis, respectively.</p><h3>Results</h3><p>The Peri 3 mm model achieved the best predictive performance among all radiomics models, with AUCs of 0.912 (95% CI: 0.836–0.961) and 0.856 (95% CI: 0.769–0.920) in the training and validation sets, respectively. The combined model integrating Peri 3 mm radiomics features, Log-AFP, and arterial phase peripheral enhancement showed AUCs of 0.948 (95% CI: 0.847–0.967) and 0.889 (95% CI: 0.728–0.934) in the training and validation sets. Exploratory evaluation in an independent testing set yielded consistent trends.</p><h3>Conclusion</h3><p>The combined model incorporating Peri 3 mm radiomics feature and clinical-radiological risk factors has significant predictive value for short-term prognosis after RFA, particularly for local tumor progression, offering potential to optimize follow-up protocols and enable timely clinical decision-making for high-risk patients. However, given the limited number of short-term poor prognosis events in the independent testing set, this set is an exploratory evaluation and requires external validation in multi-center, large-sample cohorts.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5738 - 5748"},"PeriodicalIF":2.3,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of partial splenic embolization and portosystemic shunt occlusion on portal venous hemodynamics using 4D flow MRI","authors":"Daisuke Kato,&nbsp;Naoya Kinota,&nbsp;Daisuke Abo,&nbsp;Satonori Tsuneta,&nbsp;Ryo Morita,&nbsp;Takaaki Fujii,&nbsp;Koji Yamasaki,&nbsp;Motoma Kanaya,&nbsp;Kinya Ishizaka,&nbsp;Takuya Sho,&nbsp;Tatsuhiko Kakisaka,&nbsp;Kohsuke Kudo","doi":"10.1007/s00261-026-05528-8","DOIUrl":"10.1007/s00261-026-05528-8","url":null,"abstract":"<div><h3>Purpose</h3><p>To quantitatively evaluate changes in portal venous hemodynamics after partial splenic embolization (PSE) and portosystemic shunt occlusion (PSO) using four-dimensional (4D) flow magnetic resonance imaging (MRI).</p><h3>Methods</h3><p>This retrospective cohort study included 30 procedures (16 PSE and 14 PSO) performed between 2019 and 2025. Flow rates were measured in the main portal vein (MPV), splenic vein (SpV), superior mesenteric vein (SMV), and portosystemic shunts (PSS) using pre- and post-procedural 4D flow MRI. For PSE, correlations were assessed between changes in MPV and SpV flow, and between vessel-specific flow change ratios and the embolic volume ratio. For PSO, changes in MPV flow were compared based on whether all PSS were treated, and correlation was assessed between flow changes in MPV and PSS flow.</p><h3>Results</h3><p>After PSE, MPV flow decreased (median, 751.0 to 441.5 mL/min; p = 0.025) as did SpV flow (482.5 to 323.0 mL/min; p = 0.001), whereas SMV flow remained unchanged. Changes in MPV and SpV flow were strongly correlated (ρ = 0.726, p = 0.002). The embolic volume ratio showed a moderate correlation with the SpV flow change ratio (ρ = 0.563, p = 0.036), but not with the MPV flow change ratio. After PSO, MPV flow increased (544.5 to 692.5 mL/min; p &lt; 0.001), along with SpV flow (63.0 to 231.5 mL/min; p = 0.001), while SMV flow did not change significantly. The increase in MPV flow was greater when all PSS were treated than when they were not (140.0 vs. 17.0 mL/min; p = 0.019), and was not correlated with PSS flow. </p><h3>Conclusion</h3><p>4D flow MRI demonstrates that PSE decreases portal and splenic venous flow, whereas PSO increases both. These findings provide quantitative insight into complex treatment-related hemodynamic changes in the portal venous system.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5717 - 5726"},"PeriodicalIF":2.3,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Four-dimensional flow MRI-based prediction of minimal hepatic encephalopathy in patients with liver cirrhosis","authors":"Xiang Li,&nbsp;Xin Li,&nbsp;You Zheng,&nbsp;Qiong Hu,&nbsp;Changgu Zhou,&nbsp;Surong Ren,&nbsp;Weijuan Chen,&nbsp;Xiaojing He,&nbsp;Tianwu Chen,&nbsp;Dajing Guo,&nbsp;Jun Zhou","doi":"10.1007/s00261-026-05539-5","DOIUrl":"10.1007/s00261-026-05539-5","url":null,"abstract":"<div><h3>Objectives</h3><p>To develop a noninvasive predictive model for minimal hepatic encephalopathy (MHE) according to the blood flow indices of spontaneous portosystemic shunt (SPSS) on four-dimensional (4D) flow MRI in patients with liver cirrhosis.</p><h3>Methods</h3><p>A consecutive cohort of patients with liver cirrhosis complicated with enhanced abdominal CT-confirmed SPSS from April 2022 to October 2023 was prospectively recruited and divided into MHE and non-MHE groups. All patients underwent portal vascular imaging with 4D flow MRI, the blood flow indices of SPSSs (mainly including gastroesophageal shunts (F-GES) and splenorenal/gastrorenal shunts (F-SRS/F-GRS)), the splenic vein (F-SV), and the superior mesenteric vein (F-SMV) were measured. Additionally, the total crosssectional area of the SPSSs (TA-S) on enhanced CT, demographic characteristics and clinical factors were collected. Clinical, morphological, hemodynamic, and combined predictive models (Model_C, Model_M, Model_H, and Model_C+H) were developed. Their discrimination, calibration and net clinical benefit were evaluated to determine the value of 4D flow MRI-based hemodynamic assessment and to identify the optimal predictive model for MHE.</p><h3>Results</h3><p>A total of 72 patients (mean age 53.9 ± 8.0 years, 43 males) were included, with 27 in the MHE group and 45 in the non-MHE group. Child–Pugh class C, TA-S, F-GES, F-SRS/F-GRS and F-SV were factors influencing the presence of MHE. The combined Model_C+H, integrating clinical factors and 4D flow parameters, substantially outperformed models based on either data type alone (AUC 0.97 vs. 0.77 and 0.90), demonstrating the complementary value of clinical and hemodynamic information. The model also exhibited good calibration (χ² = 14.80, <i>p</i> = 0.35) and the highest net clinical benefit, confirming its excellent predictive performance for MHE.</p><h3>Conclusion</h3><p>4D flow MRI can reveal the hemodynamic characteristics of SPSSs in patients with liver cirrhosis, and a prediction model based on its hemodynamic index and clinical factors can provide valuable insights for early detection and serving as a clinical foundation for timely intervention in MHE.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5727 - 5737"},"PeriodicalIF":2.3,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent developments and prospects in locoregional therapy of hepatocellular carcinoma","authors":"Lauren Park,&nbsp;Anirudh Maddali,&nbsp;David C. Madoff,&nbsp;Hooman Yarmohammadi,&nbsp;Emil I. Cohen,&nbsp;Neil K. Jain,&nbsp;Nima Kokabi,&nbsp;Christos Georgiades,&nbsp;Armeen Mahvash,&nbsp;Peiman Habibollahi,&nbsp;Nariman Nezami","doi":"10.1007/s00261-025-05367-z","DOIUrl":"10.1007/s00261-025-05367-z","url":null,"abstract":"<div><p>This invited review discusses recent advancements and prospects in locoregional therapy (LRT) for hepatocellular carcinoma (HCC), a leading cause of cancer mortality globally. The epidemiology of HCC is shifting toward nonviral etiologies, prompting a focus on early surveillance and personalized treatment approaches. Updated international guidelines, such as the Barcelona Clinic Liver Cancer (BCLC) staging system, American Association for Study of Liver Disease (AASLD) guideline, and European Association for the Study of the Liver (EASL) now recognize LRT—including transarterial embolization, ablation, and radiotherapy—as cornerstone therapies in curative and downstaging strategies for HCC at multiple stages. The article reviews novel transarterial and ablative technologies, including advanced dosimetry for radioembolization, innovative embolic agents, and emerging nonthermal ablation modalities like irreversible electroporation and pulsed electric field ablation, all aimed at increasing precision, local delivery, minimizing systemic exposure. Additionally, combination approaches integrating LRT with immunotherapy are highlighted, reflecting the evolving understanding of HCC’s tumor-immune microenvironment. Progress in robotic guidance, fusion imaging, and imageable radioembolics is also explored. Ongoing and future clinical trials will further define the optimal use of these evolving modalities. Overall, LRT continues to expand as a minimally invasive, indispensable backbone in multidisciplinary HCC management.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5385 - 5401"},"PeriodicalIF":2.3,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hepatobiliary phase radiomics for identifying subclinical liver dysfunction in compensated chronic liver disease: development and external validation","authors":"Fatih Işık,&nbsp;Serdar Sipahioğlu,&nbsp;Muhammed Emre Işıktaş,&nbsp;Muhammed Halid Yener,&nbsp;Mehmet Alperen Tezcan,&nbsp;Nurullah Dag,&nbsp;Hakan Dursun,&nbsp;Fatih Alper","doi":"10.1007/s00261-026-05537-7","DOIUrl":"10.1007/s00261-026-05537-7","url":null,"abstract":"<div><h3>Purpose</h3><p>To develop and externally validate a radiomics model derived from gadoxetic acid–enhanced hepatobiliary phase (HBP) MRI for detecting subclinical liver dysfunction in patients with compensated chronic liver disease (CLD).</p><h3>Methods</h3><p>In this retrospective study, patients were included based on Child–Pugh Class A status and absence of clinical decompensation. Subclinical liver dysfunction was defined using a composite reference standard integrating ALBI score ( &gt; − 2.60), MELD-Na ≥ 10, and FIB-4 ≥ 2.67. Whole-liver radiomic features were extracted from HBP images. Feature selection and model development were performed using Boruta and class-weighted logistic regression within a nested cross-validation framework.</p><h3>Results</h3><p>A total of 138 individuals (90 patients with subclinical liver dysfunction and 48 controls) were analyzed. Six radiomic features were selected for model development. In the internal test cohort (<i>n</i> = 21), the model achieved a ROC–AUC of 0.86 [95% CI: 0.66–1.00] with an accuracy of 0.81 [95% CI: 0.62–0.95]. In the external validation cohort (<i>n</i> = 26), the model achieved a ROC–AUC of 0.89 [95% CI: 0.73–0.99] and an accuracy of 0.77 [95% CI: 0.58–0.92] using a fixed decision threshold. Both first-order and texture-based features were represented among the selected predictors.</p><h3>Conclusion</h3><p>HBP-derived radiomics demonstrates potential as a noninvasive biomarker for identifying subclinical liver dysfunction in compensated CLD patients. While the results are promising, the relatively small validation cohorts warrant cautious interpretation, and further validation in larger multicenter populations is required to confirm robustness and generalizability.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5702 - 5716"},"PeriodicalIF":2.3,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic resonance fingerprinting of the bladder is feasible in men with lower urinary tract symptoms","authors":"Aymen Bahsoun,&nbsp;Jesse Hamilton,&nbsp;Giulia Ippolito,&nbsp;Christopher Brace,&nbsp;Chamila Perera,&nbsp;Wilson Sui,&nbsp;Jesus Fajardo,&nbsp;Vikas Gulani,&nbsp;Nicole Seiberlich,&nbsp;Anne Cameron,&nbsp;John Wei,&nbsp;Hero Hussain,&nbsp;Yun Jiang,&nbsp;Shane Wells","doi":"10.1007/s00261-026-05428-x","DOIUrl":"10.1007/s00261-026-05428-x","url":null,"abstract":"<div><h3>Purpose</h3><p>To quantify bladder wall relaxation times in men without and with lower urinary tract symptoms (LUTS) using three-dimensional (3D) magnetic resonance fingerprinting (MRF).</p><h3>Materials and methods</h3><p>Following informed consent, 51 men (mean age 68.2 years, range 46–80) on active surveillance for prostate cancer underwent multiparametric MRI that included an investigational 3D MRF sequence. Anterior bladder wall T₁ and T₂ relaxation times were measured from the quantitative maps. Anterior bladder wall thickness (BWT) was measured and prostate and transition zone (TZ) volumes were estimated, from the conventional T2w images. Body mass index (BMI), LUTS scores (AUA-SI), risk factors for metabolic syndrome, history of diabetes (DM), and medications prescribed for LUTS were documented from the electronic medical record. The Kruskal-Wallis rank sum test, Wilcoxon rank sum exact test, and Fisher’s exact test were used to assess the associations between clinical variables, categorical imaging data, and mean T₁ and T₂ relaxation times.</p><h3>Results</h3><p>Native bladder wall T₁ relaxation times were longer in men with higher AUA-SI scores [AUA-SI 0–7: 1,496 ms vs. AUA-SI 8–20: 1,787 ms vs. AUA-SI 21–35: 2,063 ms, <i>p</i> = 0.03] while native bladder wall T₂ relaxation times were similar [AUA-SI 0–7: 67 ms vs. AUA-SI 8–20: 78 ms vs. AUA-SI 21–35: 122 ms, <i>p</i> = 0.3]. Native bladder wall T₁ relaxation times were shorter in diabetic men compared to non-diabetic men (1239 ms vs. 1633 ms, <i>p</i> &lt; 0.001), while T₂ relaxation times were similar (86 ms vs. 62 ms, <i>p</i> = 0.4). Prostate and TZ volume, and BWT were similar in men without and with LUTS and DM.</p><h3>Conclusion</h3><p>3D MRF of the bladder is feasible, provides in-vivo tissue characterization of the bladder wall in men with LUTS.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 11","pages":"5694 - 5701"},"PeriodicalIF":2.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00261-026-05428-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825912","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}