Diagnostic and interventional radiology最新文献

{"title":"Impact of biopsy route, muscle pathway, and cortex target on safety and diagnostic yield in ultrasound-guided renal parenchymal biopsy.","authors":"Kadir Han Alver, Halil Serdar Aslan, Muhammet Arslan, Muhammed Tekinhatun, Mahmut Demirci, Nagihan Yalçın, Ahmet Baki Yağcı","doi":"10.4274/dir.2025.253699","DOIUrl":"10.4274/dir.2025.253699","url":null,"abstract":"<p><strong>Purpose: </strong>To compare the safety and diagnostic yield of two ultrasound (US)-guided percutaneous renal biopsy (PRB) approaches, lateral to medial and medial to lateral, which differ in access route, muscle groups traversed, and cortical targets.</p><p><strong>Methods: </strong>This retrospective study included 490 patients (mean age: 38.2 ± 21.2 years; 267 men, 223 women) who underwent US-guided PRB between 2019 and 2024 and had abdominal computed tomography (CT)/magnetic resonance imaging (MRI) within 1 year. At the left kidney lower pole level (L3-L4), anterior-posterior thicknesses of the traversed muscle groups were measured on CT/MRI. Complications were classified according to the Society of Interventional Radiology guidelines. Diagnostic yield was categorized as optimal (≥ 12 glomeruli), suboptimal (≥ 3 glomeruli), and pathologist based (diagnostic according to final pathology assessment). Group comparisons were performed using the chi-square test, Fisher's exact test, and t-test.</p><p><strong>Results: </strong>In 490 PRBs (237 lateral to medial, 253 medial to lateral), the medial-to-lateral approach, despite traversing thicker muscles (35.7 vs. 11.5 mm, <i>P</i> = 0.001), produced smaller hematomas (8.6 vs. 17.3 mm, <i>P</i> = 0.001) with similar complication rates (major: 3.6% vs. 3.4%, <i>P</i> = 0.913; minor: 36% vs. 33.8%, <i>P</i> = 0.608). Diagnostic adequacy was comparable, but optimal yield was higher with the medial-to-lateral route (85.0% vs. 73.0%, <i>P</i> = 0.001).</p><p><strong>Conclusion: </strong>Both approaches demonstrated comparable safety. However, the medial-to-lateral route was associated with smaller hematomas and a higher proportion of optimal biopsies from the lateral cortex, but suboptimal and pathologist-based adequacy remained high in both techniques.</p><p><strong>Clinical significance: </strong>When standard lower pole lateral cortex biopsy is not feasible due to cortical scarring, cysts, overlying skin lesions, or anatomic limitations-especially in patients for whom contralateral biopsy is not possible (e.g., solitary or ectopic pelvic kidney, severe unilateral hydronephrosis)-alternative cortical targets must be used. Understanding how different access routes and muscle pathways influence hemorrhage control and diagnostic yield helps operators choose the safest and most effective technique in these situations.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"479-486"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145699919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the diagnostic performance of the artificial intelligence-based TIRADS algorithm with established classification systems for thyroid nodules.","authors":"Abdilkadir Bozkuş, Yeliz Başar, Koray Güven","doi":"10.4274/dir.2025.253428","DOIUrl":"10.4274/dir.2025.253428","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate and compare the diagnostic performance of various Thyroid Imaging Reporting and Data Systems (TIRADS), with a particular focus on the artificial intelligence-based TIRADS (AI-TIRADS), in characterizing thyroid nodules.</p><p><strong>Methods: </strong>In this retrospective study conducted between April 2016 and May 2022, 1,322 thyroid nodules from 1,139 patients with confirmed cytopathological diagnoses were included. Each nodule was assessed using TIRADS classifications defined by the American College of Radiology (ACR-TIRADS), the American Thyroid Association (ATA-TIRADS), the European Thyroid Association (EU-TIRADS), the Korean Thyroid Association (K-TIRADS), and the AI-TIRADS. Three radiologists independently evaluated the ultrasound (US) characteristics of the nodules using all classification systems. Diagnostic performance was assessed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value, and comparisons were made using the McNemar test.</p><p><strong>Results: </strong>Among the nodules, 846 (64%) were benign, 299 (22.6%) were of intermediate risk, and 147 (11.1%) were malignant. The AI-TIRADS demonstrated a PPV of 21.2% and a specificity of 53.6%, outperforming the other systems in specificity without compromising sensitivity. The specificities of the ACR-TIRADS, the ATA-TIRADS, the EU-TIRADS, and the K-TIRADS were 44.6%, 39.3%, 40.1%, and 40.1%, respectively (all pairwise comparisons with the AI-TIRADS: <i>P</i> < 0.001). The PPVs for the ACR-TIRADS, the ATA-TIRADS, the EU-TIRADS, and the K-TIRADS were 18.5%, 17.9%, 17.9%, and 17.4%, respectively (all pairwise comparisons with the AI-TIRADS, excluding the ACR-TIRADS: <i>P</i> < 0.05).</p><p><strong>Conclusion: </strong>The AI-TIRADS shows promise in improving diagnostic specificity and reducing unnecessary biopsies in thyroid nodule assessment while maintaining high sensitivity. The findings suggest that the AI-TIRADS may enhance risk stratification, leading to better patient management. Additionally, the study found that the presence of multiple suspicious US features markedly increases the risk of malignancy, whereas isolated features do not substantially elevate the risk.</p><p><strong>Clinical significance: </strong>The AI-TIRADS can enhance thyroid nodule risk stratification by improving diagnostic specificity and reducing unnecessary biopsies, potentially leading to more efficient patient management and better utilization of healthcare resources.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"376-381"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13324035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144946717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-organ non-contrast computed tomography radiomics model to predict hepatic encephalopathy in patients with cirrhosis and hepatorenal failure.","authors":"Jin-Ming Cao, Ming-Ya Zhang, Xue-Mei Ding, Hai-Ying Zhou, Xiao-Ming Zhang, Tian-Wu Chen","doi":"10.4274/dir.2025.253655","DOIUrl":"10.4274/dir.2025.253655","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To develop and validate a model by incorporating abdominal multi-organ non-contrast computed tomography (CT) radiomics and clinical features to predict the feasibility of hepatic encephalopathy (HE) occurrence in patients with cirrhosis and hepatorenal failure.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In total, 351 consecutive patients with cirrhosis and hepatorenal failure undergoing non-contrast abdominal CT scans at Centers 1 and 2 were enrolled. Patients from Center 1 were randomly allocated to training (n = 191) and internal test (n = 81) groups, and those from Center 2 were assigned to the external test group (n = 79). The nnU-Net framework was used for automated three-dimensional (3D) segmentation of abdominal organs-the liver, spleen, portal and splenic vein, inferior vena cava, esophagogastric junction, stomach, liver-adjacent small bowel, and colon. Segmented multi-organ radiomics features were extracted using 3D Slicer, with R software used for feature processing and model construction. Model performance in predicting HE occurrence was evaluated using receiver operating characteristic (ROC) analysis in the training, internal test, and external test cohorts. Decision curve analysis (DCA) was used to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) tool was used to provide a basis for model interpretability analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 351 patients (mean age, 61.3 ± 10.7 years; 231 men) were enrolled in this study. Esophageal variceal bleeding, peritonitis, and ascites were independent clinical predictors of HE. Twenty discriminative radiomics features, selected from the abovementioned multi-organs through intraclass correlation coefficient and least absolute shrinkage and selection operator analysis, were used to construct the radiomics model. The integrated model, incorporating both radiomics and clinical features, obtained higher areas under the ROC curve than the radiomics and clinical models in the training (0.87 vs. 0.83 vs. 0.68), internal test (0.85 vs. 0.81 vs. 0.66), and external test (0.83 vs. 0.78 vs. 0.72) cohorts, as evidenced by favorable integrated discrimination improvement values (&lt;i&gt;P&lt;/i&gt; &lt; 0.05 for all). The integrated model demonstrated superior clinical utility in DCA. Moreover, SHAP feature contribution analysis revealed that the top five features in terms of contribution were all extracted from the digestive tract.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The integrated model can effectively predict HE occurrence in patients with cirrhosis and hepatorenal failure.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Clinical significance: &lt;/strong&gt;This novel model, developed by integrating abdominal multi-organ non-contrast CT radiomics and clinical features, demonstrates robust performance in predicting the occurrence of cirrhosis-related HE in patients with cirrhosis and hepatorenal failure. It thus provides a valuable tool for clinical decision-making, facilitating the prevention of this compli","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"354-362"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145721747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.","authors":"Sophia Ty, Fahmida Haque, Parth Desai, Nobuyuki Takahashi, Usamah Chaudhary, Peter L Choyke, Anish Thomas, Barış Türkbey, Stephanie A Harmon","doi":"10.4274/dir.2025.253310","DOIUrl":"10.4274/dir.2025.253310","url":null,"abstract":"<p><strong>Purpose: </strong>Small cell lung cancer (SCLC) is an aggressive disease with diverse phenotypes that reflect the heterogeneous expression of tumor-related genes. Recent studies have shown that neuroendocrine (NE) transcription factors may be used to classify SCLC tumors with distinct therapeutic responses. The liver is a common site of metastatic disease in SCLC and can drive a poor prognosis. Here, we present a computational approach to detect and characterize metastatic SCLC (mSCLC) liver lesions and their associated NE-related phenotype as a method to improve patient management.</p><p><strong>Methods: </strong>This study utilized computed tomography scans of patients with hepatic lesions from two data sources for segmentation and classification of liver disease: (1) a public dataset from patients of various cancer types (segmentation; n = 131) and (2) an institutional cohort of patients with SCLC (segmentation and classification; n = 86). We developed deep learning segmentation algorithms and compared their performance for automatically detecting liver lesions, evaluating the results with and without the inclusion of the SCLC cohort. Following segmentation in the SCLC cohort, radiomic features were extracted from the detected lesions, and least absolute shrinkage and selection operator regression was utilized to select features from a training cohort (80/20 split). Subsequently, we trained radiomics-based machine learning classifiers to stratify patients based on their NE tumor profile, defined as expression levels of a preselected gene set derived from bulk RNA sequencing or circulating free DNA chromatin immunoprecipitation sequencing.</p><p><strong>Results: </strong>Our liver lesion detection tool achieved lesion-based sensitivities of 66%-83% for the two datasets. In patients with mSCLC, the radiomics-based NE phenotype classifier distinguished patients as positive or negative for harboring NE-like liver metastasis phenotype with an area under the receiver operating characteristic curve of 0.73 and an F1 score of 0.88 in the testing cohort.</p><p><strong>Conclusion: </strong>We demonstrate the potential of utilizing artificial intelligence (AI)-based platforms as clinical decision support systems, which could help clinicians determine treatment options for patients with SCLC based on their associated molecular tumor profile.</p><p><strong>Clinical significance: </strong>Targeted therapy requires accurate molecular characterization of disease, which imaging and AI may aid in determining.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"382-390"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145231687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic value of nidus sphericity in brain AVMs treated with Gamma Knife Radiosurgery: a preliminary study.","authors":"Yunus Emre Şentürk, Enes Muhammed Cantürk, Ahmet Peker, Sabahattin Yüzkan, Selçuk Peker","doi":"10.4274/dir.2025.253651","DOIUrl":"10.4274/dir.2025.253651","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To evaluate the association of the three-dimensional (3D)-modelled sphericity index of brain arteriovenous malformation (AVM) with nidus obliteration rate and time following Gamma Knife&lt;sup&gt;®&lt;/sup&gt; Radiosurgery (GKRS), and to compare the predictive value of the AVM nidus sphericity index with previously established morphological predictors, such as AVM volume.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This institutional review board-approved retrospective study included 44 patients with cerebral AVMs who underwent single-session or hypofractionated GKRS between 2020 and 2023. Patients who received multimodal therapy, including prior endovascular embolization or microsurgical resection of the AVM nidus, were excluded. A minimum follow-up of 24 months was required for study inclusion. The primary endpoint was defined as complete angiographic obliteration following the initial GKRS, without any latent intracranial hemorrhage requiring hospitalization or surgical intervention. Pretreatment threshold-based semi-automatic segmentation of the AVM nidus was performed to obtain its volume and surface area, from which the sphericity index (Φ) was calculated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Nineteen (43.2%) AVMs achieved obliteration at a mean of 35.7 ± 11.4 months, whereas 25 (56.8%) had residual nidus at 43.7 ± 13.4 months. Sphericity values were more compact and stable, whereas volume showed wide variability across the groups. The median volume of obliterated AVMs was 1.6 (3.9) cm&lt;sup&gt;3&lt;/sup&gt;, and the median volume for AVMs with residual nidus was 4.9 (13.7) cm&lt;sup&gt;3&lt;/sup&gt; (&lt;i&gt;P&lt;/i&gt; = 0.002). Median AVM sphericity was 0.53 (0.26) for obliterated AVMs and 0.32 (0.19) for AVMs with residual nidus (&lt;i&gt;P&lt;/i&gt; = 0.003). Sphericity demonstrated fair discriminative performance, comparable to AVM volume (Φ cut-off: 0.41; sensitivity 79%, specificity 68%, area under the curve: 0.77). However, optimal cut-off values of 0.30 and 0.66 yielded a sensitivity and specificity of 100% and 96%, respectively. Kaplan-Meier analysis revealed a shorter median obliteration time for high-sphericity AVMs (&gt; 0.41) compared with low-sphericity AVMs (45 vs. 60 months, &lt;i&gt;P&lt;/i&gt; = 0.001). Among patient-related and morphological parameters, sphericity was associated with earlier AVM obliteration (hazard ratio: 36.29, 95% confidence interval: 2.89-454.37, &lt;i&gt;P&lt;/i&gt; = 0.005), although it did not remain an independent predictor in multivariate analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This preliminary study found that higher AVM nidus sphericity was associated with increased obliteration rates and shorter time to obliteration following GKRS. Although not an independent predictor, sphericity exhibited a more stable distribution than volume, suggesting its potential as a complementary 3D biomarker for predicting radiosurgical outcomes of AVMs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Clinical significance: &lt;/strong&gt;AVM nidus sphericity may serve as a practical 3D geometric biomarker for predictin","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"510-518"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145951487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor: Rethinking reference accuracy in large language models for radiology.","authors":"Hamza Eren Güzel","doi":"10.4274/dir.2026.264022","DOIUrl":"10.4274/dir.2026.264022","url":null,"abstract":"","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"395-396"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the editor: Beyond publication rates: improving the quality and impact of radiology residency research.","authors":"Hakan Ayyıldız, Şükrü Mehmet Ertürk","doi":"10.4274/dir.2025.253814","DOIUrl":"10.4274/dir.2025.253814","url":null,"abstract":"","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"453-454"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.","authors":"Ali Cantürk, Raif Can Yarol, Ali Samet Tasak, Hakan Gülmez, Kenan Kadirli, Tayfun Bişgin, Berke Manoğlu, Selman Sökmen, İlhan Öztop, İlknur Görken Bilkay, Özgül Sağol, Sülen Sarıoğlu, Funda Barlık","doi":"10.4274/dir.2025.253270","DOIUrl":"10.4274/dir.2025.253270","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;Neoadjuvant chemoradiotherapy (CRT) is known to increase sphincter preservation rates and decrease the risk of postoperative recurrence in patients with locally advanced rectal tumors. However, the response to CRT in patients with locally advanced rectal cancer (LARC) varies significantly. The objective of this study was to compare the performance of models based on radiomics features of the tumor alone, the mesorectum alone, and a combination of both in predicting tumor response to neoadjuvant CRT in LARC.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective study included 101 patients with LARC. Patients were categorized as responders (modified Ryan score 0-1) and non-responders (modified Ryan score 2-3). Pre-CRT magnetic resonance imaging evaluations included tumor-T2 weighted imaging (T2WI), tumor-diffusion weighted imaging (DWI), tumor-apparent diffusion coefficient (ADC) maps, and mesorectum-T2WI. The first radiologist segmented the tumor and mesorectum from T2-weighted images, and the second radiologist performed tumor segmentation using DWI and ADC maps. Feature reproducibility was assessed by calculating the intraclass correlation coefficient (ICC) using a two-way mixed-effects model with absolute agreement for single measurements [ICC(3,1)]. Radiomic features with ICC values &lt;0.60 were excluded from further analysis. Subsequently, the least absolute shrinkage and selection operator method was applied to select the most relevant radiomic features. The top five features with the highest coefficients were selected for model training. To address class imbalance between groups, the synthetic minority over-sampling technique was applied exclusively to the training folds during cross-validation. Thereafter, classification learner models were developed using 10-fold cross-validation to achieve the highest performance. The performance metrics of the final models, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC), were calculated to evaluate the classification performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among the 101 patients, 36 were classified as responders and 65 as non-responders. A total of 25 radiomic features from the tumor and 20 from the mesorectum were found to be statistically significant (&lt;i&gt;P&lt;/i&gt; &lt; 0.05). The AUC values for predicting treatment response were 0.781 for the tumor-only model (random forest), 0.726 for the mesorectum-only model (logistic regression), and 0.837 for the combined model (logistic regression).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Radiomic features derived from both the tumor and mesorectum demonstrated complementary prognostic value in predicting treatment response. The inclusion of mesorectal features substantially improved model performance, with the combined model achieving the highest AUC value. These findings highlight the added predictive contribution of the mesorectum as a key peritumoral structure in radiomics-ba","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"365-375"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144820905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hepatic alveolar echinococcosis: a great tumor mimicker.","authors":"Diğdem Kuru Öz, Ayşe Erden","doi":"10.4274/dir.2025.253582","DOIUrl":"10.4274/dir.2025.253582","url":null,"abstract":"","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"363-364"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13319924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145534284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The imaging spectrum of myocardial infarction and its associated complications: a contemporary pictorial review of computed tomography and magnetic resonance imaging.","authors":"Yu Zhang, Sung Min Ko","doi":"10.4274/dir.2026.263576","DOIUrl":"10.4274/dir.2026.263576","url":null,"abstract":"<p><p>Myocardial infarction (MI) is a leading cause of morbidity and mortality worldwide. Although rapid diagnosis and reperfusion in the acute setting rely primarily on clinical assessment, electrocardiography, echocardiography, and invasive coronary angiography, advanced cardiac imaging plays an essential role beyond the hyperacute phase. Cardiac computed tomography (CCT) and cardiac magnetic resonance (CMR) imaging provide complementary information that extends from coronary anatomy to myocardial tissue characterization and functional assessment. CMR imaging enables the comprehensive, multiparametric evaluation of MI, including left ventricular function, myocardial edema and area at risk, infarct size and transmural extent, microvascular obstruction, intramyocardial hemorrhage, myocardial viability, and ischemia using cine imaging, T1/T2 and T2* mapping, perfusion imaging, and late gadolinium enhancement. These features support an accurate differentiation between acute and chronic infarction, an assessment of myocardial salvage, and prognostic stratification. CCT offers a rapid, non-invasive assessment of coronary artery stenosis and plaque characteristics and has expanded to include an evaluation of ventricular function, myocardial perfusion, and delayed-enhancement patterns. When combined with CT-derived fractional flow reserve or myocardial perfusion imaging, CT allows for an integrated anatomic and functional assessment of the myocardium, particularly for non-culprit lesions, following MI. Myocardial delayed-enhancement CT can visualize the infarcted myocardium and microvascular injury in select patients, though it remains complementary to magnetic resonance imaging. This pictorial essay illustrates the imaging spectrum of MI and its major mechanical and thromboembolic complications, including ventricular rupture, septal defects, papillary muscle rupture, aneurysm formation, left ventricular thrombi, and pericardial disease. By highlighting the strengths and limitations of CCT and CMR and providing practical guidance for modality selection, this article aims to support informed clinical decision-making in the contemporary management of patients with MI.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"426-436"},"PeriodicalIF":2.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147303529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}