Rofo-fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren最新文献

Imaging during pregnancy presents unique challenges. Computed tomography (CT) is generally avoided due to fetal radiation exposure, and magnetic resonance imaging (MRI) may be limited by concerns regarding gadolinium-based contrast agents. As a result, ultrasound (US) remains the primary imaging tool during pregnancy. Contrast-enhanced ultrasound (CEUS) expands the diagnostic capabilities of conventional US by providing real-time vascular information comparable to contrast-enhanced CT or MRI. Importantly, microbubble contrast agents used in CEUS do not cross the placenta and have shown no fetal harm in clinical studies. Nevertheless, CEUS remains an off-label technique during pregnancy, underscoring the need for a comprehensive review of the available literature to assess its safety and clinical applications during pregnancy.This review summarizes the current evidence up to 2025 on the use of CEUS in pregnancy and highlights its clinical value with respect to evaluating acute and chronic maternal conditions - including oncologic, vascular, infectious, inflammatory, and other organ-specific pathologies - illustrated through representative case examples. Clinical studies, case reports, and international guidelines were analyzed with a focus on safety and diagnostic performance.Although still off-label, CEUS demonstrates an excellent safety profile and high diagnostic utility when clinically indicated in pregnancy. No maternal, fetal, or neonatal adverse effects have been reported, and no placental transfer of microbubbles has been demonstrated. CEUS enables high-resolution assessment of abdominal organs, vascular pathologies, and inflammatory or infectious changes, reducing the need for ionizing or gadolinium-based imaging. It represents a safe, effective, and radiation-free diagnostic alternative when conventional imaging is limited. · CEUS expands diagnostic ultrasound without radiation exposure.. · CEUS is a safe alternative to CT or MRI.. · Microbubbles remain intravascular and do not cross the placenta.. · No maternal, fetal, or neonatal adverse effects reported.. · Case examples illustrate safety and diagnostic value.. · Begaj K, Sperr AG, Clevert DA. Contrast-Enhanced Ultrasound (CEUS) in Pregnancy: A Comprehensive Review of Clinical Applications. Rofo 2026; DOI 10.1055/a-2784-9759.

The number of stem cell transplants for hematologic malignancies has doubled since 2000 and continues to rise. Acute complications within the first 100 days post-transplant are linked to high morbidity and mortality of up to 30%. Since all organ systems can be affected and symptoms may be subtle, imaging plays a central role in early detection and monitoring of these complications. In response to increasing transplant numbers, expanding indications, and rapid therapeutic advances, the European Society for Blood and Marrow Transplantation (EBMT) revised its handbook in 2024. Most existing reviews on imaging acute complications post-transplant are five to ten years old. This article aims to provide an updated overview for radiologists increasingly confronted with acute complications after stem cell transplantation.The structure of this systematic review follows the updated EBMT Handbook. A systematic search was conducted in the PUBMED database to identify original studies on imaging of acute post-transplant complications from the past five years. A supplementary selective search helped to integrate the findings into the EBMT-handbook-aligned framework. A total of 29 original studies published in the last five years were included, providing new insights into imaging of acute complications post-HSCT.This review offers a concise overview of typical acute organ-specific complications following stem cell transplantation and highlights recent advances in imaging. · Acute complications after stem cell transplantation are still associated with high mortality.. · Imaging is essential for early diagnosis and management.. · This review presents an updated, guideline-based overview of typical acute post-transplant complications.. · Brandt J, Helfen A. Acute complications after hematopoietic stem cell transplantations - a radiological perspective. Rofo 2026; DOI 10.1055/a-2787-4646.

Brain tumors, especially glioblastomas, remain among the tumor diseases with the worst prognosis. Recent findings in brain tumor research show that neuronal and glial integration of tumors, as well as the formation of glioma cell networks, promote tumor progression and therapy resistance. This highlights the need for innovative imaging techniques that conceptualize brain tumors as systemic central nervous system (CNS) diseases that are deeply integrated in the brain's network architecture.This review presents current imaging methods for analyzing tumor-associated functional and structural connectivity with a focus on resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI).Functional connectivity changes in glioma patients can be detected and quantified using fMRI. These changes are associated with tumor biology, as well as prognosis and cognitive performance. Rs-fMRI parameters may support prognostic assessment and the development of new therapeutic strategies. Quantitative structural connectivity analysis at the individual patient level can provide further insight into tumor integration in the brain's connectional architecture. DTI-based tractography is especially relevant in neurosurgical planning, as it maps the spatial relationship between the tumor and white matter tracts.Imaging analysis of tumor-associated network alterations provides deeper insight into brain tumor biology and may support the development of network-targeted therapeutic approaches. Connectivity-based imaging methods, particularly rs-fMRI and DTI, hold great potential to further enhance preoperative planning, prognostic assessment, and personalized treatment strategies for patients with brain tumors. · Glioma cells form networks beyond macroscopic tumor boundaries and promote therapy resistance.. · Glioma cells form synapses with neurons and exploit neural signals for growth.. · Network alterations can be visualized and quantified using rs-fMRI and DTI.. · Tumor-associated network alterations in imaging correlate with tumor biology and prognosis.. · Imaging markers optimize patient management and support development of new therapeutic strategies.. · Suvak S, Wunderlich S, Stoecklein V et al. Imaging of Brain Tumor Connectivity. Rofo 2026; DOI 10.1055/a-2779-7718.

Primary resection of liver metastases in colorectal cancer remains the leading curative approach. However, a small future liver remnant or central localization of metastases often prevent resectability. In such cases, combining resection with percutaneous ablation may offer a curative-intent treatment option. This study aims to evaluate the oncologic results and morbidity of this combined treatment.In this retrospective study, 21 patients with advanced colorectal liver metastases underwent a two-stage treatment consisting of planned incomplete resection and percutaneous ablation. Outcomes assessed included overall survival and intrahepatic progression-free survival. Main exclusion criteria were extrahepatic disease and ablation of metastases > 3 cm. Results are reported as medians with 95% confidence intervals and standard deviation.The median number of preoperative intrahepatic metastases was 5 [2-13]. Follow-up was available for 95% of patients, with a median follow-up of 21 months. Intra- und extrahepatic recurrences occurred in 81% (17/21). Median intrahepatic progression-free survival was 5 [0-44] months. Median overall survival was 36.5 [6.6-55] months, resulting in a 1-year survival rate of 91%. Twelve patients (57%) passed away. Technical success was achieved in 90%. Three major complications occurred, all of which were successfully treated.In patients with partially unresectable colorectal liver metastases, a two-stage approach combining resection and percutaneous ablation represents a potential curative-intent strategy, particularly when intraoperative ablation is not feasible. Despite high recurrence rates, OS was comparatively long relative to systemic therapy. Further studies are needed to explore treatment sequencing, perioperative therapies, and advanced ablation technologies for optimizing the concept. Ultimately, treatment must be individualized based on the patient's disease profile and institutional capabilities. · A two-stage approach combining resection and percutaneous ablation represents a potential curative-intent strategy in partially irresectable colorectal liver metastases.. · High recurrence rates occur, but overall survival remains comparatively long.. · Individualized, interdisciplinary treatment planning optimizes patient outcomes.. · Further research is needed on sequencing, perioperative strategies, and ablation technologies.. · Lokancevic T, Keil S, Bruners P et al. Treatment of Irresectable Colorectal Liver Metastases by Combination of Liver Resection and Percutaneous Tumor Ablation: Mid-term Outcome. Rofo 2026; DOI 10.1055/a-2781-8926.

AI is emerging as a promising tool for diagnosing COVID-19 based on chest CT scans. The aim of this study was the comparison of AI models for COVID-19 diagnosis. Therefore, we: (1) trained three distinct AI models for classifying COVID-19 and non-COVID-19 pneumonia (nCP) using a large, clinically relevant CT dataset, (2) evaluated the models' performance using an independent test set, and (3) compared the models both algorithmically and experimentally.In this multicenter multi-vendor study, we collected n=1591 chest CT scans of COVID-19 (n=762) and nCP (n=829) patients from China and Germany. In Germany, the data was collected from three RACOON sites. We trained and validated three COVID-19 AI models with different architectures: COVNet based on 2D-CNN, DeCoVnet based on 3D-CNN, and AD3D-MIL based on 3D-CNN with attention module. 991 CT scans were used for training the AI models using 5-fold cross-validation. 600 CT scans from 6 different centers were used for independent testing. The models' performance was evaluated using accuracy (Acc), sensitivity (Se), and specificity (Sp).The average validation accuracy of the COVNet, DeCoVnet, and AD3D-MIL models over the 5 folds was 80.9%, 82.0%, and 84.3%, respectively. On the independent test set with n=600 CT scans, COVNet yielded Acc=76.6%, Se=67.8%, Sp=85.7%; DeCoVnet provided Acc=75.1%, Se=61.2%, Sp=89.7%; and AD3D-MIL achieved Acc=73.9%, Se=57.7%, Sp=90.8%.The classification performance of the evaluated AI models is highly dependent on the training data rather than the architecture itself. Our results demonstrate a high specificity and moderate sensitivity. The AI classification models should not be used unsupervised but could potentially assist radiologists in COVID-19 and nCP identification. · This study compares AI approaches for diagnosing COVID-19 in chest CT scans, which is essential for further optimizing the delivery of healthcare and for pandemic preparedness.. · Our experiments using a multicenter, multi-vendor, diverse dataset show that the training data is the key factor in determining the diagnostic performance.. · The AI models should not be used unsupervised but as a tool to assist radiologists.. · Jaiswal A, Fervers P, Meng F et al. Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset. Rofo 2026; 198: 185-198.