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.
The integration of artificial intelligence (AI) into healthcare is transforming clinical decision-making, patient outcomes, and workflows. AI inference, applying trained models to new data, is central to this evolution, with cloud-based infrastructures enabling scalable AI deployment. The Open Medical Inference (OMI) platform democratizes AI access through open protocols and standardized data formats for seamless, interoperable healthcare data exchange. By integrating standards like FHIR and DICOMweb, OMI ensures interoperability between healthcare institutions and AI services while fostering ethical AI use through a governance framework addressing privacy, transparency, and fairness.OMI's implementation is structured into work packages, each addressing technical and ethical aspects. These include expanding the Medical Informatics Initiative (MII) Core Dataset for medical imaging, developing infrastructure for AI inference, and creating an open-source DICOMweb adapter for legacy systems. Standardized data formats ensure interoperability, while the AI Governance Framework promotes trust and responsible AI use.The project aims to establish an interoperable AI network across healthcare institutions, connecting existing infrastructures and AI services to enhance clinical outcomes. · OMI develops open protocols and standardized data formats for seamless healthcare data exchange.. · Integration with FHIR and DICOMweb ensures interoperability between healthcare systems and AI services.. · A governance framework addresses privacy, transparency, and fairness in AI usage.. · Work packages focus on expanding datasets, creating infrastructure, and enabling legacy system integration.. · The project aims to create a scalable, secure, and interoperable AI network in healthcare.. · Pelka O, Sigle S, Werner P et al. Democratizing AI in Healthcare with Open Medical Inference (OMI): Protocols, Data Exchange, and AI Integration. Rofo 2026; 198: 173-184.
Mammography screening programs (MSP) are established for women age 50 to 69 years in Germany and Europe. Some of the studies that build the evidence base for these programs also included women who were younger or older than this target population. The aim of our study was to assess whether screening also provides more benefit than harm to women outside the originally defined age range of the German MSP.A systematic review and meta-analysis of randomized controlled trials (RCT) was performed to assess overall and breast cancer mortality in women older than 70 years and women under 50 years. Radiation-associated age-specific lifetime attributable risks (LAR) were estimated based on a modified risk model of the BEIR Committee using current cancer and lifetime data for a female German population.Two RCTs with 33,268 women age 70 years or older, and eight RCTs with 394,080 women age 39-49 years were included. The relative reduction in breast cancer mortality was 28% (risk ratio (RR) = 0.72; 95% confidence interval (CI): 0.54-0.95) and 18% (RR = 0.82; 95%-CI: 0.71-0.96), respectively. The proportion of overdiagnoses in older women is estimated at 19% and is higher than in younger women. Assuming biennial screening from below 50 to 69 years of age, the LAR decreases considerably with increasing age at start of screening, being 0.06%, 0.04%, and 0.025% when starting at 40, 45, or 50 years, respectively. The corresponding benefit-risk ratios are about 25, 35, and 45, respectively. Changing the upper screening age to 75 has little impact on the benefit-risk ratio.Extending the age limits in MSP to women starting from 45 years and up to 75 years is justified from the radiation perspective since the benefit substantially outweighs the radiation risk. Based on our report, the MSP has also been approved for women age 70 to 75 in Germany as of February 2024, while it is still pending for younger women. · Screening can reduce breast cancer mortality in women age 45-49 and 70-75.. · As a result, more women can benefit from mammography screening programs.. · The downside for older women is more overdiagnoses.. · Younger women face a higher radiation risk.. · Hunger T, Nekolla EA, Wanka-Pail E et al. Extending the Age Range in Mammography Screening: A Benefit-Risk Assessment from a Radiation Protection Perspective. Rofo 2026; 198: 164-172.
To evaluate the non-contrast-enhanced relaxation-enhanced angiography without contrast (REACT) sequence for the assessment of extrathoracic vessels in pediatric patients compared to contrast-enhanced (CE), multiphasic magnetic resonance angiography (MRA).In this prospective, single-center study, pediatric patients referred for clinically indicated contrast-enhanced MRI of various extrathoracic body regions underwent additional free-breathing REACT and multiphasic, free-breathing CE-MRA at 1.5 T (Philips Ingenia). REACT was acquired using Cartesian k-space order, except in the abdomen, where it was acquired using a radial stack of stars k-space sampling (REACT VANE). The acquisition time was recorded. Image quality (Likert scale 1-5, with 5 being the best) and vessel diameter were evaluated by two independent readers in four predefined vessels in each body region. Furthermore, a quantitative analysis of SNR and CNR was performed.30 patients (age: 12.3 ± 4 years) successfully completed REACT and CE-MRA. The acquisition time for REACT was 2:49 ± 1:03 min, while abdominal REACT VANE required 4:51 ± 0:52 min. The CE-MRA acquisition time was 3:49 ± 1:03 min. The median image quality ratings were good to excellent (Likert scale 4-5) for both readers. No significant difference in the image quality ratings was found (p = 0.12 - 0.58). Interobserver agreement of image quality ratings of the two readers was moderate to substantial (Cohen's kappa REACT: 0.58, CE-MRA: 0.64). Vessel diameter measurements showed a strong correlation (r = 0.93) between REACT and CE-MRA with high intraclass correlation coefficients (REACT: 0.97, CE-MRA: 0.97). Quantitative analysis showed a higher venous SNR and higher arterial and venous CNR in REACT (p = 0.001-0.018).Given the good and comparable image quality, REACT can be useful in vascular imaging in children under free-breathing, while potentially eliminating the need for contrast agent injection. · MR angiography is widely used in pediatric imaging for vessel assessment.. · Contrast-enhanced MRA has limitations due to the use of gadolinium-based contrast agents.. · REACT is a novel contrast-free MRA technique performed during free breathing.. · REACT provides image quality comparable to contrast-enhanced free-breathing MRA.. · Spogis J, Tsiflikas I, Katemann C et al. Free-breathing non-contrast-enhanced flow-independent MR angiography using REACT: A prospective study for pediatric vessel assessment. Rofo 2026; 10.1055/a-2781-8861.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：