Katrine Riklund

Colorectal liver metastases (CLM) exhibit distinct histopathological growth patterns (HGPs) at the metastasis-liver interface, clinically relevant for prognosis and potential predictive value. This study investigates the feasibility of using Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography Computed Tomography ( ¹⁸ F-FDG PET/CT) to preoperatively classify HGPs in CLM and secondly investigate PET parameters as prognostic markers for overall survival (OS). In a prospective study at Umeå University Hospital (June 2013-December 2017), 78 patients with resectable CLM underwent contrast-enhanced liver MRI and ¹⁸ F-FDG PET/CT imaging. HGP classification was performed on tissue sections according to 2017 international guidelines. PET features (SUV _mean [standardised uptake value], SUV _max , metabolic tumour volume [MTV] and first-order texture metrics) were analysed to distinguish encapsulating (eHGP) from replacement (rHGP) patterns. In pretreated patients, multivariate logistic regression including both metabolic and texture parameters identified significant associations for parameters from the fixed level threshold algorithm (VuePoint Sharp IR) with small odds ratios. Overall, PET parameters showed limited ability to discriminate between eHGP and rHGP. OS was superior with eHGP versus rHGP (log-rank p = 0.045). In chemonaïve patients, SUV-based metrics showed unadjusted exploratory associations with OS after liver surgery, varying by reconstruction/delineation and were not significant after correction for multiple testing. FDG-PET-derived parameters were not reliably associated with HGP and did not support non-invasive HGP prediction in this cohort. eHGP was associated with superior survival, underscoring the value of HGP assessment for prognostication while alternative non-invasive biomarkers remain to be identified.

Background Radiology training in Europe has progressively evolved through collaboration between professional societies, accreditation bodies, and European medical organizations. Over the last three decades, the European Union of Medical Specialists (UEMS) and the European Society of Radiology (ESR) have jointly developed a coordinated framework designed to harmonize radiology education, assessment, accreditation, and continuing professional development across Europe. Objective This article presents the concept of a unified European radiology training ecosystem and examines the complementary roles of the European Diploma in Radiology (EDiR), the European Training Assessment Programme (ETAP 2.0), the Accreditation Council in Imaging (ACI), and the European Commission–supported EU-REST project. Discussion The European radiology training ecosystem is built upon interconnected pillars linking individual competence assessment, programme evaluation, accreditation systems, and European workforce and educational standards. The EDiR provides a harmonized assessment framework for individual radiologists, ETAP 2.0 evaluates training programme quality, and the Accreditation Council in Imaging supports continuing medical education accreditation through collaboration with the UEMS-EACCME®. The EU-REST project further strengthens this ecosystem by defining workforce, education, and training recommendations for radiology across the European Union. Together, these initiatives support harmonization, quality assurance, professional mobility, and patient safety. Conclusions The coordinated collaboration between the ESR and the UEMS has created a robust European radiology training ecosystem integrating education, assessment, accreditation, and workforce planning. This model provides an important framework for harmonized postgraduate medical training and may serve as an example for other medical specialties seeking to balance national diversity with European standards of excellence.

According to glymphatic system theory, cerebrospinal fluid (CSF) perfuses the brain’s interstitial space to support waste clearance, but the magnitude of this flow and the outflow pathway of interstitial fluid (ISF) in humans remain uncertain. To achieve flow quantification, we applied a compartment-model approach applied in conjunction with serial quantitative MRI data acquired after intrathecal gadolinium administration. Using the method, we estimated CSF-to-ISF inflow to 45 ± 20 mL/h, in patients with suspected idiopathic normal pressure hydrocephalus. Tissue-specific contributions were 34 ± 14 mL/h in cortical gray matter, 11±6 mL/h in white matter, and 0.4 ± 0.3 mL/h in subcortical gray matter, suggesting that CSF perfusion occurs primarily in superficial regions near the subarachnoid space. A lack of correlation between inflow and total craniospinal system outflow (r = 0.03, P = 0.91) suggested that ISF recirculates back into CSF rather than exiting the craniospinal system via a separate route. Independent experiments in healthy older individuals using intravenous gadolinium administration supported ISF-to-CSF recirculation, where contrast material that presumably crossed the blood–brain barrier subsequently appeared in the subarachnoid space, allowing ISF-to-CSF flow quantification. These findings provide a quantitative framework for studying brain clearance in humans and support subarachnoid space recirculation as an important efflux route.

From 29th September to 2nd October 2025, European Radiation Protection Week was hosted in London by the UK Health Security Agency with Imperial College London’s Department of Epidemiology and Biostatistics. The meeting brought together the platforms under the MEENAS umbrella (MELODI, EURADOS, EURAMED, NERIS, ALLIANCE, and SHARE) to deliver a diverse programme of presentations and posters spanning the breadth of radiation protection research. This paper provides a summary of the meeting.

Background and purpose: The study aims to evaluate dosimetric properties of hypofractionated treatment plans integrating focal boost, using registered whole-mount histopathology (WMHP) as reference standard. Methods: Fifteen men from the PAMP trial (EudraCT: 2015-005046-55) were included. Participants had ≥ 1 ISUP Grade group ≥ 4 lesion and underwent [68Ga]prostate-specific membrane antigen (PSMA) positron emission tomography/multiparametric magnetic resonance imaging (PET/mpMRI) and [11C]Acetate-PET/computed tomography before radical prostatectomy. Four radiation oncologists delineated gross tumor volumes (GTVs) on PSMA-PET/mpMRI. Sixty treatment plans were optimized, one per GTV and patient. Prostate planning target volumes were prescribed 42.7 Gy in seven fractions, with a simultaneous GTV boost up to 49.0 Gy, prioritizing organs at risk (OARs). Digital WMHP provided Gleason grading and was co-registered with in-vivo imaging. Target coverage for GTVs and voxels sharing Gleason patterns (GPs) was assessed via dose-volume histogram (DVH) analysis. Interobserver agreement in GTV-delineations was quantified with Fleiss’ kappa. Results: The median GTV dose per plan (D50) ranged from 48.3 to 49.1 Gy. For voxels with the highest GP, D50 was 42.9–49.2 Gy, exceeding 47.2 Gy in all except one plan. In lowest pattern voxels, D50 was 42.5–49.3 Gy, and below 43.4 Gy in over half the plans. Significant positive correlations between Fleiss’ kappa and DVH parameters appeared only for GP 5 regions, specifically for Fleiss’ kappa and D50 for two observers and the average D50 across observers. Interpretation: The histologically confirmed tumor was only partially boosted. Regions with more aggressive disease received better coverage. These findings provide a rational for prioritizing OARs in treatment planning.

Variations in cerebral blood flow and blood volume interact with intracranial pressure and cerebrospinal fluid dynamics, all of which play a crucial role in brain homeostasis. A key physiological modulator is respiration, but its impact on cerebral blood flow and volume has not been thoroughly investigated. Here we used 4D flow MRI in a population-based sample of 65 participants (mean age = 75 ± 1) to quantify these effects. Two gating approaches were considered, one using respiratory-phase and the other using respiratory-time (i.e. raw time in the cycle). For both gating methods, the arterial inflow was significantly larger during exhalation compared to inhalation, whereas the venous outflow was significantly larger during inhalation compared to exhalation. The cerebral blood volume variation per respiratory cycle was 0.83 [0.62, 1.13] ml for respiratory-phase gating and 0.78 [0.59, 1.02] ml for respiratory-time gating. For comparison, the volume variation of the cardiac cycle was 1.01 [0.80, 1.30] ml. Taken together, our results clearly demonstrate respiratory influences on cerebral blood flow. The corresponding vascular volume variations appear to be of the same order of magnitude as those of the cardiac cycle, highlighting respiration as an important modulator of cerebral blood flow and blood volume.

Background: An unprecedented amount of personal health data, with the potential to revolutionize precision medicine, is generated at health care institutions worldwide. The exploitation of such data using artificial intelligence (AI) relies on the ability to combine heterogeneous, multicentric, multimodal, and multiparametric data, as well as thoughtful representation of knowledge and data availability. Despite these possibilities, significant methodological challenges and ethicolegal constraints still impede the real-world implementation of data models. Technical details: The EuCanImage is an international consortium aimed at developing AI algorithms for precision medicine in oncology and enabling secondary use of the data based on necessary ethical approvals. The use of well-defined clinical data standards to allow interoperability was a central element within the initiative. The consortium is focused on 3 different cancer types and addresses 7 unmet clinical needs. We have conceived and implemented an innovative process to capture clinical data from hospitals, transform it into the newly developed EuCanImage data models, and then store the standardized data in permanent repositories. This new workflow combines recognized software (REDCap for data capture), data standards (FHIR for data structuring), and an existing repository (EGA for permanent data storage and sharing), with newly developed custom tools for data transformation and quality control purposes (ETL pipeline, QC scripts) to complement the gaps. Conclusion: This article synthesizes our experience and procedures for health care data interoperability, standardization, and reproducibility.

Although age differences in the dopamine system have been suggested to contribute to age-related cognitive decline based on cross-sectional data, recent large-scale cross-sectional studies reported only weak evidence for a correlation among aging, dopamine receptor availability, and cognition. Regardless, longitudinal data remain essential to make robust statements about dopamine losses as a basis for cognitive aging. We present correlations between changes in D2/3 dopamine receptor availability and changes in working memory measured over 5 yr in healthy, older adults (n = 128, ages 64 to 68 yr at baseline). Greater decline in D2/3 dopamine receptor availability in working memory-relevant regions (caudate, middle frontal cortex, hippocampus) was related to greater decline in working memory performance in individuals who exhibited working memory reductions across time (n = 43; caudate: rs = 0.494; middle frontal cortex: rs = 0.506; hippocampus; rs = 0.423), but not in individuals who maintained performance (n = 41; caudate: rs = 0.052; middle frontal cortex: rs = 0.198; hippocampus; rs = 0.076). The dopamine–working memory link in decliners was not observed in the orbitofrontal cortex, which does not belong to the core working memory network. Our longitudinal analyses support the notion that aging-related changes in the dopamine system contribute to working memory decline in aging.

Background: An unprecedented amount of personal health data, with the potential to revolutionise precision medicine, is generated at healthcare institutions worldwide. The exploitation of such data using artificial intelligence relies on the ability to combine heterogeneous, multicentric, multimodal and multiparametric data, as well as thoughtful representation of knowledge and data availability. Despite these possibilities, significant methodological challenges and ethico-legal constraints still impede the real-world implementation of data models. Technical details: The EuCanImage is an international consortium aimed at developing AI algorithms for precision medicine in oncology and enabling secondary use of the data based on necessary ethical approvals. The use of well-defined clinical data standards to allow interoperability was a central element within the initiative. The consortium is focused on three different cancer types and addresses seven unmet clinical needs. We have conceived and implemented an innovative process to capture clinical data from hospitals, transform it into the newly developed EuCanImage data models and then store the standardised data in permanent repositories. This new workflow combines recognized software (REDCap for data capture), data standards (FHIR for data structuring) and an existing repository (EGA for permanent data storage and sharing), with newly developed custom tools for data transformation and quality control purposes (ETL pipeline, QC scripts) to complement the gaps. Conclusion: This article synthesises our experience and procedures for healthcare data interoperability, standardisation and reproducibility.