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Technological innovations in genomics and related fields have facilitated large sequencing efforts, supported new biological discoveries in cancer, and spawned an era of liquid biopsy biomarkers. Despite these advances, precision oncology has practical constraints, partly related to cancer's biological diversity and spatial and temporal complexity. Advanced imaging technologies are being developed to address some of the current limitations in early detection, treatment selection and planning, drug delivery, and therapeutic response, as well as difficulties posed by drug resistance, drug toxicity, disease monitoring, and metastatic evolution. We discuss key areas of advanced imaging for improving cancer outcomes and survival. Finally, we discuss practical challenges to the broader adoption of precision imaging in the clinic and the need for a robust translational infrastructure.

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: Normal brain aging is associated with dopamine decline, which has been linked to age-related cognitive decline. Factors underlying individual differences in dopamine integrity at older ages remain, however, unclear. Here we aimed at investigating: (i) whether inflammation is associated with levels and 5-year changes of in vivo dopamine D2-receptor (DRD2) availability, (ii) if DRD2-inflammation associations differ between men and women, and (iii) whether inflammation and cerebral small-vessel disease (white-matter lesions) serve as two independent predictors of DRD2 availability.

Methods: Analyses were performed in a sample of healthy adults > 60 years assessed at two measurement occasions separated by 5 years. At both occasions, DRD2 availability was estimated by ¹¹C-raclopride PET, and white-matter lesions by MRI. Inflammation was assessed by two C-reactive protein-associated DNA methylation scores at study baseline.

Results: Individuals with higher DNA methylation scores at baseline showed reduced striatal DRD2 availability. An interaction was found between DNA methylation scores and sex in relation to striatal DRD2 availability, such that associations were found in men but not in women. DNA methylation scores at study entrance were not significantly associated with 5-year striatal DRD2 decline rates. No significant association was found between DNA methylation scores and white-matter lesions, but higher scores as well as higher lesion burden were independently associated with reduced striatal DRD2 availability in men.

Conclusions: These findings suggest negative associations between one proxy of inflammation and DRD2 availability in older adults, selectively for men who had higher DNA methylation scores. Future studies should investigate other inflammatory markers in relation to dopamine integrity.

Background: Glioblastoma is the most aggressive and malignant brain tumor, characterized by a high degree of heterogeneity, invasiveness, and resistance to treatment. Patients with glioblastoma have a very poor prognosis despite multimodal interventions. In this study, we investigated how 18F-fluorothymidine (18F-FLT) PET combined with contrast-enhanced MRI and blood metabolomics can contribute to evaluate prognosis and treatment response for patients with glioblastoma.

Methods: Patients, scheduled for surgery due to suspected high-grade glioma were included in this clinical study and underwent four 18F-FLT-PET/MRI examinations prior to surgery and during standard treatment. Blood samples were collected and analyzed by metabolomics. Patients were grouped according to survival as long-time survivors (>3 years) and short-time survivors (<500 days).

Results: Both 2 and 6 weeks into treatment, short-time survivors displayed a significantly larger tumor volume than long-time survivors. When comparing MRI findings during treatment, long-time survivors displayed a substantial tumor decrease, whereas the short-time survivors showed minor or no effect. Regarding 18F-FLT-PET the results were not as unambiguous. Furthermore, there was a clear and significant separation in the metabolomic pattern in blood between the survival groups and across treatment time points.

Conclusions: MRI measures of tumor volume and growth during treatment appear to be prognostic clinical factors that affect outcome. Metabolomic patterns in blood differ significantly between the defined survival groups and may serve as support for an early forecast of prognosis. We also observe a clear separation in metabolite levels between different time points during treatment, which likely reflects treatment effects.

Artificial intelligence (AI) is a powerful technology with the potential to disrupt cancer detection, diagnosis and treatment. However, the development of new AI algorithms requires access to large and complex real-world datasets. Although such datasets are constantly being generated, access to them is limited by data fragmentation across numerous repositories and sites, heterogeneity, lack of annotations, and potential privacy issues. The European Cancer Imaging Initiative is a flagship of Europe’s Beating Cancer Plan, aiming to unlock the power of AI for cancer patients, clinicians, and researchers by establishing a federated European infrastructure for cancer images through the EU-funded EUropean Federation for CAncer IMages (EUCAIM) project. This infrastructure, called Cancer Image Europe, builds on the AI for Health Imaging network (AI4HI), established European Research Infrastructures (Euro-BioImaging, BBMRI-ERIC, EATRIS, ECRIN, and ELIXIR), and numerous related partners providing access to research tools, images, and related clinical, pathology and molecular data. The infrastructure targets clinicians, researchers, and innovators by providing the means to develop and validate data-intensive AI-based and other IT-enabled clinical decision-making systems supporting precision medicine. Common data models, including a linking hyperontology, quality standards, compliance with the FAIR (Findability, Accessibility, Interoperability and Reusability) principles, data annotation, curation and anonymization services are provided to ensure data quality and interoperability, consistency and privacy. In summer 2024, the EUCAIM project released the first prototype of an EU-wide infrastructure, with a comprehensive dashboard integrating applications for dataset discovery, federated search, data access request, metadata harvesting, annotation, secure processing environments and federated processing.

Critical relevance statement: EUCAIM’s federated infrastructure for cancer image data advances medical research and related AI development in Europe. It addresses the current fragmentation and heterogeneity of data repositories is legally compliant, and facilitates collaboration among clinicians, researchers, and innovators.

Key Points: AI solutions to advance cancer care rely on large, high-quality real-world datasets. EUCAIM’s federated infrastructure for cancer image data empowers cancer research in Europe. It provides access to research tools, images, and related clinical, pathology and molecular data.

Purpose: To evaluate current MRI-based criteria for malignancy in mesorectal nodal structures in rectal cancer.

Method: Mesorectal nodal structures identified on baseline MRI as lymph nodes were anatomically compared to their corresponding structures histopathologically, reported as lymph nodes, tumour deposits or extramural venous invasion. All anatomically matched nodal structures from patients with primary surgery and all malignant nodal structures from patients with neoadjuvant treatment were included. Mixed-effects logistic regression models were used to evaluate the morphological criteria irregular margin, round shape, heterogeneous signal and nodal size, as well as the combined 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus criteria, with histopathological nodal status as the gold standard.

Results: In total, 458 matched nodal structures were included from 46 patients (mean age, 67.7 years ± 1.5 [SD], 27 men), of which 19 received neoadjuvant treatment. The strongest associations in the univariable model were found for short-axis diameter ≥ 5 mm (OR 21.43; 95% CI: 4.13–111.29, p < 0.001) and heterogeneous signal (OR 9.02; 95% CI: 1.33–61.08, p = 0.024). Only size remained significant in multivariable analysis (OR 12.32; 95% CI: 2.03–74.57, p = 0.006). When applying the ESGAR consensus criteria to create a binary classification of nodal status, the OR of malignant outcome for nodes with positive ESGAR was 8.23 (95% CI: 2.15–31.50, p = 0.002), with corresponding sensitivity and specificity of 54% and 85%, respectively.

Conclusion: The results confirm the role of morphological and size criteria in predicting lymph node metastases. However, the current criteria might not be accurate enough for nodal staging.

Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two year period, the FUTURE-AI guideline was established through consensus based on six guiding principles-fairness, universality, traceability, usability, robustness, and explainability. To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions. The recommendations cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.

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.

Introduction: Improvements in obstetric/neonatal care have increased the survival rates of extremely-preterm-born (EP), though followed by an increased number, and degrees, of white matter injury (WMI), and long-lasting neurodevelopmental impairments. Study aim: to investigate consequences of EP birth on the evolving white matter microstructure and myelination; and its causative role on acquired IQ and sensory-motor functions.

Participants and methods: Two MRI methods (3T; DTI and SyMRI) used to calculate white matter properties and associations with IQ and sensory-motor functions in children born FT and EP at 12-y. In total N = 57 children; 32 FT (GA 38-42), and 25 EP (GA 22- <27) successfully performed MRI and administered WISC-V and Beery visual-motor integration (VMI) test. DTI used for analyzing fractional anisotropy (FA), mean diffusivity (MD), axial- (AD) and radial diffusivity (RD). SyMRI (T1-T2-R1-R2 and PD) for analyzing myelin segmentation, white-matter (WM), gray-matter (GM), cerebrospinal fluid (CSF), and brain parenchyma volume (BPV).

Results: Children born EP had significant smaller total brain (BPV), less GM, WM and more CSF/ICV compared to FT born. GA correlated positively with brain volumes, myelin, and with IQ and Beery-WMI. For EPs myelin correlated positively with GA, WISC-V and Beery-VMI. EPs had significant higher RD and AD compared to FT, no significant FA difference found. Within EP-group, myelin content correlated positively with FA and negatively with RD and AD.

Conclusion: These findings suggest that EP births is associated with high-risk for long-lasting, globally compromised white matter myelination and microstructural organization which causes adverse cognitive and sensory-motor functions.

BACKGROUND: The delineation of intraprostatic lesions is vital for correct delivery of focal radiotherapy boost in patients with prostate cancer (PC). Errors in the delineation could translate into reduced tumour control and potentially increase the side effects. The purpose of this study is to compare PET-based delineation methods with histopathology.

MATERIALS AND METHODS: The study population consisted of 15 patients with confirmed high-risk PC intended for prostatectomy. [68Ga]-PSMA-PET/MR was performed prior to surgery. Prostate lesions identified in histopathology were transferred to the in vivo [68Ga]-PSMA-PET/MR coordinate system. Four radiation oncologists manually delineated intraprostatic lesions based on PET data. Various semi-automatic segmentation methods were employed, including absolute and relative thresholds, adaptive threshold, and multi-level Otsu threshold.

RESULTS: The gross tumour volumes (GTVs) delineated by the oncologists showed a moderate level of interobserver agreement with Dice similarity coefficient (DSC) of 0.68. In comparison with histopathology, manual delineations exhibited the highest median DSC and the lowest false discovery rate (FDR) among all approaches. Among semi-automatic approaches, GTVs generated using standardized uptake value (SUV) thresholds above 4 (SUV > 4) demonstrated the highest median DSC (0.41), with 0.51 median lesion coverage ratio, FDR of 0.66 and the 95th percentile of the Hausdorff distance (HD95%) of 8.22 mm.

INTERPRETATION: Manual delineations showed a moderate level of interobserver agreement. Compared to histopathology, manual delineations and SUV > 4 exhibited the highest DSC and the lowest HD95% values. The methods that resulted in a high lesion coverage were associated with a large overestimation of the size of the lesions.