Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort.

Introduction: Neuroblastoma, the most prevalent solid cancer in children, presents significant biological and clinical heterogeneity. This inherent heterogeneity underscores the need for more precise prognostic markers at the time of diagnosis to enhance patient stratification, allowing for more personalized treatment strategies. In response, this investigation developed a machine learning model using clinical, molecular, and magnetic resonance (MR) radiomics features at diagnosis to predict patient's overall survival (OS) and improve their risk stratification.

Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure.

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.

Guiding AI in radiology: ESR's recommendations for effective implementation of the European AI Act.

This statement has been produced within the European Society of Radiology AI Working Group and identifies the key policies of the EU AI Act as they pertain to medical imaging. It offers specific recommendations to policymakers and the professional community for the effective implementation of the legislation, addressing potential gaps and uncertainties. Key areas include AI literacy, classification rules for high-risk AI systems, data governance, transparency, human oversight, quality management, deployer obligations, regulatory sandboxes, post-market monitoring, information sharing, and market surveillance.

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.

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.

Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!

Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging.

Dynamic 18 F-FDG PET to detect differences among patients with progressive and relapsing multiple sclerosis: a pilot study.

Background: Patients with multiple sclerosis (MS) may remain in a relapsing-remitting (RRMS) course despite long-standing disease, while others will develop secondary progression (SPMS). Chronic inflammation and changes in the blood-brain barrier resulting in perturbed glucose metabolism may account for these differences. PET-MRI with kinetic analysis of 2-deoxy-2(18 F)fluoro-d-glucose (18 F-FDG) provides insight into glucose metabolism and has proven useful in several chronic inflammatory diseases.

Gray matter volume differences in intimate partner violence perpetrators and its role in explaining dropout and recidivism.

Aim: Psychological instruments that are employed to adequately explain treatment compliance and recidivism of intimate partner violence (IPV) perpetrators present a limited ability and certain biases. Therefore, it becomes necessary to incorporate new techniques, such as magnetic resonance imaging (MRI), to be able to surpass those limitations and measure central nervous system characteristics to explain dropout (premature abandonment of intervention) and recidivism.

Method: The main objectives of this study were: 1) to assess whether IPV perpetrators (n = 60) showed differences in terms of their brain's regional gray matter volume (GMV) when compared to a control group of non-violent men (n = 57); 2) to analyze whether the regional GMV of IPV perpetrators before starting a tailored intervention program explain treatment compliance (dropout) and recidivism rate.

Synergies Among Health Data Projects with Cancer Use Cases Based on Health Standards.

We are creating a synergy among European Health Data Space projects (e.g., IDERHA, EUCAIM, ASCAPE, iHELP, Bigpicture, and HealthData@EU pilot project) via health standards usage thanks to the HSBOOSTER EU Project since they are involved or using standards, and/or designing health ontologies.

Interventional solutions for post-surgical problems: a lymphatic leaks review.

The lymphatic circulation plays a crucial role in maintaining fluid balance and supporting immune responses by returning serum proteins and lipids to the systemic circulation. Lymphatic leaks, though rare, pose significant challenges post-radical neck surgery, oesophagectomy, and thoracic or retroperitoneal oncological resections, leading to heightened morbidity and mortality. Managing lymphatic leaks necessitates consideration of aetiology, severity, and volume of leakage.

Transforming experimental radiology: Design and implementation of an innovative ePACS image storage system for AI imaging research environments.

Introduction And Purpose: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security.

Executive dysfunction and cortical variations among intimate partner violence perpetrators and the association with sexism.

Malfunctioning in executive functioning has been proposed as a risk factor for intimate partner violence (IPV). This is not only due to its effects on behavioral regulation but also because of its association with other variables such as sexism. Executive dysfunctions have been associated with frontal and prefrontal cortical thickness.

Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm.

Positron Emission Tomography (PET) imaging after Y liver radioembolization is used for both lesion identification and dosimetry. Bayesian penalized likelihood (BPL) reconstruction algorithms are an alternative to ordered subset expectation maximization (OSEM) with improved image quality and lesion detectability. The investigation of optimal parameters for Y image reconstruction of Q.

Thrombotic risk and features of patients with inferior vena cava agenesis: a multicentre, retrospective, observational study.

Background: Inferior vena cava agenesis (IVCA) is a rare anomaly predisposing affected people to lower-limb venous thrombosis with low frequency of pulmonary embolism. Antenatal thrombosis and inherited thrombophilia have been suggested as causes of IVCA. However, there is little evidence on the clinical course and management of this condition.

Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors.

Purpose To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods A retrospective study included 419 patients (mean age, 29 months ± 34 [SD]; 220 male, 199 female) with neuroblastic tumors diagnosed between 2002 and 2023, within the scope of the PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences at diagnosis and/or after initial chemotherapy. Images underwent processing steps (denoising, inhomogeneity bias field correction, normalization, and resampling).

Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.

Objectives: In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 distinct regions: enhanced tumor, peritumoral edema, non-enhanced/necrotic tumor, and total tumor.

Material And Methods: A 3D U-Net neural network algorithm was developed for semantic segmentation of GBM. The training dataset was manually delineated by a group of expert neuroradiologists on MR images from the Brain Tumor Segmentation Challenge 2021 (BraTS2021) image repository, as ground truth labels for diverse glioma (GBM and low-grade glioma) subregions across four MR sequences (T1w, T1w-contrast enhanced, T2w, and FLAIR) in 1251 patients.

Documenting the de-identification process of clinical and imaging data for AI for health imaging projects.

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients.

CXCR4: From Signaling to Clinical Applications in Neuroendocrine Neoplasms.

There are several well-described molecular mechanisms that influence cell growth and are related to the development of cancer. Chemokines constitute a fundamental element that is not only involved in local growth but also affects angiogenesis, tumor spread, and metastatic disease. Among them, the C-X-C motif chemokine ligand 12 (CXCL12) and its specific receptor the chemokine C-X-C motif receptor 4 (CXCR4) have been widely studied.

Evolving Characteristics of Gadolinium-Based Contrast Agents for MR Imaging: A Systematic Review of the Importance of Relaxivity.

Gadolinium-based contrast agents (GBCAs) are widely and routinely used to enhance the diagnostic performance of magnetic resonance imaging and magnetic resonance angiography examinations. T1 relaxivity (r) is the measure of their ability to increase signal intensity in tissues and blood on T1-weighted images at a given dose. Pharmaceutical companies have invested in the design and development of GBCAs with higher and higher T1 relaxivity values, and "high relaxivity" is a claim frequently used to promote GBCAs, with no clear definition of what "high relaxivity" means, or general concurrence about its clinical benefit.

Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation.

Objectives: In lung cancer, one of the main limitations for the optimal integration of the biological and anatomical information derived from Positron Emission Tomography (PET) and Computed Tomography (CT) is the time and expertise required for the evaluation of the different respiratory phases. In this study, we present two open-source models able to automatically segment lung tumors on PET and CT, with and without motion compensation.

Materials And Methods: This study involved time-bin gated (4D) and non-gated (3D) PET/CT images from two prospective lung cancer cohorts (Trials 108237 and 108472) and one retrospective.