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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. However, to our knowledge, it has never been studied in MS., Objective: To explore potential differences in glucose distribution kinetics among individuals with long-standing SPMS and RRMS using dynamic 18-F-FDG PET-MRI., Methods: Dynamic 18-F-FDG PET-MRI scans were obtained in 11 patients with long-standing MS: 4 with RRMS and 7 with SPMS. Kinetic analysis of PET data was performed using a three-compartment model equation that represents plasma, tissue and 18 F-FDG phosphorylation. Individual rate constants of 18-F-FDG across the compartments were calculated., Results: Patients with SPMS exhibited a trend towards an increased net influx rate of glucose (p = 0.059) and an increased rate constant representing glucose phosphorylation. Together, the data suggest increased uptake of glucose and glycolysis in these patients., Conclusion: Dynamic 18 F-FDG PET-MRI is a feasible technique that may show information in vivo of glucose metabolism in MS. Although preliminary data suggest a potential radiological marker of progression in MS, further studies are required to confirm this hypothesis., Competing Interests: Declarations. Ethical standards: The study was approved by the Institutional Ethics Committee at Hospital Universitari I Politècnic La Fe and all patients gave written informed consent to participate in the study. Conflict of interest: On behalf of all authors, the corresponding author states that there is no conflict of interest., (© 2024. Fondazione Società Italiana di Neurologia.)

Background and Aims: A noteworthy proportion of patients with metabolic dysfunction-associated steatotic liver disease (MASLD) have an indeterminate vibration-controlled transient elastography (VCTE). Among these patients, we aimed to identify candidates for MASLD treatment by diagnosing significant fibrosis., Methods: This was a real-world prospective study including a large dataset of MASLD patients with paired VCTE and liver biopsy from 6 centers. A total of 1196 patients were recruited and divided in training (3 centers, Spain), internal validation (2 centers, Spain), and external validation (1 center, United States) cohorts. In patients with indeterminate liver stiffness measurement (LSM) (8-12 kPa), a diagnostic algorithm was developed to identify significant fibrosis, defined as histological stage ≥F2. Statistical analysis was performed using Gaussian mixture model (GMM) and k-means unsupervised clusterization., Results: From the eligible population, 33%, 29%, and 31% had indeterminate VCTE in the training, internal and external validation samples, respectively. The controlled attenuation parameter allowed the differentiation of GMM clusters with a cutoff of 280 dB/m (area under the curve, 0.89; 95% confidence interval, 0.86-0.97). Within patients with <280 dB/m, a LSM between 8.0-9.0 kPa showed a 93% sensitivity and a 91% negative predictive value to exclude significant fibrosis. Among patients with ≥280 dB/m, a LSM between 10.3 and 12.0 kPa diagnosed significant fibrosis with a 91% specificity. Applying this algorithm to the validation cohorts, 36% of the indeterminate VCTE were reallocated. The reallocated high-risk group showed a prevalence of 86% significant fibrosis, opening the therapeutic window for MASLD patients., Conclusions: To identify candidates for MASLD treatment among indeterminate VCTE, an algorithm-based on the sequential combination of LSM and controlled attenuation parameter thresholds can optimize the diagnosis of moderate-to-advanced fibrosis., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

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). Tumors were automatically segmented, and 107 shape, first-order, and second-order radiomics features were extracted, considered as the reference standard. Subsequently, the previous image processing settings were modified, and volumetric masks were applied. New radiomics features were extracted and compared with the reference standard. Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes ( P < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. Conclusion Modifications in the T2-weighted MRI preparation process in patients with neuroblastoma resulted in changes in radiomics features, with normalization identified as the most influential factor for reproducibility. Inhomogeneities removal had the least impact on radiomics features. Keywords: Pediatrics, MR Imaging, Oncology, Radiomics, Reproducibility, Repeatability, Neuroblastic Tumors Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Safdar and Galaria in this issue.

This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a noninvasive and reliable way to improve the diagnosis and prognosis in pediatric oncology. The PRIMAGE project is a European multi-center research initiative that focuses on developing medical imaging-derived artificial intelligence (AI) solutions designed to enhance overall management and decision-making for two types of pediatric cancer: neuroblastoma and diffuse intrinsic pontine glioma. To allow this, the PRIMAGE project has created an open-cloud platform that combines imaging, clinical, and molecular data together with AI models developed from this data, creating a comprehensive decision support environment for clinicians managing patients with these two cancers. In order to achieve this, a standardized data processing and analysis workflow was implemented to generate robust and reliable predictions for different clinical endpoints. Magnetic resonance (MR) image harmonization and registration was performed as part of the workflow. Subsequently, an automated tool for the detection and segmentation of tumors was trained and internally validated. The Dice similarity coefficient obtained for the independent validation dataset was 0.997, indicating compatibility with the manual segmentation variability. Following this, radiomics and deep features were extracted and correlated with clinical endpoints. Finally, reproducible and relevant imaging quantitative features were integrated with clinical and molecular data to enrich both the predictive models and a set of visual analytics tools, making the PRIMAGE platform a complete clinical decision aid system. In order to ensure the advancement of research in this field and to foster engagement with the wider research community, the PRIMAGE data repository and platform are currently being integrated into the European Federation for Cancer Images (EUCAIM), which is the largest European cancer imaging research infrastructure created to date., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To develop a QA procedure, easy to use, reproducible and based on open-source code, to automatically evaluate the stability of different metrics extracted from CT images: Hounsfield Unit (HU) calibration, edge characterization metrics (contrast and drop range) and radiomic features., Methods: The QA protocol was based on electron density phantom imaging. Home-made open-source Python code was developed for the automatic computation of the metrics and their reproducibility analysis. The impact on reproducibility was evaluated for different radiation therapy protocols, and phantom positions within the field of view and systems, in terms of variability (Shapiro-Wilk test for 15 repeated measurements carried out over three days) and comparability (Bland-Altman analysis and Wilcoxon Rank Sum Test or Kendall Rank Correlation Coefficient)., Results: Regarding intrinsic variability, most metrics followed a normal distribution (88% of HU, 63% of edge parameters and 82% of radiomic features). Regarding comparability, HU and contrast were comparable in all conditions, and drop range only in the same CT scanner and phantom position. The percentages of comparable radiomic features independent of protocol, position and system were 59%, 78% and 54%, respectively. The non-significantly differences in HU calibration curves obtained for two different institutions (7%) translated in comparable Gamma Index G (1 mm, 1%, >99%)., Conclusions: An automated software to assess the reproducibility of different CT metrics was successfully created and validated. A QA routine proposal is suggested., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Objectives: To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort., Methods: An international multicenter, multivendor imaging repository of patients with neuroblastic tumors was used to validate the performance of a trained Machine Learning (ML) tool to identify and delineate primary neuroblastoma tumors. The dataset was heterogeneous and completely independent from the one used to train and tune the model, consisting of 300 children with neuroblastic tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and 49 after finalization of the first phase of chemotherapy). The automatic segmentation algorithm was based on a nnU-Net architecture developed within the PRIMAGE project. For comparison, the segmentation masks were manually edited by an expert radiologist, and the time for the manual editing was recorded. Different overlaps and spatial metrics were calculated to compare both masks., Results: The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944-1.000 (median; Q1-Q3). In 18 MR sequences (6%), the net was not able neither to identify nor segment the tumor. No differences were found regarding the MR magnetic field, type of T2 sequence, or tumor location. No significant differences in the performance of the net were found in patients with an MR performed after chemotherapy. The time for visual inspection of the generated masks was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where manual editing was needed (136 masks) required 124 ± 120 s., Conclusions: The automatic CNN was able to locate and segment the primary tumor on the T2-weighted images in 94% of cases. There was an extremely high agreement between the automatic tool and the manually edited masks. This is the first study to validate an automatic segmentation model for neuroblastic tumor identification and segmentation with body MR images. The semi-automatic approach with minor manual editing of the deep learning segmentation increases the radiologist's confidence in the solution with a minor workload for the radiologist.