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Introduction Resistant to treatment depression (RTD) is a prevalent disease that implies functional impairment and high resources consumption. Theta Burst Transcranial Magnetic Stimulation (TBS) in dorsolateral prefrontal cortex (DLPFC) is a novel therapy that has shown experimental efficacy and as an adjuvant strategy in RTD. The implementation of TBS in the Public National Health Service requires cost-effective protocols that achieve earlier responses and higher rates of effectiveness, and whose design is based on biomarkers of response so as to adequately select candidate patients. Objectives To assess the efficacy and safety of novel bilateral and unilateral intensive/spaced protocols of TBS in outpatients with unipolar/bipolar RTD compared with sham stimulation. Specific objectives: I) Comparison of mood change at the end of TBS protocol in the groups and maintenance of its effect at 3 months; II) Characterization of cerebral connectivity and metabolism patterns related to the effects of TBS; III) Analysis of the interaction between clinical and neuroimaging predictors so as to determine a RTD profile of patient that can benefit from TBS. Methods A two-year randomized double-blind clinical trial with 96 outpatients with TRD will be carried out. Participants will be randomized in three groups (active bilateral, active left and sham right and sham bilateral) to receive 22 active/sham sessions of continuous TBS (right DLPFC) and intermittent TBS (left DLPFC) during 6 weeks (w 1-2: 5 sessions/w, w 3-6: 3 sessions/w). Assessments of mood and side-effects will be carried out weekly. Functional neuroimaging will be a a simultaneous PET/MR acquisition previous and at the end of TBS treatment. Between-group comparisons of efficacy in terms of Hamilton Depression Rating Scale (HDRS-17) from basal to 6th week will be performed using controlled mixed regression models. Between-group comparisons will be made at baseline and after treatment, studying the imaging biomarkers obtained. Clinical and neuroimaging predictors of response will be integrated in machine learning models. Results The expected results of the project are summarized in the following hypotheses: 1) The intensive and spaced protocols of TBS as an adjuvant antidepressant treatment will have greater efficacy than sham stimulation in patients with TRD. 2) Both protocols will be safe, with mild side effects. 3) Unilateral and bilateral TBS protocols will involve changes in connectivity and cerebral metabolic consumption mainly in regions of the fronto-cingulo-temporal circuitry. 4) PET/MR imaging biomarkers will allow us to differentiate whether patients have responded to treatment with TBS. Conclusions This project may help to improve resistant to treatment depression management by personalizing TMS treatment with the use of neuroimaging biomarkers. Disclosure of Interest None Declared

Objectives: The aim of this study was to evaluate the use of phase-contrast MR imaging to diagnose normal pressure hydrocephalus (NPH) and differentiate it from other neurological disorders with similar clinical symptoms. Methods: The study included 108 subjects, of whom 61 were healthy controls and 47, patients; in the patient group, 19 had cerebrovascular disease (CVD) and 28 had NPH. All patients underwent a phase-contrast MRI study and several CSF flow and velocity parameters were measured at the aqueduct of Sylvius. Discriminant analyses were performed to evaluate the classification capacity of both individual parameters and the combination of different parameters. Results: Maximum diastolic velocity, mean flow, and stroke volume showed statistically significant differences that could be used to distinguish between NPH and CVD patients (P

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

La imagen de resonancia magnética en contraste de fase permite estudiar la dinámica del líquido cefalorraquídeo (LCR) perimedular de manera cuantitativa. Sin embargo la anatomía propia del espacio subaracnoideo dificulta la segmentación del LCR debido a la presencia de estructuras vasculares y nervios raquídeos. El objetivo de este trabajo es describir un método de segmentación semiautomático para el estudio de la dinámica del LCR perimedular. El proceso se inicializa con un punto semilla dentro de la región a analizar. El algoritmo crea un mapa de correlación, calcula un valor de umbral y clasifica píxeles de LCR combinando diversas características temporales del comportamiento del flujo como atributos de entrada a un algoritmo k-medias. Un observador llevó a cabo 10 veces la segmentación en 5 sujetos sanos y se calculó el volumen por ciclo y el área en el espacio perimedular C2C3. Las variaciones de las medidas fueron evaluadas como una estimación de la reproducibilidad del método. Para esto se calculó el coeficiente de variación. La variabilidad de las medidas fue menor del 5%. El método facilita la cuantificación del LCR perimedular. En 16 sujetos sanos se cuantificó el volumen por ciclo de LCR y el área en el espacio C2C3 y cisterna prepontina.

Many abstracts submitted to medical meetings never come to full publication in peer-reviewed journals. From the 2,992 abstracts presented at the 1994-1998 Spanish Congresses of Radiology, 464 (15%) were published as full articles in journals covered by the Medline and IME (Índice Médico Español), the Spanish medical database. The publication rate of oral presentations was higher than that of posters (18% versus 13%). Collaboration between radiologists and clinicians and between radiologists from different institutions increased full publication (21% and 27%, respectively) compared to abstracts from just one institution (14%). Therefore, oral presentations, multi-disciplinary and multi-institutional collaboration in the abstract predicted full publication. [ABSTRACT FROM AUTHOR]

In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40 of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40 contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the compensation of tumor motion did not have a significant impact on the quantitative PET parameters. The variability of PET parameters due to voxel size in image reconstruction was more significant than variability due to voxel size in image post-resampling. In conclusion, most of the parameters (apart from the contrast of neighborhood matrix) were robust to the motion compensation implied by 4D-PET/CT. The impact on parameter variability due to the voxel size in image reconstruction and in image post-resampling could not be assumed to be equivalent. [ABSTRACT FROM AUTHOR]

A major source of error in quantitative PET/CT scans of lung cancer tumors is respiratory motion. Regarding the variability of PET texture features (TF), the impact of respiratory motion has not been properly studied with experimental phantoms. The primary aim of this work was to evaluate the current use of PET texture analysis for heterogeneity characterization in lesions affected by respiratory motion. Twenty-eight heterogeneous lesions were simulated by a mixture of alginate and 18F-fluoro-2-deoxy-D-glucose (FDG). Sixteen respiratory patterns were applied. Firstly, the TF response for different heterogeneous phantoms and its robustness with respect to the segmentation method were calculated. Secondly, the variability for TF derived from PET image with (gated, G-) and without (ungated, U-) motion compensation was analyzed. Finally, TF complementarity was assessed. In the comparison of TF derived from the ideal contour with respect to TF derived from 40%-threshold and adaptive-threshold PET contours, 7/8 TF showed strong linear correlation (LC) (p  <  0.001, r  >  0.75), despite a significant volume underestimation. Independence of lesion movement (LC in 100% of the combined pairs of movements, p  <  0.05) was obtained for 1/8 TF with U-image (width of the volume-activity histogram, WH) and 4/8 TF with G-image (WH and energy (ENG), local-homogeneity (LH) and entropy (ENT), derived from the co-ocurrence matrix). Their variability in terms of the coefficient of variance () resulted in (WH)  =  0.18 on the U-image and (WH)  =  0.24, (ENG)  =  0.15, (LH)  =  0.07 and (ENT)  =  0.06 on the G-image. Apart from WH (r  >  0.9, p  <  0.001), not one of these TF has shown LC with Cmax. Complementarity was observed for the TF pairs: ENG–LH, CONT (contrast)–ENT and LH–ENT. In conclusion, the effect of respiratory motion should be taken into account when the heterogeneity of lung cancer is quantified on PET/CT images. Despite inaccurate volume delineation, TF derived from 40% and COA contours could be reliable for their prognostic use. The TF that exhibited simultaneous added value and independence of lesion movement were ENG and ENT computed from the G-image. Their use is therefore recommended for heterogeneity quantification of lesions affected by respiratory motion. [ABSTRACT FROM AUTHOR]

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., Results: IPV perpetrators presented increased GMV in the cerebellum and the occipital, temporal, and subcortical brain regions compared to controls. There were also bilateral differences in the occipital pole and subcortical structures (thalamus, and putamen), with IPV perpetrators presenting reduced GMV in the above-mentioned brain regions compared to controls. Moreover, while a reduced GMV of the left pallidum explained dropout, a considerable number of frontal, temporal, parietal, occipital, subcortical and limbic regions added to dropout to explain recidivism., Conclusions: Our study found that certain brain structures not only distinguished IPV perpetrators from controls but also played a role in explaining dropout and recidivism. Given the multifactorial nature of IPV perpetration, it is crucial to combine neuroimaging techniques with other psychological instruments to effectively create risk profiles of IPV perpetrators., 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 © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., Methods and Results: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing., Discussion and Conclusions: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology., 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 © 2024 Elsevier B.V. All rights reserved.)

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. For model construction, the ground truth (GT) was defined by consensus of two experts, and the nnU-Net with 5-fold cross-validation was applied to 560 4D-images for PET and 100 3D-images for CT. The test sets included 270 4D- images and 19 3D-images for PET and 80 4D-images and 27 3D-images for CT, recruited at 10 different centres., Results: In the performance evaluation with the multicentre test sets, the Dice Similarity Coefficients (DSC) obtained for our PET model were DSC(4D-PET) = 0.74 ± 0.06, improving 19% relative to the DSC between experts and DSC(3D-PET) = 0.82 ± 0.11. The performance for CT was DSC(4D-CT) = 0.61 ± 0.28 and DSC(3D-CT) = 0.63 ± 0.34, improving 4% and 15% relative to DSC between experts., Conclusions: Performance evaluation demonstrated that the automatic segmentation models have the potential to achieve accuracy comparable to manual segmentation and thus hold promise for clinical application. The resulting models can be freely downloaded and employed to support the integration of 3D- or 4D- PET/CT and to facilitate the evaluation of its impact on lung cancer clinical practice., Clinical Relevance Statement: We provide two open-source nnU-Net models for the automatic segmentation of lung tumors on PET/CT to facilitate the optimal integration of biological and anatomical information in clinical practice. The models have superior performance compared to the variability observed in manual segmentations by the different experts for images with and without motion compensation, allowing to take advantage in the clinical practice of the more accurate and robust 4D-quantification., Key Points: Lung tumor segmentation on PET/CT imaging is limited by respiratory motion and manual delineation is time consuming and suffer from inter- and intra-variability. Our segmentation models had superior performance compared to the manual segmentations by different experts. Automating PET image segmentation allows for easier clinical implementation of biological information., (© 2024. The Author(s).)

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. The in-house test was performed on 50 GBM patients from our cohort (PerProGlio project). By exploring various hyperparameters, the network's performance was optimized, and the most optimal parameter configuration was identified. The assessment of the optimized network's performance utilized Dice scores, precision, and sensitivity metrics., Results: Our adaptation of the 3D U-net with additional residual blocks demonstrated reliable performance on both the BraTS2021 dataset and the in-house PerProGlio cohort, employing only T1w-ce sequences for enhancement and non-enhanced/necrotic tumor models and T1w-ce + T2w + FLAIR for peritumoral edema and total tumor. The mean Dice scores (training and test) were 0.89 and 0.75; 0.75 and 0.64; 0.79 and 0.71; and 0.60 and 0.55, for total tumor, edema, enhanced tumor, and non-enhanced/necrotic tumor, respectively., Conclusions: The results underscore the high precision with which our network can effectively segment GBM tumors and their distinct subregions. The level of accuracy achieved agrees with the coefficients recorded in previous GBM studies. In particular, our approach allows model specialization for each of the different tumor subregions employing only those MR sequences that provide value for segmentation., (© 2024. CARS.)

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. Therefore, our first aim was to assess differences in cortical thickness in frontal and prefrontal regions, as well as levels of sexism, between two groups of IPV perpetrators (with and without executive dysfunctions) and a control group of non-violent men. Second, we analyzed whether the cortical thickness in the frontal and prefrontal regions would explain sexism scores. Our results indicate that IPV perpetrators classified as dysexecutive exhibited a lower cortical thickness in the right rostral anterior cingulate superior frontal bilaterally, caudal middle frontal bilaterally, right medial orbitofrontal, right paracentral, and precentral bilaterally when compared with controls. Furthermore, they exhibited higher levels of sexism than the rest of the groups. Most importantly, in the brain structures that distinguished between groups, lower thickness was associated with higher sexism scores. This research emphasizes the need to incorporate neuroimaging techniques to develop accurate IPV profiles or subtypes based on neuropsychological functioning., (© The Author(s) 2024. Published by Oxford University Press.)

Introduction: Pneumatosis intestinalis is a radiological finding characterized by the presence of gas in the bowel wall that is associated with multiple entities. Our aim is to know its incidence in lung transplant patients, its physiopathology and its clinical relevance., Methods: A search of patients with pneumatosis intestinalis was performed in the database of the Lung Transplant Unit of our hospital. The presence of pneumatosis after transplantation was confirmed in all of them and relevant demographic, clinical and imaging variables were collected to evaluate its association and clinical expression, as well as the therapeutic approach after the findings., Results: The incidence of pneumatosis intestinalis after lung transplantation in our center was 3.1% (17/546), developing between 9 and 1270 days after transplantation (mean, 198 days; median 68 days). Most of the patients were asymptomatic or with mild symptoms, without any major analytical alterations, and with a cystic and expansive radiological appearance. Pneumoperitoneum was associated in 70% of the patients (12/17). Conservative treatment was chosen in all cases. The mean time to resolution was 389 days., Conclusion: Pneumatosis intestinalis in lung transplant patients is a rare complication of uncertain origin, which can appear for a very long period of time after transplantation. It has little clinical relevance and can be managed without other diagnostic or therapeutic interventions., (Copyright © 2023 SERAM. Published by Elsevier España, S.L.U. 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.

Positron Emission Tomography (PET) imaging after 90 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 90 Y image reconstruction of Q.Clear, a commercial BPL algorithm developed by General Electric (GE), in PET/MR is a field of interest and the subject of this study. The NEMA phantom was filled at an 8:1 sphere-to-background ratio. Acquisitions were performed on a PET/MR scanner for clinically relevant activities between 0.7 and 3.3 MBq/ml. Reconstructions with Q.Clear were performed varying the β penalty parameter between 20 and 6000, the acquisition time between 5 and 20 min and pixel size between 1.56 and 4.69 mm. OSEM reconstructions of 28 subsets with 2 and 4 iterations with and without Time-of-Flight (TOF) were compared to Q.Clear with β = 4000. Recovery coefficients (RC), their coefficient of variation (COV), background variability (BV), contrast-to-noise ratio (CNR) and residual activity in the cold insert were evaluated. Increasing β parameter lowered RC, COV and BV, while CNR was maximized at β = 4000; further increase resulted in oversmoothing. For quantification purposes, β = 1000-2000 could be more appropriate. Longer acquisition times resulted in larger CNR due to reduced image noise. Q.Clear reconstructions led to higher CNR than OSEM. A β of 4000 was obtained for optimal image quality, although lower values could be considered for quantification purposes. An optimal acquisition time of 15 min was proposed considering its clinical use., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

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. The overexpression in cell membranes of CXCR4 has been shown to be associated with the development of different kinds of histological malignancies, such as adenocarcinomas, epidermoid carcinomas, mesenchymal tumors, or neuroendocrine neoplasms (NENs). The molecular synapsis between CXCL12 and CXCR4 leads to the interaction of G proteins and the activation of different intracellular signaling pathways in both gastroenteropancreatic (GEP) and bronchopulmonary (BP) NENs, conferring greater capacity for locoregional aggressiveness, the epithelial-mesenchymal transition (EMT), and the appearance of metastases. Therefore, it has been hypothesized as to how to design tools that target this receptor. The aim of this review is to focus on current knowledge of the relationship between CXCR4 and NENs, with a special emphasis on diagnostic and therapeutic molecular targets.

Purpose: To evaluate whether the Centiloid Scale may be used to diagnose Alzheimer's Disease (AD) pathology effectively with the only use of amyloid PET imaging modality from a brain-dedicated PET scanner., Methods: This study included 26 patients with amyloid PET images with 3 different radiotracers. All patients were acquired both on a PET/CT and a brain-dedicated PET scanner (CareMiBrain, CMB), from which 4 different reconstructions were implemented. A new pipeline was proposed and used for the PET image analysis based on the original Centiloid Scale processing pipeline, but with only PET images. The Youden's Index was employed to calculate the optimal cutoffs for diagnosis and evaluated by the AUC, accuracy, precision, and recall metrics., Results: The Centiloid Scale (CL) processing pipeline was validated with and without the use of MR images. The CL cutoffs for AD pathology diagnosis on the PET/CT and the 4 CMB reconstructions were 34.4 ± 2.2, 43.5 ± 3.5, 51.9 ± 12.5, 57.5 ± 6.8 and 41.8 ± 1.2 respectively. Overall, for these cutoffs all metrics obtained the maximum score., Conclusion: The Centiloid scale applied to PET images allows for AD pathology diagnosis. The CMB scanner can be used with the Centiloid scale to automatically assist in the diagnosis of AD pathology, relieving the large burden of neurodegenerative diseases on a traditional PET/CT., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Eng. Constantino Morera is employed at Oncovision, S.A, and acts as the CareMiBrain Project Coordinator and PET Imaging Team Leader of this company. Dr. Enrique Muñoz is employed at Oncovision, S.A, as the Senior Image Reconstruction engineer., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

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: This article presents the design and validation of evaluation criteria checklist aimed at facilitating decision-making processes regarding participation in research projects and allocation of resources before the preparation of research proposals., Materials and Methods: A multidisciplinary team developed a comprehensive evaluation focusing on the proposal preparation phase of research projects. A Delphi survey method was used to establish a connection between the relevance of the project and the possible success of research proposals. Assessment criteria were agreed upon, each assigned specific weights. The results of the survey were applied to a database of 62 proposals for which our research group sought funding during 2020-2021. The method was validated using the funding body's outcomes (approval or rejection) of the submitted proposals as the ground truth per project type (national, European and regional)., Results: The results of the survey generated a checklist of 8 criteria (excellence, impact, and efficiency aspects) that effectively assess the possibility of success of research proposals during the preparatory phase. For national projects, the tool validation demonstrated a sensitivity of 100% and a specificity of 76.19%; European projects exhibited a sensitivity of 100% and a specificity of 53.84%; and regional projects showed a sensitivity of 80% and a specificity of 30%., Conclusions: By establishing an agreed set of evaluation criteria, the developed comprehensive index enables a more precise decision support tool for the participation in research proposals and the allocation of necessary resources. This control system saves valuable time and effort for research groups while enhancing the overall efficiency of available resources., 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 © 2024. Published by Elsevier B.V.)

Background: There is an urgent need to better understand the mechanisms associated with the development, progression, and onset of recurrence after initial surgery in glioblastoma (GBM). The use of integrative phenotype-focused -omics technologies such as proteomics and lipidomics provides an unbiased approach to explore the molecular evolution of the tumor and its associated environment., Methods: We assembled a cohort of patient-matched initial (iGBM) and recurrent (rGBM) specimens of resected GBM. Proteome and metabolome composition were determined by mass spectrometry-based techniques. We performed neutrophil-GBM cell coculture experiments to evaluate the behavior of rGBM-enriched proteins in the tumor microenvironment. ELISA-based quantitation of candidate proteins was performed to test the association of their plasma concentrations in iGBM with the onset of recurrence., Results: Proteomic profiles reflect increased immune cell infiltration and extracellular matrix reorganization in rGBM. ASAH1, SYMN, and GPNMB were highly enriched proteins in rGBM. Lipidomics indicates the downregulation of ceramides in rGBM. Cell analyses suggest a role for ASAH1 in neutrophils and its localization in extracellular traps. Plasma concentrations of ASAH1 and SYNM show an association with time to recurrence., Conclusions: We describe the potential importance of ASAH1 in tumor progression and development of rGBM via metabolic rearrangement and showcase the feedback from the tumor microenvironment to plasma proteome profiles. We report the potential of ASAH1 and SYNM as plasma markers of rGBM progression. The published datasets can be considered as a resource for further functional and biomarker studies involving additional -omics technologies., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Pyridoxine (pyr) is a versatile molecule that forms part of the family of B vitamins. It is used to treat and prevent vitamin B 6 deficiency and certain types of metabolic disorders. Moreover, the pyridoxine molecule has been investigated as a suitable ligand toward metal ions. Nevertheless, the study of the magnetic properties of metal complexes containing lanthanide(III) ions and this biomolecule is unexplored. We have synthesized and characterized a novel pyridoxine-based Gd III complex of formula [Gd III (pyr) 2 (H 2 O) 4 ]Cl 3 · 2 H 2 O ( 1 ) [pyr = pyridoxine]. 1 crystallizes in the triclinic system and space group P ī. In its crystal packing, cationic [Gd(pyr) 2 (H 2 O) 4 ] 3+ entities are connected through H-bonding interactions involving non-coordinating water molecules and chloride anions. In addition, Hirshfeld surfaces of 1 were calculated to further investigate their intermolecular interactions in the crystal lattice. Our investigation of the magnetic properties of 1 , through ac magnetic susceptibility measurements, reveals the occurrence of a slow relaxation in magnetization in this mononuclear Gd III complex, indicating an unusual single-ion magnet (SIM) behavior for this pseudo-isotropic metal ion at very low temperatures. We also studied the relaxometric properties of 1 , as a potential contrast agent for high-field magnetic resonance imaging (MRI), from solutions of 1 prepared in physiological serum (0.0-3.2 mM range) and measured at 3 T on a clinical MRI scanner. The values of relaxivity obtained for 1 are larger than those of some commercial MRI contrast agents based on mononuclear Gd III systems.

To expand the scientific literature on how resting state functional connectivity (rsFC) magnetic resonance imaging (MRI) (or the measurement of the strength of the coactivation of two brain regions over a sustained period of time) can be used to explain treatment compliance and recidivism among intimate partner violence (IPV) perpetrators. Therefore, our first aim was to assess whether men convicted of IPV (n = 53) presented different rsFC patterns from a control group of non-violent (n = 47) men. We also analyzed if the rsFC of IPV perpetrators before staring the intervention program could explain treatment compliance and recidivism one year after the intervention ended. The rsFC was measured by applying a whole brain analysis during a resting period, which lasted 45 min. IPV perpetrators showed higher rsFC in the occipital brain areas compared to controls. Furthermore, there was a positive association between the occipital pole (OP) and temporal lobes (ITG) and a negative association between the occipital (e.g., occipital fusiform gyrus, visual network) and both the parietal lobe regions (e.g., supramarginal gyrus, parietal operculum cortex, lingual gyrus) and the putamen in IPV perpetrators. This pattern was the opposite in the control group. The positive association between many of these occipital regions and the parietal, frontal, and temporal regions explained treatment compliance. Conversely, treatment compliance was also explained by a reduced rsFC between the rostral prefrontal cortex and the frontal gyrus and both the occipital and temporal gyrus, and between the temporal and the occipital and cerebellum areas and the sensorimotor superior networks. Last, the enhanced rsFC between the occipital regions and both the cerebellum and temporal gyrus predicted recidivism. Our results highlight that there are specific rsFC patterns that can distinguish IPV perpetrators from controls. These rsFC patterns could be useful to explain treatment compliance and recidivism among IPV perpetrators., (© 2024. The Author(s).)

At the European Society of Radiology (ESR), we strive to provide evidence for radiological practices that improve patient outcomes and have a societal impact. Successful translation of radiological research into clinical practice requires multiple factors including tailored methodology, a multidisciplinary approach aiming beyond technical validation, and a focus on unmet clinical needs. Low levels of evidence are a threat to radiology, resulting in low visibility and credibility. Here, we provide the background and rationale for the thematic series Translating radiological research into practice-from discovery to clinical impact, inviting authors to describe their processes of achieving clinically impactful radiological research. We describe the challenges unique to radiological research. Additionally, a survey was sent to non-radiological clinical societies. The majority of respondents (6/11) were in the field of gastrointestinal/abdominal medicine. The implementation of CT/MRI techniques for disease characterisation, detection and staging of cancer, and treatment planning and radiological interventions were mentioned as the most important radiological developments in the past years. The perception was that patients are substantially unaware of the impact of these developments. Unmet clinical needs were mostly early diagnosis and staging of cancer, microstructural/functional assessment of tissues and organs, and implant assessment. All but one respondent considered radiology important for research in their discipline, but five indicated that radiology is currently not involved in their research. Radiology research holds the potential for being transformative to medical practice. It is our responsibility to take the lead in studies including radiology and strive towards the highest levels of evidence.Critical relevance statement For radiological research to make a clinical and societal impact, radiologists should take the lead in radiological studies, go beyond the assessment of technical feasibility and diagnostic accuracy, and-in a multidisciplinary approach-address clinical unmet needs.Key points• Multiple factors are essential for radiological research to make a clinical and societal impact.• Radiological research needs to go beyond diagnostic accuracy and address unmet clinical needs.• Radiologists should take the lead in radiological studies with a multidisciplinary approach., (© 2024. The Author(s).)

Introduction: Increased femoral anteversion (FAV) can have many clinical manifestations, including anterior knee pain (AKP). To our knowledge, no studies have measured the location of FAV in a cohort of female AKP patients. The objective of this research is to determine whether the increased FAV in AKP females originates above the lesser trochanter, below the lesser trochanter or at both levels., Materials and Methods: Thrity-seven consecutive AKP female patients (n = 66 femurs) were recruited prospectively. There were 17 patients (n = 26 femurs; mean age of 28 years) in whom the suspicion for the increased FAV of the femur was based on the clinical examination (pathological group-PG). The control group (CG) consisted of 20 patients (n = 40 femurs; mean age of 29 years) in whom there was no increased FAV from the clinical standpoint. All of them underwent a torsional computed tomography of the lower limbs. FAV was measured according to Murphy´s method. A segmental analysis of FAV was performed using the lesser trochanter as a landmark., Results: Significant differences in the total FAV (18.7 ± 5.52 vs. 42.46 ± 6.33; p < 0.001), the neck version (54.88 ± 9.64 vs. 64.27 ± 11.25; p = 0.0006) and the diaphysis version (- 36.17 ± 8.93 vs. - 21.81 ± 11.73; p < 0.001) were observed between the CG and the PG. The difference in the diaphyseal angle between CG and PG accounts for 60% of the total difference between healthy and pathological groups, while the difference between both groups in the angle of the neck accounts for 40%., Conclusion: In chronic AKP female patients with increased FAV, the two segments of the femur contribute to the total FAV, with a different pattern among patients and controls, being the compensation mechanism of the diaphysis much lower in the pathological femurs than in the controls., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: The aim of this study was to determine whether MRI texture analysis could predict the prognosis of patients with non-specific chronic low back pain., Methods: A prospective observational study was conducted on 100 patients with non-specific chronic low back pain, who underwent a conventional MRI, followed by rehabilitation treatment, and revisited after 6 months. Sociodemographic variables, numeric pain scale (NPS) value, and the degree of disability as measured by the Roland-Morris disability questionnaire (RMDQ), were collected. The MRI analysis included segmentation of regions of interest (vertebral endplates and intervertebral disks from L3-L4 to L5-S1, paravertebral musculature at the L4-L5 space) to extract texture variables (PyRadiomics software). The classification random forest algorithm was applied to identify individuals who would improve less than 30% in the NPS or would score more than 4 in the RMDQ at the end of the follow-up. Sensitivity, specificity, and the area under the ROC curve were calculated., Results: The final series included 94 patients. The predictive model for classifying patients whose pain did not improve by 30% or more offered a sensitivity of 0.86, specificity 0.57, and area under the ROC curve 0.71. The predictive model for classifying patients with a RMDQ score 4 or more offered a sensitivity of 0.83, specificity of 0.20, and area under the ROC curve of 0.52., Conclusion: The texture analysis of lumbar MRI could help identify patients who are more likely to improve their non-specific chronic low back pain through rehabilitation programs, allowing a personalized therapeutic plan to be established., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: This study investigates the functional brain connectivity in patients with anterior knee pain (AKP). While biomechanical models are frequently employed to investigate AKP, it is important to recognize that pain can manifest even in the absence of structural abnormalities. Leveraging the capabilities of functional magnetic resonance imaging (fMRI), this research aims to investigate the brain mechanisms present in AKP patients., Methods: Forty-five female subjects (24 AKP patients, 21 controls) underwent resting-state fMRI and T1-weighted structural MRI. Functional brain connectivity patterns were analyzed, focusing on pain network areas, and the influence of catastrophizing thoughts was evaluated., Results: Comparing patients and controls, several findings emerged. First, patients with AKP exhibited increased correlation between the right supplementary motor area and cerebellum I, as well as decreased correlation between the right insula and the left rostral prefrontal cortex and superior frontal gyrus. Second, in AKP patients with catastrophizing thoughts, there was increased correlation of the left lateral parietal cortex with two regions of the right cerebellum (II and VII) and the right pallidum, and decreased correlation between the left medial frontal gyrus and the right thalamus. Furthermore, the correlation between these regions showed promising results for discriminating AKP patients from controls, achieving a cross-validation accuracy of 80.5%., Conclusions: Resting-state fMRI revealed correlation differences in AKP patients compared to controls and based on catastrophizing thoughts levels. These findings shed light on neural correlates of chronic pain in AKP, suggesting that functional brain connectivity alterations may be linked to pain experience in this population., Relevance Statement: Etiopathogenesis of pain in anterior knee pain patients might not be limited to the knee, but also to underlying alterations in the central nervous system: cortical changes might lead a perpetuation of pain., Key Points: • Anterior knee pain patients exhibit distinct functional brain connectivity compared to controls, and among catastrophizing subgroups. • Resting-state fMRI reveals potential for discriminating anterior knee pain patients with 80.5% accuracy. • Functional brain connectivity differences improve understanding of pain pathogenesis and objective anterior knee pain identification., (© 2023. European Society of Radiology (ESR).)

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.)

As part of a clinical validation of a new brain-dedicated PET system (CMB), image quality of this scanner has been compared to that of a whole-body PET/CT scanner. To that goal, Hoffman phantom and patient data were obtined with both devices. Since CMB does not use a CT for attenuation correction (AC) which is crucial for PET images quality, this study includes the evaluation of CMB PET images using emission-based or CT-based attenuation maps. PET images were compared using 34 image quality metrics. Moreover, a neural network was used to evaluate the degree of agreement between both devices on the patients diagnosis prediction. Overall, results showed that CMB images have higher contrast and recovery coefficient but higher noise than PET/CT images. Although SUVr values presented statistically significant differences in many brain regions, relative differences were low. An asymmetry between left and right hemispheres, however, was identified. Even so, the variations between the two devices were minor. Finally, there is a greater similarity between PET/CT and CMB CT-based AC PET images than between PET/CT and the CMB emission-based AC PET images., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background and Aims: The primary objective was to analyze the prevalence and degree of professional burnout in radiologists in Spain. Secondary objectives were to identify possible factors that increase or decrease the risk of burnout to enable preventive and corrective measures, decrease the stress associated with this condition, and thereby increase radiologists' performance and satisfaction at work., Material and Methods: This cross-sectional observational study used a voluntary, anonymous online survey of attending radiologists and residents through Google Forms®. The survey was structured into three sections: a qualitative assessment of the degree of professional burnout with the Maslach Burnout Inventory Human Services Survey (MBI-HSS), a series of sociodemographic and work-related questions, and a final section centered on possible causes of stress and improvements to the working environment. The results of the survey were analyzed statistically to determine which variables were associated with burnout syndrome as well as to identify possible risk factors and protective factors., Results: After disseminating the survey through social networks and email contacts, we received a total of 226 responses (175 from attending radiologists and 51 from residents; 52% men; mean age, 41 ± 11 years; age range, 25-68). The prevalence of the syndrome was 33%, without significant differences between attending radiologists and residents. No risk factors associated with burnout were identified. Teaching in the workplace was the only protective factor., Conclusions: One-third of the respondents had burnout syndrome. Because the consequences of this syndrome can affect professionals' personal life and their ability to do their jobs, early detection and intervention should be prioritized., (Copyright © 2021 SERAM. Published by Elsevier España, S.L.U. All rights reserved.)

Accumulation of excess iron in the body, or systemic iron overload, results from a variety of causes. The concentration of iron in the liver is linearly related to the total body iron stores and, for this reason, quantification of liver iron concentration (LIC) is widely regarded as the best surrogate to assess total body iron. Historically assessed using biopsy, there is a clear need for noninvasive quantitative imaging biomarkers of LIC. MRI is highly sensitive to the presence of tissue iron and has been increasingly adopted as a noninvasive alternative to biopsy for detection, severity grading, and treatment monitoring in patients with known or suspected iron overload. Multiple MRI strategies have been developed in the past 2 decades, based on both gradient-echo and spin-echo imaging, including signal intensity ratio and relaxometry strategies. However, there is a general lack of consensus regarding the appropriate use of these methods. The overall goal of this article is to summarize the current state of the art in the clinical use of MRI to quantify liver iron content and to assess the overall level of evidence of these various methods. Based on this summary, expert consensus panel recommendations on best practices for MRI-based quantification of liver iron are provided., (© RSNA, 2023.)

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.

Myotonic dystrophy type 1 (DM1) is a severe autosomal dominant neuromuscular disease in which the musculoskeletal system contributes substantially to overall mortality and morbidity. DM1 stems from a noncoding CTG trinucleotide repeat expansion in the DMPK gene. The human skeletal actin long repeat (HSA LR ) mouse model reproduces several aspects of the disease, but the muscle-wasting phenotype of this model has never been characterized in vivo. Herein, we used quantitative MRI to measure the fat and muscle volumes in the leg compartment (LC) of mice. These acquired data were processed to extract relevant parameters such as fat fraction and fat infiltration (fat LC/LC) in HSA LR and control (FBV) muscles. These results showed increased fat volume (fat LC) and fat infiltration within the muscle tissue of the leg compartment (muscle LC), in agreement with necropsies, in which fatty clumps were observed, and consistent with previous findings in DM1 patients. Model mice did not reproduce the characteristic impaired fat fraction, widespread fat replacement through the muscles, or reduced muscle volume reported in patients. Taken together, the observed abnormal replacement of skeletal muscle by fat in the HSA LR mice indicates that these mice partially reproduced the muscle phenotype observed in humans., (© 2023. The Author(s).)

Background: We explored perceptions and preferences regarding the conversion of in-person to virtual conferences as necessitated by travel and in-person meeting restrictions., Methods: A 16-question online survey to assess preferences regarding virtual conferences during the COVID-19 pandemic and future perspectives on this subject was disseminated internationally online between June and August 2020., Findings: A total of 508 responses were received from 73 countries. The largest number of responses came from Italy and the USA. The majority of respondents had already attended a virtual conference (80%) and would like to attend future virtual meetings (97%). The ideal duration of such an event was 2-3 days (42%). The preferred time format was a 2-4-h session (43%). Most respondents also noted that they would like a significant fee reduction and the possibility to attend a conference partly in-person and partly online. Respondents indicated educational sessions as the most valuable sections of virtual meetings. The reported positive factor of the virtual meeting format is the ability to re-watch lectures on demand. On the other hand, the absence of networking and human contact was recognized as a significant loss. In the future, people expressed a preference to attend conferences in person for networking purposes, but only in safer conditions., Conclusions: Respondents appreciated the opportunity to attend the main radiological congresses online and found it a good opportunity to stay updated without having to travel. However, in general, they would prefer these conferences to be structured differently. The lack of networking opportunities was the main reason for preferring an in-person meeting., Key Points: • Respondents appreciated the opportunity to attend the main radiological meetings online, considering it a good opportunity to stay updated without having to travel. • In the future, it is likely for congresses to offer attendance options both in person and online, making them more accessible to a larger audience. • Respondents indicated that networking represents the most valuable advantage of in-person conferences compared to online ones., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Patient's breathing affects the quality of chest images acquired with positron emission tomography/computed tomography (PET/CT) studies. Movement correction is required to optimize PET quantification in clinical settings. We present a reproducible methodology to compare the impact of different movement compensation protocols on PET image quality. Static phantom images were set as reference values, and recovery coefficients (RCs) were calculated from motion compensated images for the phantoms in respiratory movement. Image quality was evaluated in terms of: (1) volume accuracy (VA) with the NEMA phantom; (2) concentration accuracy (CA) by six refillable inserts within the electron density CIRS phantom; and (3) spatial resolution (R) with the Jaszczak phantom. Three different respiratory patterns were applied to the phantoms. We developed an open-source package to automatically analyze VA, CA and R. We compared 10 different movement compensation protocols available in the Philips Gemini TF-64 PET/CT (4-, 6-, 8- and 10-time bins, 20%-, 30%-, 40%-window width in Inhale and Exhale)., Results: The homemade package provided RC values for VA, CA and R of 102 PET images in less than 5 min. Results of the comparison of the 10 different protocols demonstrated the feasibility of the proposed method for quantifying the variations observed qualitatively. Overall, prospective protocols showed better motion compensation than retrospective. The best performance was obtained for the protocol Exhale 30% (0.3 s after maximum Exhale position and window width of 30%) with RC[Formula: see text], RC[Formula: see text] and RC[Formula: see text]. Among retrospective protocols, 8 Phase protocol showed the best performance., Conclusion: We provided an open-source package able to automatically evaluate the impact of motion compensation methods on PET image quality. A setup based on commonly available experimental phantoms is recommended. Its application for the comparison of 10 time-based approaches showed that Exhale 30% protocol had the best performance. The proposed framework is not specific to the phantoms and protocols presented on this study., (© 2022. The Author(s).)

An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging., Competing Interests: Competing interestsU.B. has received patent royalties from Hologic, Inc, which has arisen from one or more of the following: US Patent 5 452 3671 [Automated method and system for the segmentation of medical images (1995)], US Patent 5 984 870 [Method and system for the automated analysis of lesions in ultrasound images (1999)]; US Patent 6 112 112 [Method and system for the assessment of tumour extent in magnetic resonance images (2000)]; US Patent 6 185 320 [Method and system for the detection of lesions in medical images 2001]]; US Patent 6 317 617 [Method, computer program product, and system for the automated analysis of lesions in magnetic resonance, mammogram and ultrasound images (2001)]. The remaining authors declare no competing interests., (© The Author(s) 2022.)

Mobile medical imaging devices are invaluable for clinical diagnostic purposes both in and outside healthcare institutions. Among the various imaging modalities, only a few are readily portable. Magnetic resonance imaging (MRI), the gold standard for numerous healthcare conditions, does not traditionally belong to this group. Recently, low-field MRI technology companies have demonstrated the first decisive steps towards portability within medical facilities and vehicles. However, these scanners' weight and dimensions are incompatible with more demanding use cases such as in remote and developing regions, sports facilities and events, medical and military camps, or home healthcare. Here we present in vivo images taken with a light, small footprint, low-field extremity MRI scanner outside the controlled environment provided by medical facilities. To demonstrate the true portability of the system and benchmark its performance in various relevant scenarios, we have acquired images of a volunteer's knee in: (i) an MRI physics laboratory; (ii) an office room; (iii) outside a campus building, connected to a nearby power outlet; (iv) in open air, powered from a small fuel-based generator; and (v) at the volunteer's home. All images have been acquired within clinically viable times, and signal-to-noise ratios and tissue contrast suffice for 2D and 3D reconstructions with diagnostic value. Furthermore, the volunteer carries a fixation metallic implant screwed to the femur, which leads to strong artifacts in standard clinical systems but appears sharp in our low-field acquisitions. Altogether, this work opens a path towards highly accessible MRI under circumstances previously unrealistic., (© 2022. The Author(s).)

Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.

Objective: To evaluate pathological changes in cartilage and subchondral bone MRI biomarkers in a rabbit model of osteoarthritis (OA) and correlate these with histological variations., Design: Transection of the anterior cruciate ligament was performed on the right knee of eighteen 12-week-old New Zealand white rabbits to induce OA. 3-Tesla MR images were obtained from 18 healthy control knees (left) and 18 knees with OA (right). Imaging biomarkers included volume, thickness, T1 and T2* cartilage parametric maps, and several subchondral bone features: bone volume to total volume ratio, trabecular thickness, trabecular spacing, trabecular number (TbN), 2D and 3D fractal dimensions, and quality of trabecular score (QTS). Microscopic analysis of the lateral femoral condyles was set as the ground truth., Results: When healthy and osteoarthritic knees were compared, significant differences were seen in the T1 and T2* values of the femur and tibia cartilage and in the subchondral bone volume to total volume, TbN, and QTS of both the lateral and medial aspects of the femur and tibia. Histological findings revealed significant osteoarthritic changes between healthy and osteoarthritic knees in stain, structure, chondrocyte density, total score, and subchondral bone biomarker levels. A positive correlation was found between histological staining, structure, chondrocyte density, and total score variables in T1 and T2* cartilage biomarkers. A negative correlation was observed between histological subchondral bone variables and magnetic resonance D2D and QTS biomarkers., Conclusion: Quantification of several cartilage and subchondral bone imaging biomarkers in a rabbit model of OA allows the detection of significant changes, which are correlated with histological findings.

The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Estimating the required sample size is crucial when developing and validating clinical prediction models. However, there is no consensus about how to determine the sample size in such a setting. Here, the goal was to compare available methods to define a practical solution to sample size estimation for clinical predictive models, as applied to Horizon 2020 PRIMAGE as a case study., Methods: Three different methods (Riley's; "rule of thumb" with 10 and 5 events per predictor) were employed to calculate the sample size required to develop predictive models to analyse the variation in sample size as a function of different parameters. Subsequently, the sample size for model validation was also estimated., Results: To develop reliable predictive models, 1397 neuroblastoma patients are required, 1060 high-risk neuroblastoma patients and 1345 diffuse intrinsic pontine glioma (DIPG) patients. This sample size can be lowered by reducing the number of variables included in the model, by including direct measures of the outcome to be predicted and/or by increasing the follow-up period. For model validation, the estimated sample size resulted to be 326 patients for neuroblastoma, 246 for high-risk neuroblastoma, and 592 for DIPG., Conclusions: Given the variability of the different sample sizes obtained, we recommend using methods based on epidemiological data and the nature of the results, as the results are tailored to the specific clinical problem. In addition, sample size can be reduced by lowering the number of parameter predictors, by including direct measures of the outcome of interest., (© 2022. The Author(s) under exclusive licence to European Society of Radiology.)

Background: Rapid screening and accurate diagnosis of acute myocardial infarction are critical to reduce the progression of myocardial necrosis, in which proteolytic degradation of myocardial extracellular matrix plays a major role. In previous studies, we found that targeting the extracellular matrix metalloprotease inducer (EMMPRIN) by injecting nanoparticles conjugated with the specific EMMPRIN-binding peptide AP9 significantly improved cardiac function in mice subjected to ischemia/reperfusion., Methods: In a porcine model of coronary ischemia/reperfusion, we tested the theragnostic effects of administering 0.1 mg/kg gadolinium-containing nanoparticles conjugated with AP9 (NAP9), a synthetic peptide that targets EMMPRIN or a control nanoparticle (NAPSC). Cardiac magnetic resonance assessment of the infarct progression, ventricular function, and nanoparticle distribution was performed the next 7 days. We also measured the infarcted area of the heart and cardiac remodeling at 7 or 21 days after ischemia/reperfusion., Results: After 21 days of ischemia/reperfusion, NAP9 reduced the extension of cardiac necrosis (14.1±9.7 versus 35.5±1.8) and the levels of collagenolytic activity of MMPs (matrix metalloproteases), along with a significant reduction in collagen deposition (7.5±4.5 versus 41.3±20); including the ratio of type I versus III collagen fibers in the necrotic myocardium. In terms of cardiac function, the response to NAP9 administration resulted in a significant improvement of cardiac performance overtime, as evidenced by the left ventricle ejection fraction (64.0±7.8), when compared with those present in the NAPSC group (47.3±4.7). As shown by magnetic resonance imaging, noninvasive molecular imaging of NAP9 enabled us to find a significant reduction in cardiac necrosis, myocardial edema, hemorrhage, and microvascular obstruction, suggesting that NAP9 may reduce myocardial injury and preserve left ventricular function, at least, by preventing the effect of EMMPRIN on extracellular matrix degradation., Conclusions: Our data point towards NAP9 as a promising theragnostic tool in managing acute myocardial infarction, by inhibiting EMMPRIN-induced extracellular matrix degradation and allowing noninvasive visualization of cardiac necrosis progression over time.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research., Competing Interests: 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., (© 2022 The Author(s).)

Background and Objective: The systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks., Methods: Pubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: "imaging" AND "biobanks" and "imaging" AND "repository". Moreover, the Community Research and Development Information Service (CORDIS) database ( https://cordis.europa.eu/projects ) was searched using the terms: "imaging" AND "biobanks", also including collections, projects, project deliverables, project publications and programmes., Results: Of 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request., Conclusions: Imaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks., Key Points: • Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data. • Most imaging biobanks retrieved were European, disease-oriented and accessible under user request. • While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks., (© 2021. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters., Methods: All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model., Results: A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics., Conclusion: The developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour., (Copyright © 2021 SERAM. Published by Elsevier España, S.L.U. All rights reserved.)

Background: The coronavirus 2019 (COVID-19) epidemic spread throughout the world from the beginning of 2020, increasing healthcare professionals' workloads and levels of physical and emotional stress., Aims: To determine the prevalence of burnout syndrome in Spanish radiologists during the COVID-19 pandemic and the factors associated with the development of this syndrome, and to compare these findings with those obtained before the pandemic., Methods: This observation study took place between April 2020 and August 2020 (during the pandemic) through an online survey. A total of 150 responses were obtained. Demographic and work-related information was compiled. Burnout syndrome was measured with the Maslach Burnout Inventory Human Services Survey (MBI-HSS). The prevalence and characteristics of burnout syndrome obtained in this survey were compared with those of the same survey done in 2019. We performed a statistical analysis to identify possible risk factors and protective factors associated with this syndrome and to determine the homogeneity of the two samples., Results: The prevalence of burnout syndrome increased during the COVID-19 pandemic (49.3% vs. 33.6%, p = 0.002). No risk factors or protective factors that were valid both before and after the pandemic were identified. No correlations were identified between sociodemographic or work-related characteristics and burnout syndrome., Conclusion: This study demonstrates that burnout syndrome increased significantly in radiologists during the COVID-19 pandemic, affecting nearly half of all those who responded to the survey. These results underline the need to assess support for professional wellbeing of radiologists in Spain. No correlations were identified between burnout and gender, age, number of calls, years in the job, annual income, teaching, marital status, number of children, or type of contract., (Copyright © 2021 SERAM. Published by Elsevier España, S.L.U. All rights reserved.)