Construction of Recombinant Baculoviruses.

The success of using the insect cell-baculovirus expression technology (BEST) relies on the efficient construction of recombinant baculovirus with genetic stability and high productivity, ideally within a short time period. Generation of recombinant baculoviruses requires the transfection of insect cells, harvesting of recombinant baculovirus pools, isolation of plaques, and the expansion of baculovirus stocks for their use for recombinant protein production. Moreover, many options exist for selecting the genetic elements to be present in the recombinant baculovirus. This chapter describes the most commonly used homologous recombination systems for the production of recombinant baculoviruses, as well as strategies to maximize generation efficiency and recombinant protein or baculovirus production. The key steps for generating baculovirus stocks and troubleshooting strategies are described.

One-Step Purification Strategy for Cowpea Chlorotic Mottle Virus-Like Particles Produced by the IC-BEVS.

Virus-like particles (VLP) of the cowpea chlorotic mottle virus (CCMV), a plant virus, have been shown to be safe and noncytotoxic vehicles for delivering various cargos, including nucleic acids and peptides, and as scaffolds for presenting epitopes. Thus, CCMV-VLP have acquired increasing attention to be used in fields such as gene therapy, drug delivery, and vaccine development. Regardless of their production method, most reports purify CCMV-VLP through a series of ultracentrifugation steps using sucrose density gradient ultracentrifugation, which is a complex and time-consuming process. Here, the use of anion exchange chromatography is described as a one-step protocol for purification of CCMV-VLP produced by the insect cell-baculovirus expression vector system (IC-BEVS).

Clinical and serological characteristics of patients allergic to LTP.

BACKGROUND: Allergy to lipid transfer proteins (LPT) is common in Mediterranean Europe, and it causes severe reactions in patients and affects multiple foods, impairing the quality of life. OBJECTIVE: This study aimed to describe the clinical and sensitization profile of patients with LTP syndrome and to determine a clinical pattern of severity. Molecular diagnosis is shown in a broad population through microarrays. MATERIAL AND METHODS: This study was performed at the LTP Allergy Consultation of the Reina Sofia Hospital in Murcia, Spain. We analyzed the patients' characteristics, reactions, cofactors, food implicated, quality of life, skin prick test to food and aeroallergens, and serologic parameters, such as total immunoglobulin E, peach LTP (Pru p 3 IgE) and immunoglobulin G4, and microarray Immuno Solid-phase Allergen Chip (ISAC). We related the severity of the reactions with other variables. RESULTS: We presented a series of 236 patients diagnosed with LTP allergy, 54.66% suffering from anaphylaxis, 36.02% from urticaria angioedema, and 9.32% from oral allergy syndrome. The most frequently implicated food was peach, producing symptoms in 70% of patients, followed by walnut in 55%, peanut in 45%, hazelnut in 44%, and apple in 38% patients. Regarding the food that provoked anaphylaxis, walnut was the most frequent instigator, along with peach, peanut, hazelnut, almond, sunflower seed, and apple. According to the severity of LPT reaction, we did not discover significant differences in gender, age, food group involved, and serologic parameters. We found differences in the presence of cofactors, with 48.84% of cofactors in patients with anaphylaxis, compared to 27.1% in patients without anaphylaxis and in family allergy background (P < 0.0001). CONCLUSION: In our series of patients, 54% presented anaphylaxis, and the foods that most frequently produced symptoms were peaches, apples, and nuts. Cofactors and family allergy backgrounds were associated with the severity of LPT reaction.

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

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

Evaluation of the first 5 years of a glutaric aciduria type I neonatal screening programme in Asturias.

INTRODUCTION: . Neonatal screening of glutaric aciduria type 1 (GA-1) has brought radical changes in the course and outcomes of this disease. This study analyses the outcomes of the first 5 years (2015-2019) of the AGA1 neonatal screening programme in our autonomous community. MATERIAL: . We conducted an observational, descriptive and retrospective study. All neonates born between January 1, 2015 and December 31, 2019 that participated in the neonatal screening programme were included in the study. The glutarylcarnitine (C5DC) concentration in dry blood spot samples was measured by means of tandem mass spectrometry applying a cut-off point of 0.25 µmol/L. RESULTS: . A total of 30 120 newborns underwent screening. We found differences in the C5DC concentration based on gestational age, type of feeding and hours of life at sample collection. These differences were not relevant for screening purposes. There were no differences between neonates with weights smaller and greater than 1500 g. Screening identified 2 affected patients and there were 3 false positives. There were no false negatives. The diagnosis was confirmed by genetic testing. Patients have been in treatment since diagnosis and have not developed encephalopathic crises in the first 4 years of life. CONCLUSIONS: . Screening allowed early diagnosis of two cases of GA-1 in the first 5 years since its introduction in our autonomous community. Although there were differences in C5DC levels based on gestational age, type of feeding and hours of life at blood extraction, they were not relevant for screening.

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

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.

Serum GFAP levels correlate with astrocyte reactivity, post-mortem brain atrophy and neurofibrillary tangles.

Glial fibrillary acidic protein (GFAP), a proxy of astrocyte reactivity, has been proposed as biomarker of Alzheimer's disease. However, there is limited information about the correlation between blood biomarkers and post-mortem neuropathology. In a single-centre prospective clinicopathological cohort of 139 dementia patients, for which the time-frame between GFAP level determination and neuropathological assessment was exceptionally short (on average 139 days), we analysed this biomarker, measured at three time points, in relation to proxies of disease progression such as cognitive decline and brain weight. Most importantly, we investigated the use of blood GFAP to detect the neuropathological hallmarks of Alzheimer's disease, while accounting for potential influences of the most frequent brain co-pathologies. The main findings demonstrated an association between serum GFAP level and post-mortem tau pathology (ß = 12.85; P < 0.001) that was independent of amyloid deposits (ß = 13.23; P = 0.02). A mediation analysis provided additional support for the role of astrocytic activation as a link between amyloid and tau pathology in Alzheimer's disease. Furthermore, a negative correlation was observed between pre-mortem serum GFAP and brain weight at post-mortem (r = -0.35; P < 0.001). This finding, together with evidence of a negative correlation with cognitive assessments (r = -0.27; P = 0.005), supports the role of GFAP as a biomarker for disease monitoring, even in the late phases of Alzheimer's disease. Moreover, the diagnostic performance of GFAP in advanced dementia patients was explored, and its discriminative power (area under the receiver operator characteristic curve at baseline = 0.91) in differentiating neuropathologically-confirmed Alzheimer's disease dementias from non-Alzheimer's disease dementias was determined, despite the challenging scenario of advanced age and frequent co-pathologies in these patients. Independently of Alzheimer's disease, serum GFAP levels were shown to be associated with two other pathologies targeting the temporal lobes-hippocampal sclerosis (ß = 3.64; P = 0.03) and argyrophilic grain disease (ß = -6.11; P = 0.02). Finally, serum GFAP levels were revealed to be correlated with astrocyte reactivity, using the brain GFAP-immunostained area as a proxy (ρ = 0.21; P = 0.02). Our results contribute to increasing evidence suggesting a role for blood GFAP as an Alzheimer's disease biomarker, and the findings offer mechanistic insights into the relationship between blood GFAP and Alzheimer's disease neuropathology, highlighting its ties with tau burden. Moreover, the data highlighting an independent association between serum GFAP levels and other neuropathological lesions provide information for clinicians to consider when interpreting test results. The longitudinal design and correlation with post-mortem data reinforce the robustness of our findings. However, studies correlating blood biomarkers and neuropathological assessments are still scant, and further research is needed to replicate and validate these results in diverse populations.

Development and validation of an image biomarker to identify metabolic dysfunction associated steatohepatitis: MR-MASH score.

BACKGROUND AND AIMS: Diagnosis of metabolic dysfunction-associated steatohepatitis (MASH) requires histology. In this study, a magnetic resonance imaging (MRI) score was developed and validated to identify MASH in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Secondarily, a screening strategy for MASH diagnosis was investigated. METHODS: This prospective multicentre study included 317 patients with biopsy-proven MASLD and contemporaneous MRI. The discovery cohort (Spain, Portugal) included 194 patients. NAFLD activity score (NAS) and fibrosis were assessed with the NASH-CRN histologic system. MASH was defined by the presence of steatosis, lobular inflammation, and ballooning, with NAS ≥4 with or without fibrosis. An MRI-based composite biomarker of Proton Density Fat Fraction and waist circumference (MR-MASH score) was developed. Findings were afterwards validated in an independent cohort (United States, Spain) with different MRI protocols. RESULTS: In the derivation cohort, 51% (n = 99) had MASH. The MR-MASH score identified MASH with an AUC = .88 (95% CI .83-.93) and strongly correlated with NAS (r = .69). The MRI score lower cut-off corresponded to 88% sensitivity with 86% NPV, while the upper cut-off corresponded to 92% specificity with 87% PPV. MR-MASH was validated with an AUC = .86 (95% CI .77-.92), 91% sensitivity (lower cut-off) and 87% specificity (upper cut-off). A two-step screening strategy with sequential MR-MASH examination performed in patients with indeterminate-high FIB-4 or transient elastography showed an 83-84% PPV to identify MASH. The AUC of MR-MASH was significantly higher than that of the FAST score (p < .001). CONCLUSIONS: The MR-MASH score has clinical utility in the identification and management of patients with MASH at risk of progression.

Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project.

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.

A Novel Formulation of Asparaginase Encapsulated into Virus-like Particles of Brome Mosaic Virus: In Vitro and In Vivo Evidence.

The interest in plant-derived virus-like particles (pVLPs) for the design of a new generation of nanocarriers is based on their lack of infection for humans, their immunostimulatory properties to fight cancer cells, and their capability to contain and release cargo molecules. Asparaginase (ASNase) is an FDA-approved drug to treat acute lymphoblastic leukemia (LLA); however, it exhibits high immunogenicity which often leads to discontinuation of treatment. In previous work, we encapsulated ASNase into bacteriophage P22-based VLPs through genetic-directed design to form the ASNase-P22 nanobioreactors. In this work, a commercial ASNase was encapsulated into brome mosaic virus-like particles (BMV-VLPs) to form stable ASNase-BMV nanobioreactors. According to our results, we observed that ASNase-BMV nanobioreactors had similar cytotoxicity against MOLT-4 and Reh cells as the commercial drug. In vivo assays showed a higher specific anti-ASNase IgG response in BALB/c mice immunized with ASNase encapsulated into BMV-VLPs compared with those immunized with free ASNase. Nevertheless, we also detected a high and specific IgG response against BMV capsids on both ASNase-filled capsids (ASNase-BMV) and empty BMV capsids. Despite the fact that our in vivo studies showed that the BMV-VLPs stimulate the immune response either empty or with cargo proteins, the specific cytotoxicity against leukemic cells allows us to propose ASNase-BMV as a potential novel formulation for LLA treatment where in vitro and in vivo evidence of functionality is provided.

Non-invasive biomarkers for mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15 years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance (1H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n = 51), mild cognitive impairment (MCI, n = 27), and cognitively healthy controls (HC, n = 50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n = 9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n = 14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n = 4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of 1H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.

Pancreatic steatosis and iron overload increases cardiovascular risk in non-alcoholic fatty liver disease.

Objective: To assess the prevalence of pancreatic steatosis and iron overload in non-alcoholic fatty liver disease (NAFLD) and their correlation with liver histology severity and the risk of cardiometabolic diseases. Method: A prospective, multicenter study including NAFLD patients with biopsy and paired Magnetic Resonance Imaging (MRI) was performed. Liver biopsies were evaluated according to NASH Clinical Research Network, hepatic iron storages were scored, and digital pathology quantified the tissue proportionate areas of fat and iron. MRI-biomarkers of fat fraction (PDFF) and iron accumulation (R2*) were obtained from the liver and pancreas. Different metabolic traits were evaluated, cardiovascular disease (CVD) risk was estimated with the atherosclerotic CVD score, and the severity of iron metabolism alteration was determined by grading metabolic hiperferritinemia (MHF). Associations between CVD, histology and MRI were investigated. Results: In total, 324 patients were included. MRI-determined pancreatic iron overload and moderate-to severe steatosis were present in 45% and 25%, respectively. Liver and pancreatic MRI-biomarkers showed a weak correlation (r=0.32 for PDFF, r=0.17 for R2*). Pancreatic PDFF increased with hepatic histologic steatosis grades and NASH diagnosis (p<0.001). Prevalence of pancreatic steatosis and iron overload increased with the number of metabolic traits (p<0.001). Liver R2* significantly correlated with MHF (AUC=0.77 [0.72-0.82]). MRI-determined pancreatic steatosis (OR=3.15 [1.63-6.09]), and iron overload (OR=2.39 [1.32-4.37]) were independently associated with high-risk CVD. Histologic diagnosis of NASH and advanced fibrosis were also associated with high-risk CVD. Conclusion: Pancreatic steatosis and iron overload could be of utility in clinical decision-making and prognostication of NAFLD.

Hypopyon Uveitis as a Manifestation of Primary Choroidal Lymphoma.

A 78-year-old female was referred to our hospital due to a decrease of visual acuity in her left eye. On examination, presence of left choroidal folds and subretinal fluid was disclosed. After being misdiagnosed as neovascular age-related macular degeneration treatment with intravitreal injections of Aflibercept was started. Despite improvement of fluid, persistence of choroidal folds encouraged a magnetic resonance imaging revealing a left retrobulbar nodular lesion. Furthermore, development of hypopyon during follow-up allowed a flow cytometry analysis of an aqueous humour sample that confirmed infiltration by a non-Hodgkin mature B-cell lymphoproliferative process. Finally, treatment with Rituximab and intravenous corticosteroids achieved complete resolution. Primary choroidal lymphoma may occur with an atypical presentation, including hypopyon uveitis. Thus, familiarity with its clinical features is fundamental for an early recognition and correct management.

Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis.

PURPOSE: Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative. METHODS: Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020-January 2021. Considering PCR and/or serology as reference standard, DTT and radiograph diagnostic performance and interobserver agreement, and DTT contributions in unequivocal, equivocal, and absent radiograph opacities were analysed by the area under the curve (AUC), Cohen's Kappa, Mc-Nemar's and Wilcoxon tests. RESULTS: We recruited 480 patients (49 ± 15 years, 277 female). DTT increased ER1 (from 0.76, CI95% 0.7-0.8 to 0.79, CI95% 0.7-0.8; P=.04) and ER2 (from 0.77 CI95% 0.7-0.8 to 0.80 CI95% 0.8-0.8, P=.02) radiograph-AUCs, sensitivity, specificity, predictive values, and positive likelihood ratio. In false negative microbiological cases, DTT suggested COVID-19 pneumonia in 13% (4/30; P=.052, ER1) and 20% (6/30; P=.020, ER2) more than radiograph. DTT showed new or larger opacities in 33-47% of cases with unequivocal opacities in radiograph, new opacities in 2-6% of normal radiographs and reduced equivocal opacities by 13-16%. Kappa increased from 0.64 (CI95% 0.6-0.8) to 0.7 (CI95% 0.7-0.8) for COVID-19 pneumonia probability, and from 0.69 (CI95% 0.6-0.7) to 0.76 (CI95% 0.7-0.8) for pneumonic extension. CONCLUSION: DTT improves radiograph performance and agreement for COVID-19 pneumonia diagnosis and reduces PCR false negatives.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative.

BACKGROUND: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). OBJECTIVES: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. METHODS: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. RESULTS: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. CONCLUSION: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

Evolution of laboratory parameters in patients with lipid transfer protein syndrome after 3 years of immunotherapy.

Lipid transfer protein (LTP) syndrome is an increasingly prevailing disease, especially in the young population, with severely affected quality of life. Since 2013, a specific treatment, called sublingual immunotherapy (SLIT), with peach extract (SLIT-peach®) has been used, but with no long-term effectiveness studies. The main objective of the present study was to assess the long-term effectiveness of SLIT-peach® and to relate the clinical evolution of patients. This was an ambispective study conducted for 3 years. A total of 25 patients with LTP syndrome were selected and treated with SLIT-peach®. They underwent a provocation test in the first year with reintroduced foods that had produced symptoms in the past. Analytical determination of specific immunoglobulin E (IgE) and immunoglobulin G4 (IgG4) to peach (Pru p 3) was performed at the beginning of treatment, at the first year of initiation, and at the end of treatment. These data were compared with the control group comprising 14 patients with LTP syndrome without treatment. A statistically significant decrease in specific IgE to Pru p 3 at the end of the treatment and an increase in specific IgG4 to Pru p 3 1 year after treatment initiation were observed in the active group in relation to tolerance to foods with LTPs. These results indicate that food tolerance begins after the first year and is maintained after the end of 3 years of treatment. In conclusion, treatment with SLIT-peach® for 3 years is effective for patients with LTP syndrome, preventing the evolution of the disease, allowing patients to restart a diet with plant foods, and improving their quality of life.

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects.

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images.

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.

Association between Clinical Frailty Scale (CFS) and clinical presentation and outcomes in older inpatients with COVID-19.

BACKGROUND: Frailty is a physiological condition characterized by a decreased reserve to stressors. In patients with COVID-19, frailty is a risk factor for in-hospital mortality. The aim of this study was to assess the relationship between clinical presentation, analytical and radiological parameters at admission, and clinical outcomes according to frailty, as defined by the Clinical Frailty Scale (CFS), in old people hospitalized with COVID-19. MATERIALS AND METHODS: This retrospective cohort study included people aged 65 years and older and admitted with community-acquired COVID-19 from 3 March 2020 to 31 April 2021. Patients were categorized using the CFS. Primary outcomes were symptoms of COVID-19 prior to admission, mortality, readmission, admission in intensive care unit (ICU), and need for invasive mechanical ventilation. Analysis of clinical symptoms, clinical outcomes, and CFS was performed using multivariable logistic regression, and results were expressed as odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Of the 785 included patients, 326 (41.5%, 95% CI 38.1%-45.0%) were defined as frail (CFS ≥ 5 points): 208 (26.5%, 95% CI 23.5%-29.7%) presented mild-moderate frailty (CFS 5-6 points) and 118 (15.0%, 95% CI 12.7%-17.7%), severe frailty (7-9 points). After adjusting for epidemiological variables (age, gender, residence in a nursing home, and Charlson comorbidity index), frail patients were significantly less likely to present dry cough (OR 0.58, 95% CI 0.40-0.83), myalgia-arthralgia (OR 0.46, 95% CI 0.29-0.75), and anosmia-dysgeusia (OR 0.46, 95% CI 0.23-0.94). Confusion was more common in severely frail patients (OR 3.14; 95% CI 1.64-5.97). After adjusting for epidemiological variables, the risk of in-hospital mortality was higher in frail patients (OR 2.79, 95% CI 1.79-4.25), including both those with mild-moderate frailty (OR 1.98, 95% CI 1.23-3.19) and severe frailty (OR 5.44, 95% CI 3.14-9.42). Readmission was higher in frail patients (OR 2.11, 95% CI 1.07-4.16), but only in mild-moderate frailty (OR 2.35, 95% CI 1.17-4.75).. CONCLUSION: Frail patients presented atypical symptoms (less dry cough, myalgia-arthralgia, and anosmia-dysgeusia, and more confusion). Frailty was an independent predictor for death, regardless of severity, and mild-moderate frailty was associated with readmission.

Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.

OBJECTIVE: Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines. Therefore, the main objective of this work was to develop a robust and reproducible CNN-based automatic prostate multi-regional segmentation model using an intercontinental cohort of prostate MRI. METHODS: A heterogeneous database of 243 T2-weighted prostate studies from 7 countries and 10 machines of 3 different vendors, with the CZ-TZ, PZ, and SV regions manually delineated by two experienced radiologists (ground truth), was used to train (n = 123) and test (n = 120) a U-Net-based model with deep supervision using a cyclical learning rate. The performance of the model was evaluated by means of dice similarity coefficient (DSC), among others. Segmentation results with a DSC above 0.7 were considered accurate. RESULTS: The proposed method obtained a DSC of 0.88 ± 0.01, 0.85 ± 0.02, 0.72 ± 0.02, and 0.72 ± 0.02 for the prostate gland, CZ-TZ, PZ, and SV respectively in the 120 studies of the test set when comparing the predicted segmentations with the ground truth. No statistically significant differences were found in the results obtained between manufacturers or continents. CONCLUSION: Prostate multi-regional T2-weighted MR images automatic segmentation can be accurately achieved by U-Net like CNN, generalizable in a highly variable clinical environment with different equipment, acquisition configurations, and population. KEY POINTS: • Deep learning techniques allows the accurate segmentation of the prostate in three different regions on MR T2w images. • Multi-centric database proved the generalization of the CNN model on different institutions across different continents. • CNN models can be used to aid on the diagnosis and follow-up of patients with prostate cancer.