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The ELI-NP facility will provide a monochromatic, high brilliance γ beam with tunable energy up to 19.5 MeV. The time structure of the beam consists of 32 pulses of 1 0 5 photons separated by 16 ns and delivered at repetition rate of 100 Hz. In order to match such unprecedented beam specifications and to measure its energy spectrum, intensity and space profile, a characterization system has been developed. This paper will focus on the working principle, the expected performances and the results of tests carried out on a low-Z sampling calorimeter, made of silicon detectors and polyethylene absorbers, which will measure the average beam energy and its intensity. The results of tests performed with an infrared pulsed laser have shown the capability of the detector to cope with the time structure of ELI-NP beam. Further tests carried out at the LABEC facility in Firenze have shown the excellent linearity of the silicon detectors in the energy range relevant to ELI-NP beam. [ABSTRACT FROM AUTHOR]

The ELI-NP facility, currently being built in Bucharest, Romania, will deliver an intense and almost monochromatic γ beam with tunable energy between 0.2 MeV and 19.5 MeV in two different beamlines. An articulated beam characterization system will be installed downstream of the collimator of each line. The system will use, as calibration candles, a few selected nuclear levels whose fluorescence condition will be monitored by a Nuclear Resonance Scattering System (NRSS). The NRSS will use a peculiar double-readout approach in order to detect resonant events overwhelming background: both scintillation and Cherenkov photons produced inside the same crystals will be separately read. • The NRS system will play a crucial role in the characterization of the Eli-NP beam. • It will be able to give a precise absolute energy calibration of the gamma beam. • The determination of the resonance will be achieved using a matrix of BaF/LYSO crystals. • A novel double readout technique shows a very good background rejection power. [ABSTRACT FROM AUTHOR]

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine., (© 2024. The Author(s).)

NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e., standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence., (© 2022. The Author(s) under exclusive licence to European Society of Radiology.)

Purpose: Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting., Methods: We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org)., Results: The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA., Conclusion: Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors.)

Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases., (© 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.)

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification., Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them., Results: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57-0.60; sensitivity = 0.56, 95% CI 0.54-0.58; specificity = 0.61, 95% CI 0.59-0.63; accuracy = 0.58, 95% CI 0.57-0.59)., Conclusions: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications.

The purpose of this study was to investigate the effect of image preprocessing on radiomic features estimation from computed tomography (CT) imaging of locally advanced rectal cancer (LARC). CT images of 20 patients with LARC were used to estimate 105 radiomic features of 7 classes (shape, first-order, GLCM, GLDM, GLRLM, GLSZM, and NGTDM). Radiomic features were estimated for 6 different isotropic resampling voxel sizes, using 10 interpolation algorithms (at fixed bin width) and 6 different bin widths (at fixed interpolation algorithm). The intraclass correlation coefficient (ICC) and the coefficient of variation (CV) were calculated to assess the variability in radiomic features estimation due to preprocessing. A repeated measures correlation analysis was performed to assess any linear correlation between radiomic feature estimate and resampling voxel size or bin width. Reproducibility of radiomic feature estimate, when assessed through ICC analysis, was nominally excellent (ICC > 0.9) for shape features, good (0.75 < ICC ≤ 0.9) or moderate (0.5 < ICC ≤ 0.75) for first-order features, and moderate or poor (0 ≤ ICC ≤ 0.5) for textural features. A number of radiomic features characterized by good or excellent reproducibility in terms of ICC showed however median CV values greater than 15%. For most textural features, a significant ( p < 0.05) correlation between their estimate and resampling voxel size or bin width was found. In CT imaging of patients with LARC, the estimate of textural features, as well as of first-order features to a lesser extent, is appreciably biased by preprocessing. Accordingly, this should be taken into account when planning clinical or research studies, as well as when comparing results from different studies and performing multicenter studies., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this article., (Copyright © 2022 Stefania Linsalata et al.)

PRIMAGE is a European Commission-financed project dealing with medical imaging and artificial intelligence aiming to create an imaging biobank in oncology. The project includes a task dedicated to the interoperability between imaging and standard biobanks. We aim at linking Digital imaging and Communications in Medicine (DICOM) metadata to the Minimum Information About BIobank data Sharing (MIABIS) standard of biobanking. A very first integration model based on the fusion of the two existing standards, MIABIS and DICOM, has been developed. The fundamental method was that of expanding the MIABIS core to the imaging field, adding DICOM metadata derived from CT scans of 18 paediatric patients with neuroblastoma. The model was developed with the relational database management system Structured Query Language. The integration data model has been built as an Entity Relationship Diagram, commonly used to organise data within databases. Five additional entities have been linked to the "Image Collection" subcategory in order to include the imaging metadata more specific to the particular type of data: Body Part Examined, Modality Information, Dataset Type, Image Analysis, and Registration Parameters. The model is a starting point for the expansion of MIABIS with further DICOM metadata, enabling the inclusion of imaging data in biorepositories.

Sarafotoxins are peptides isolated from the Atractaspis snake venom, with strong constrictor effect on cardiac and smooth muscle. They are structurally and functionally related to endothelins. The sarafotoxins precursor cDNA predicts an unusual structure 'rosary-type', with 12 successive similar stretches of sarafotoxin (SRTX) and spacer. In the present work, the recombinant precursor of SRTXs was sub-cloned and expressed in the yeast Pichia pastoris, and secreted to the culture medium. Characterization by SDS-PAGE, immunoblot, mass spectrometry and biological activity, suggests that intact precursor was expressed but processing into mature toxins also occurred. Furthermore, our results indicate that the correct proportion of sarafotoxin types as contained in the precursor, is obtained in the yeast culture medium. Contractile effects of the expressed toxins, on rat and Bothrops jararaca isolated aorta, were equivalent to 5x10(-10)M and 5x10(-11)M of sarafotoxin b, respectively. The enzymes responsible for the complete maturation of sarafotoxins precursor are still unknown. Our results strongly suggest that the yeast Pichia pastoris is able to perform such a maturation process. Thus, the yeast Pichia pastoris may offer an alternative to snake venom gland to tentatively identify the molecular process responsible for SRTXs release.

Two distinct hypertensive peptides were purified and characterized from Bothrops jararaca (Bj) plasma incubated at pH 4, 37 degrees C, 24 hr. These peptides were active on rat and Bj blood pressure, on rat isolated uterus, on guinea pig isolated ileum and on Bj isolated duodenum. At the releasing conditions no further activities were found for kininases, angiotensinases or angiotensin converting enzymes. The peptides were purified by ethanol/ether extraction, Sephadex G-25 gel filtration, semipreparative reverse-phase (C-18) HPLC and analytical (C-18) HPLC. The amino-acid sequences of the purified peptides corresponded to (Ile5)AII and (Val5-Tyr9)AI and their molecular masses were confirmed by mass spectrometry as 1046.6 and 1348.0 respectively. The presence of those two angiotensins on Bj plasma may have some evolutionary significance since (Ile5)AII is known as a mammalian angiotensin and (Val5)AII as a non-mammalian one.