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Abstract 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.

Abstract 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.

Abstract 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 (

Abstract 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.

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.

Various in vivo experimental works carried out on different animals and organs have shown that it is possible to reduce the damage caused to healthy tissue still preserving the therapeutic efficacy on the tumor tissue, by drastically reducing the total time of dose delivery (

Since Favaudon's paper of 2014, there has been an increasing interest in FLASH radiotherapy. The FLASH modality could represent a breakthrough in radiation oncology; nevertheless, it brings new scientific and technological challenges. Currently, one of the main limits the scientific community has to cope with is the lack of a common technological platform to experiment with. Considering this framework, the possibility of readapting existing linac platforms to produce a FLASH beam is particularly attractive and different attempts have been already made. The purpose of this article is to illustrate how it is possible to transform a dedicated Intra Operative Radio Therapy (IORT) mobile linac into a FLASH research machine. Compared to the modification required by a standard medical linac, such transformation is easier, does not affect the machine settings and can be rapidly performed by the final user. NOVAC 7 is an IORT linac which can reach a maximum dose-per-pulse up to 13 cGy/pulse (average dose rate 39 Gy/min); such dose rate can be significantly increased by modifying the collimation system.Four different Source Surface Distance (SSD) can be obtained: - Clinical reference configuration;- Upper applicator only (SSD 50 cm);- Monitor chambers housing only (SSD 7 cm);- Dismounted monitor chambers (SSD 1.6 cm).The fourth configuration allows reaching values of dose-per-pulse up to around 18 Gy/pulse and dose rates up to around 500 Gy/s, at a Pulse Repetition Frequency (PRF) of 30 Hz. The other three configurations can be obtained without using any tool and without changing NOVAC settings, until reaching a FLASH dose rate in the third configuration. For FLASH configurations, relative and absolute dosimetric characterization of the beam were performed using radiochromic films EBT3. NOVAC7 transformed in FLASH mode can be used both for dosimetric testing and characterization of detectors and for radiobiological studies on cells and organoids, offering a wide range of dose-per-pulse, from 3 cGy/pulse up to 18 Gy/pulse; dose rates correspondingly change from 3 cGy/s up to 540 Gy/s.

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 ELI-NP (Extreme Light Infrastructure-Nuclear Physics) facility, currently under construction near Bucharest (Romania), is the pillar of the project ELI dedicated to the generation of high-brilliance gamma beams and high-power laser pulses that will be used for frontier research in nuclear physics. To develop an experimental program at the frontiers of the present-day knowledge, two pieces of equipment will be deployed at ELI-NP: a high power laser system consisting of two 10 PW lasers and a high brilliance gamma beam system. The ELI-NP Gamma beam system will deliver an intense gamma beam with unprecedented specifications in terms of photon flux, brilliance and energy bandwidth in an energy range from 0.2 to 20 MeV. Such a gamma beam requires special devices and techniques to measure and monitor the beam parameters during the commissioning and the operational phase. To accomplish this task, the Gamma Beam Characterization System, equipped with four elements, was developed: a Compton spectrometer (CSPEC), to measure and monitor the photon energy spectrum; a nuclear resonant scattering system (NRSS), for absolute beam energy calibration and inter-calibration of the other detectors; a beam profile imager (GPI) to be used for alignment and diagnostics purposes; and finally a sampling calorimeter (GCAL), for a fast combined measurement of the beam average energy and intensity. The combination of the measurements performed by GCAL and CSPEC allows fully characterizing the gamma beam energy distribution and intensity with a precision at the level of few per mill, enough to demonstrate the fulfillment of the required parameters. This article presents an overview of the gamma beam characterization system with focus on these two detectors, which were designed, assembled and are currently under test at INFN-Firenze. The layout and the working principle of the four devices is described, as well as some of the main results of detector tests.