FLASH radiotherapy with electrons: issues related to the production, monitoring, and dosimetric characterization of the beam

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 (<200 ms). This effect, called the FLASH effect, immediately attracted considerable attention within the radiotherapy community, due to the possibility of widening the therapeutic window and treating effectively tumors which appear radioresistant to conventional techniques. Despite the experimental evidence, the radiobiological mechanisms underlying the FLASH effect and the beam parameters contributing to its optimization are not yet known in details. In order to fully understand the FLASH effect, it might be worthy to investigate some alternatives which can further improve the tools adopted so far, in terms of both linac technology and dosimetric systems. This work investigates the problems and solutions concerning the realization of an electron accelerator dedicated to FLASH therapy and optimized for in vivo experiments. Moreover, the work discusses the saturation problems of the most common radiotherapy dosimeters when used in the very high dose-per-pulse FLASH conditions and provides some preliminary experimental data on their behavior

A Characterization System for the Monitoring of ELI-NP Gamma Beam

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

Commissioning and experimental validation beyond highly heterogeneous media of Eclipse dose-calculation algorithms

During the last decade, the capabilities of image-based commercial TPS have been enhanced due to developments in treatment imaging, planning, and delivery. The accurate delivery of IMRT and VMAT treatments strongly depends on the proper commissioning and testing of TPSs. The aim of this thesis work is to provide the fundamental set of tests performed for the commissioning of a Varian Eclipse planning system on a Varian Clinac DHX, focusing on tests performed to assess the accuracy of photon dose calculation algorithms (i.e AXB and AAA) in presence of highly heterogeneous media. The importance of this study was related to the increasing use of metal medical devices, such as prostheses or dental implants. Currently, commercially available photon dose calculation algorithms are a well-established tool for the prediction of adsorbed dose in inhomogeneous media, for the density range of the human body; nevertheless, their accuracy needs to be validated also in presence of high-density materials. Absolute dose longitudinal profiles were measured using GafChromic EBT3 films and a PTW-LA48 linear ionization chamber array downward a heterogeneity insert. The insert materials range from low-Z material like lung to the higher ones, like titanium. The dose algorithms accuracy was evaluated comparing measured and calculated dose profiles. The absolute dose profiles in water were calculated replicating the real setup in Eclipse TPS using two different procedures i.e., performing a TC scan of the phantom or manually drawing a virtual phantom. In the second case, the absorbed dose was calculated performing the density override of the materials. Investigations were performed for 6 MV and 15 MV photons

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

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

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

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

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

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

A Characterization System for the Monitoring of ELI-NP Gamma Beam

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 test

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients.

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