A library of proton lineal energy spectra spanning the full range of clinically relevant energies.

Purpose: In locations where the proton energy spectrum is broad, lineal energy spectrum-based proton biological effects models may be more accurate than dose-averaged linear energy transfer (LET) based models. However, the development of microdosimetric spectrum-based biological effects models is hampered by the extreme computational difficulty of calculating microdosimetric spectra. Given a precomputed library of lineal energy spectra for monoenergetic protons, a weighted summation can be performed which yields the lineal energy spectrum of an arbitrary polyenergetic beam.

Validation of the generalized stochastic microdosimetric model (GSM2) over a broad range of LET and particle beam type: a unique model for accurate description of (therapy relevant) radiation qualities.

. The present work shows the first extensive validation of the(GSM). This mechanistic and probabilistic model is trained and tested over cell survival experiments conducted with two cell lines (H460 and H1437), three different types of radiation (protons, helium, and carbon ions), spanning a very broad LET range from1 keVμm-1up to more than300 keVμm-1.

Test-retest repeatability of myocardial radiomic features from quantitative cardiac magnetic resonance T1 and T2 mapping.

Radiomics of cardiac magnetic resonance (MR) imaging has proved to be potentially useful in the study of various myocardial diseases. Therefore, assessing the repeatability degree in radiomic features measurement is of fundamental importance. The aim of this study was to assess test-retest repeatability of myocardial radiomic features extracted from quantitative T1 and T2 maps.

Theoretical screening of N-[5'-methyl-3'-isoxasolyl]-N-[(E)-1-(-2-thiophene)] methylidene]amine and its isoxazole based derivatives as donor materials for bulk heterojunction organic solar cells: DFT and TD-DFT investigation.

Context: In the present work, the influence of aromatic ring substitution on a series of small-donor organic molecules (A, B, C, D, and E) with isoxazole cores was investigated for photovoltaic applications in organic solar cells. Frontier molecular orbital analysis, chemical reactivity descriptors, dipole moment, and population analysis showed that all the organic materials have intramolecular charge transfer abilities capable of donating electrons to the acceptor material (PCBM). The required photovoltaic parameters such as V, FF, J, LHE, and other associated optoelectronic parameters are reported.

Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance.

Integrating microdosimetricRBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool.

In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE.MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales.

Cell Survival Computation via the Generalized Stochastic Microdosimetric Model (GSM2); Part II: Numerical Results.

In the present paper we numerically investigate, using Monte Carlo simulation, the theoretical results predicted by the Generalized Stochastic Microdosimetric Model (GSM2), as shown in the published companion paper. Taking advantage of the particle irradiation data ensemble (PIDE) dataset, we calculated GSM2 biological parameters of human salivary gland (HSG) and V79 cell lines. Further, exploiting the TOPAS-microdosimetric extension, we simulated the microdosimetric spectra of different radiation fields of therapeutic interest generated by four different ions (protons, helium-4, carbon-12 and oxygen-16) each at three different residual ranges.

Microdosimetric analysis of the radiation quality of two different proton beams in the distal edge of the depth-dose profile.

Proton-therapy exploits the advantageous depth-dose profile of protons to induce the highest damage to tumoral cells in the last millimetres of their range in sharp Bragg Peak. To cover the whole tumoral volume, beams of different energies are combined to create the Spread Out Bragg Peak (SOBP). In passive modulated beams, the energy spread is created with modulators in which the highest energy beam is degraded through different thicknesses of calibrated plastic materials.

Microdosimetry of a 62-MeV clinical proton beam with five detectors.

In proton therapy, most treatment planning systems (TPS) use a fixed relative biological effectiveness (RBE) of 1.1 all along the depth-dose profile. Innovative TPS are now investigated considering the variability of RBE with radiation quality.

An artificial intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model.

The present work develops ANAKIN: an. ANAKIN is trained and tested over 513 cell survival experiments with different types of radiation contained in the publicly available PIDE database. We show how ANAKIN accurately predicts several relevant biological endpoints over a wide broad range on ion beams and for a high number of cell-lines.

Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping.

Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing-in terms of voxel size resampling, discretization, and filtering-on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy.

Multiple levels of stochasticity accounted for in different radiation biophysical models: from physics to biology.

Purpose: In the present paper we investigate how some stochastic effects are included in a class of radiobiological models with particular emphasis on how such randomnesses reflect into the predicted cell survival curve.

Materials And Methods: We consider four different models, namely the GSM, in its original full form, the GSM the GSM and the (RMR). While GSM and the RMR models are known in literature, the Dirac and the Poisson GSM have been newly introduced in this work.

Investigation of In-Field and Out-of-Field Radiation Quality With Microdosimetry and Its Impact on Relative Biological Effectiveness in Proton Therapy.

Purpose: Using microdosimetry, this study investigated the relative biological effectiveness (RBE) and quality factor (Q¯) variations in field and out of field as a function of radiation quality for clinical protons.

Methods And Materials: A water phantom with a spread-out Bragg peak (SOBP) was irradiated to acquire microdosimetric spectra at several distal and lateral depths with a tissue equivalent proportional counter. The measurements were used as inputs to microdosimetric kinetic and Loncol models to determine the RBE spatial distribution and compare it with predictions from the dose-averaged linear energy transfer-based McNamara model.

An exploratory study of machine learning techniques applied to therapeutic energies particle tracking in microdosimetry using the novel hybrid detector for microdosimetry (HDM).

In this work we present an advanced random forest-based machine learning (ML) model, trained and tested on Geant4 simulations. The developed ML model is designed to improve the performance of the hybrid detector for microdosimetry (HDM), a novel hybrid detector recently introduced to augment the microdosimetric information with the track length of particles traversing the microdosimeter. The present work leads to the following improvements of HDM: (i) the detection efficiency is increased up to 100%, filling not detected particles due to scattering within the tracker or non-active regions, (ii) the track reconstruction algorithm precision.

Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy.

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).

Ventricular tachycardia ablation guided or aided by scar characterization with cardiac magnetic resonance: rationale and design of VOYAGE study.

Background: Radiofrequency ablation has been shown to be a safe and effective treatment for scar-related ventricular arrhythmias (VA). Recent preliminary studies have shown that real time integration of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) images with electroanatomical map (EAM) data may lead to increased procedure efficacy, efficiency, and safety.

Methods: VOYAGE is a prospective, randomized, multicenter controlled open label study designed to compare in terms of efficacy, efficiency, and safety a CMR aided/guided workflow to standard EAM-guided ventricular tachycardia (VT) ablation.

Internet Gaming Disorder in Children and Adolescents with Attention Deficit Hyperactivity Disorder.

Although Attention Deficit Hyperactivity Disorder (ADHD) has been related to an increased risk for behavioral addictions, the relationship between ADHD and Internet Gaming Disorder (IGD) is still debated. The aim of this study is to address this topic by exploring the prevalence of IGD in a consecutive sample of ADHD youth, compared to a normal control group, and by assessing selected psychopathological and cognitive features in ADHD patients with and without IGD. One hundred and eight patients with ADHD (mean age 11.

Low-dose CT for lung cancer screening: position paper from the Italian college of thoracic radiology.

Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program.

Case Series: Recovery of Chemotherapy-Related Acute Heart Failure by the Combined Use of Sacubitril Valsartan and Wearable Cardioverter Defibrillator: A Novel Winning Combination in Cardio-Oncology.

Effective anticancer treatments have dramatically improved the outcome of patients with cancer, but cardiac toxicity reduces their clinical efficacy in a non-negligible percentage of patients. Sacubitril/valsartan is a new paradigm in the treatment of chronic heart failure, with a reduced ejection fraction due to the enhancement of natriuretic peptides' properties when coupled with a blocking effect on the angiotensin II type 1 (AT1) receptors. As with other clinical conditions of heart failure with potentially reversible declines in cardiac function, a wearable cardioverter defibrillator (WCD) is a valid tool for protection against sudden death until recovery occurs.

Cell Survival Computation via the Generalized Stochastic Microdosimetric Model (GSM2); Part I: The Theoretical Framework.

The current article presents the first application of the Generalized Stochastic Microdosimetric Model (GSM2) for computing explicitly a cell survival curve. GSM2 is a general probabilistic model that predicts the kinetic evolution of DNA damages taking full advantage of a microdosimetric description of a radiation energy deposition. We show that, despite the high generality and flexibility of GSM2, an explicit form for the survival fraction curve predicted by the GSM2 is achievable.

Effect of data leakage in brain MRI classification using 2D convolutional neural networks.

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer's disease (AD) and Parkinson's disease (PD).

Interaction of therapeuticC ions with bone-like targets: physical characterization and dosimetric effect at material interfaces.

Carbon therapy is a promising treatment option for cancer. The physical and biological properties of carbon ions can theoretically allow for the delivery of curative doses to the tumor, while simultaneously limiting risks of toxicity to adjacent healthy structures. The treatment effectiveness can be further improved by decreasing the uncertainties stemming from several sources, including the modeling of tissue heterogeneity.

Generalized stochastic microdosimetric model: The main formulation.

The present work introduces a rigorous stochastic model, called the generalized stochastic microdosimetric model (GSM^{2}), to describe biological damage induced by ionizing radiation. Starting from the microdosimetric spectra of energy deposition in tissue, we derive a master equation describing the time evolution of the probability density function of lethal and potentially lethal DNA damage induced by a given radiation to a cell nucleus. The resulting probability distribution is not required to satisfy any a priori conditions.

Epidemiology and pathophysiologic insights of coronary atherosclerosis relevant for contemporary non-invasive imaging.

In the past few years significant changes have taken place in the diagnostic and therapeutic approach to patients with coronary artery disease (CAD) and/or ischemic heart disease (IHD). New discoveries about the development and progression of coronary atherosclerosis have changed the clinical landscape. At the same time a marked decrease in cardiovascular (CV) mortality and CAD incidence have been observed in many Countries but particularly in the most industrialized ones.