arXiv subjects

Marta Missiaggia

Publications and source records attributed to Marta Missiaggia.

9 recordsLinked to original sources

arXiv2026

Measurements of the micro-spill structure of medical cyclotron and synchrotron beams and its impact on pulse pileup

Detector characterization and instrumentation testing are often performed at cyclotron and synchrotron facilities, many of which were originally developed for medical applications in cancer therapy. For particle physics experiments requiring a single-particle resolution, pileup can significantly degrade data quality, making precise knowledge of the beam time structure essential for selecting appropriate readout parameters. However, such information is often unavailable from the facilities and challenging to determine experimentally. Here, we report measurements of the spill time structure at two medical accelerator facilities using a silicon carbide (SiC) particle sensor coupled to a high-frequency readout system. Owing to its high carrier saturation velocity and the tolerance to large bias voltages, SiC is well suited for fast readout and measurements requiring precise timing. Using a 6 GHz readout with custom SiC diodes, we characterize the micro-spill structure of both cyclotron and synchrotron beams on a sub-nanosecond timescale. The measured arrival-time distributions exhibit modulation with the accelerator RF frequencies, reflecting features of the extraction process. The resolved micro-spill structure enables quantitative estimation of pileup contributions and provides design constraints for future readout electronics. The presented results emphasize the importance of the characterization of the beam time-structure characterization for the development of precise readout systems.

physics.ins-det↗

arXiv2026

A stochastic agent-based extension of the GSM2 model for particle therapy: cell-cycle dynamics, dose-rate dependence, and fractionation effects

Accurately linking microscopic energy deposition from ionizing radiation to emergent biological outcomes remains a central challenge in radiobiological modelling, particularly when stochastic damage induction, cell-cycle dynamics, and spatial organisation within irradiated tissues must be treated explicitly and consistently across scales. To address this, we introduce a stochastic agent-based radiobiological modelling framework for simulating biological response to particle irradiation, developed as an explicit single-cell extension of the Generalized Stochastic Microdosimetric Model (GSM2). Each cell is represented as an autonomous agent whose internal state, including DNA lesion counts, cell-cycle phase, and oxygenation level, evolves according to a continuous-time Markov chain driven by GSM2 transition rates. Radiation-induced damage induction, repair, misrepair, cell-cycle progression, proliferation, and migration are treated as competing stochastic events resolved through a next-event, event-driven algorithm, which provides computationally efficient scaling with system size while preserving full single-cell resolution. The framework is applied to three-dimensional tumour spheroids irradiated with 1H and 12C ions across a range of energies and dose rates. We characterise the spatiotemporal evolution of cell-cycle phase composition and spheroid volume following irradiation, and examine the dependence of cell survival on dose rate over four orders of magnitude. Several empirically established trends in biological response, including the dose-rate dependence of cell survival, its attenuation at high LET, and the inverse dose rate effect in split-dose irradiation, emerge from the model through the explicit coupling of particle arrivals, damage accumulation, and repair kinetics, without recourse to empirical correction factors as typically done.

physics.bio-ph↗

arXiv2026

Mechanistic driven TCP and NTCP modeling for particle therapy accounting for a broad range of physical irradiation parameters and tissue environmental conditions

In conventional radiotherapy, the probability of controlling tumor growth is quantified using Tumor Control Probability (TCP) models. Instead, the probability of experiencing a side effect after the irradiation of healthy tissues and organs is typically assessed using the concept of Normal Tissue Complication Probability (NTCP), an additional crucial metric for evaluating and comparing treatment plans. This work is dedicated to the development, implementation, and application of a general mechanistic model to describe the effects of particle therapy (PT) on different tissue organizations beyond Poissonian assumptions, extending the Generalized Stochastic Microdosimetric Model (GSM2), i.e., a stochastic radiobiological model that describes the time evolution of DNA lesions in a cell nucleus according to microdosimetric principles, to the study of macroscopic biological systems. Specifically, we extend the biological stage of radiation damage of the GSM2 model to larger spatial and temporal scales, involving cell populations with a specific geometric and functional architecture. The model's single-cell resolution allows it to account for energy deposition and tissue heterogeneity, considering different organ volume effects, cell type distributions, and oxygen gradients for different radiation qualities of the beam, that is, type, energy, and LET of radiation, and various fractionation schemes. We show the interplay between physical and environmental parameters on the induction of side effects on healthy tissues, for different radiation qualities and fractionation schemes, and we highlight the impact of biochemical heterogeneities in the target environment, for tumor response.

physics.med-ph↗

arXiv2026

A combined dose and microdosimetric modeling framework incorporating volume effects correlates with tissue sparing in proton minibeam radiotherapy

Proton minibeam (pMB) radiotherapy, delivers highly heterogeneous dose distributions alternating high-dose peaks and low-dose valleys. This aims to widen the therapeutic window by improving normal tissue sparing while maintaining the same or even better tumour control. The performance of pMB strongly depends on the collimator design and physical parameters. To better understand the physical and radiobiological drivers of this enhanced therapeutic window, we perform a detailed microdosimetric characterization of proton minibeams and assess their impact. We characterize radiation quality with microdosimetry through Monte Carlo simulations. Then we extend the Generalized Stochastic Microdosimetric Model to predict the normal tissue complication probability (NTCP) at different depths in water, 1cm, 2cm, and 4cm, for 100MeV proton minibeams realized with varying configurations of collimator. Results are compared with conventional homogeneous field (HF) irradiation after dose normalization to the tumor. The developed model is applied by considering tissues as divided into several functional subunits, connected by a seriality parameter. Microdosimetric characterization of proton minibeam irradiation shows differences between peak and valley regions in shaping lineal energy spectra, especially at low depth, while radiation quality uniforms progressively getting closer to the tumor. NTCP calculations results suggest an increased sparing effect for pMB over conventional HF. A strong dependence is found on the peak-to-valley dose ratio (PVDR), and on the seriality parameter. Predictions indicate substantial sparing from pMB, especially for PVDR > 15, including relatively serial organs with seriality around 0.7. This integrated dose-microdosimetric-biological framework elucidates how spatial fractionation, radiation quality, and organ architecture collectively shape tissue sparing in pMB.

physics.med-ph↗

arXiv2026

One scale to rule them all: interpretable multi-scale Deep Learning for predicting cell survival after proton and carbon ion irradiation

The relationship between the physical characteristics of the radiation field and biological damage is central to both radiotherapy and radioprotection, yet the link between spatial scales of energy deposition and biological effects remains not entirely understood. To address this, we developed an interpretable deep learning model that predicts cell survival after proton and carbon ion irradiation, leveraging sequential attention to highlight relevant features and provide insight into the contribution of different energy deposition scales. Trained and tested on the PIDE dataset, our model incorporates, beside LET, nanodosimetric and microdosimetric quantities simulated with MC-Startrack and Open-TOPAS, enabling multi-scale characterization. While achieving high predictive accuracy, our approach also emphasizes transparency in decision-making. We demonstrate high accuracy in predicting RBE for in vitro experiments. Multiple scales are utilized concurrently, with no single spatial scale being predominant. Quantities defined at smaller spatial domains generally have a greater influence, whereas the LET plays a lesser role.

physics.bio-ph↗

arXiv2025

Integrating nano- and micrometer-scale energy deposition models for mechanistic prediction of radiation-induced DNA damage and cell survival

We present an integrated modeling framework that combines the Generalized Stochastic Microdosimetric Model (GSM2), used to predict cell survival fractions, with MINAS-TIRITH, a fast and efficient Geant4 DNA-based tool for simulating radiation-induced DNA damage in cell populations. This approach enables the generation of spatially and structurally resolved double-strand break (DSB) distributions, capturing key features such as damage complexity and chromosome specificity. A novel application of the DBSCAN clustering algorithm is introduced to group DSBs at the micrometer scale. This allows the identification of physical aggregates of DNA damage and their association with subnuclear domains, providing a direct link to the cell survival probability as predicted by \gsm. The model was validated using experimental data from HUVEC cells irradiated with 220 kV X-rays and H460 cells exposed to protons over a wide linear energy transfer (LET) range, from approximately 4 keV/μm to over 20 keV/μm. Results show excellent agreement between simulations and experimental survival probabilities, making this one of the first consistent multi-scale models to bridge nanodosimetric and microdosimetric representations of radiation with biological outcomes such as cell survival. By incorporating the inherent stochastic nature of radiation-matter interactions, this framework effectively connects the physical properties of the radiation field to the biological response at the cellular level. Its accuracy across various radiation types and energies supports its potential for use in biologically optimized radiotherapy.

physics.bio-ph↗

arXiv2024

A multiscale radiation biophysical stochastic model describing the cell survival response at ultra-high dose rate

Ultra-high dose-rate (UHDR) radiotherapy, characterized by an extremely high radiation delivery rate, represents one of the most recent and promising frontier in radiotherapy. UHDR radiotherapy, addressed in the field as FLASH radiotherapy, is a disruptive treatment modality with several benefits, including significantly shorter treatment times, unchanged effectiveness in treating tumors, and clear reductions in side effects on normal tissues. While the benefits of UHDR irradiation have been well highlighted experimentally, the biological mechanism underlying the FLASH effect is still unclear and highly debated. Nonetheless, to effectively use UHDR radiotherapy in clinics, understanding the driving biological mechanism is paramount. Since the concurrent involvement of multiple scales of radiation damage has been suggested, we developed the MultiScale Generalized Stochastic Microdosimetric Model (MS-GSM2), a multi-stage extension of the GSM2, which is a probabilistic model describing the time evolution of the DNA damage in an irradiated cell nucleus. The MS-GSM2 can investigate several chemical species combined effects, DNA damage formation, and time evolution. We demonstrate that the MS-GSM2 can predict various in-vitro UHDR experimental results across various oxygenation levels, radiation types, and energies. The MS-GSM2 can accurately describe the empirical trend of dose and dose rate-dependent cell sensitivity over a wide range, consistently describing multiple aspects of the FLASH effect and reproducing the main evidence from the in-vitro experimental data. Our model also proposes a consistent explanation for the differential outcomes observed in normal tissues and tumors, in-vivo and in-vitro.

physics.bio-ph↗

arXiv2023

Integrating microdosimetric in vitro RBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeling for RBE Assessment (MONAS) tool

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. These spectra are used as physical input to three different formulations of the Microdosimetric Kinetic Model (MKM), and to the Generalized Stochastic Microdosimetric Model (GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra and in vitro survival fraction data. We present two different applications of the code: i) the depth-RBE curve calculation from a passively scattered proton SOBP, and ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons. MONAS can estimate dose dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP field, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target. MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.

physics.med-ph↗

arXiv2023

An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model

The present work develops ANAKIN: an Artificial iNtelligence bAsed model for (radiation induced) cell KIlliNg prediction. 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 ions beams and for a high number of cell--lines. We compare the prediction of ANAKIN to the only two radiobiological model for RBE prediction used in clinics, that is the Microdosimetric Kinetic Model (MKM) and the Local Effect Model (LEM version III), showing how ANAKIN has higher accuracy over the all considered biological endpoints. At last, via modern techniques of Explainable Artificial Intelligence (XAI), we show how ANAKIN predictions can be understood and explained, highlighting how ANAKIN is in fact able to reproduce relevant well-known biological patterns, such as the overkilling effect.

physics.med-ph↗