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

Objective.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.Approach.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. These spectra are used as physical input to three different formulations of themicrodosimetric kinetic model, and to thegeneralized stochastic microdosimetric model(GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra andin vitrosurvival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons.Main results.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 and12C fields, 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.Significance.MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.

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

The present work develops ANAKIN: anArtificial 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 ion beams and for a high number of cell-lines. We compare the prediction of ANAKIN to the only two radiobiological models forRelative Biological Effectivenessprediction used in clinics, that is theMicrodosimetric Kinetic Modeland theLocal Effect Model(LEM version III), showing how ANAKIN has higher accuracy over the all considered cell survival fractions. At last, via modern techniques ofExplainable 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.

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 Generalized Stochastic Microdosimetric Model GSM2, in its original full form, the Dirac GSM2 the Poisson GSM2 and the Repair-Misrepair Model (RMR). While GSM2 and the RMR models are known in literature, the Dirac and the Poisson GSM2  have been newly introduced in this work. We further numerically investigate via Monte Carlo simulation of four different particle beams, how the proposed stochastic approximations reflect into the predicted survival curves. To achieve these results, we consider different ion species at energies of interest for therapeutic applications, also including a mixed field scenario. RESULTS: We show how the Dirac GSM2, the Poisson GSM2 and the RMR can be obtained from the GSM2 under suitable approximations on the stochasticity considered. We analytically derive the cell survival curve predicted by the four models, characterizing rigorously the high and low dose limits. We further study how the theoretical findings emerge also using Monte Carlo numerical simulations. CONCLUSIONS: We show how different models include different levels of stochasticity in the description of cellular response to radiation. This translates into different cell survival predictions depending on the radiation quality.

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. Q¯ values and biological and dose equivalent values were also calculated. RESULTS: The data demonstrated that radiation quality changed more rapidly with depth than lateral distance from the SOBP. In beam, yD ranged from approximately 4 keV/µm at the entrance to 8 keV/µm at the SOBP far end, reaching approximately 15 keV/µm at the penumbra. Out of field, the overall highest value of 23 ± 2 keV/µm was observed at the beam-edge penumbra. Radiation quality changes caused RBE deviations from the clinical value of 1.1, whose extent depends on the approach used for assessing radiation quality as well as on the radiobiological model. For RBE10, microdosimetry-based models appeared to better reproduce the radiobiological data than the dose-averaged linear energy transfer model. Out of field, both the RBE and Q¯ values appeared to have limitations in describing the radiation biological effectiveness. This research also presents a first comprehensive benchmark of TOPAS code against in-field and out-of-field microdosimetric spectra of therapeutic protons. CONCLUSIONS: Further investigation will be necessary to evaluate the quantitative effects of RBE variations on treatment planning and assess the clinical consequences in terms of both tumor control and normal-tissue toxicity. The achievement of this goal calls for accurate radiobiological data to validate the RBE models.

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. Thanks to the ML models, we were able to reconstruct the microdosimetric spectra of both protons and carbon ions at therapeutic energies, predicting the real track length for every particle detected by the microdosimeter. The ML model results have been extensively studied, focusing on non-accurate predictions of the real track lengths. Such analysis has been used to identify HDM limitations and to understand possible future improvements of both the detector and the ML models.

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. We illustrate how several correction terms typically added a posteriori in existing radiobiological models to improve the prediction accuracy, are naturally included into GSM2. Among the most relevant features of the survival curve derived from GSM2 and presented in this article, is the linear-quadratic behavior at low doses and a purely linear trend for high doses. The study also identifies and discusses the connections between GSM2 and existing cell survival models, such as the Microdosimetric Kinetic Model (MKM) and the Multi-hit model. Several approximations to predict cell survival in different irradiation regimes are also introduced to include intercellular non-Poissonian behaviors.