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

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

Total and Partial Fragmentation Cross-Section of 500 MeV/nucleon Carbon Ions on Different Target Materials

By using an experimental setup based on thin and thick double-sided microstrip silicon detectors, it has been possible to identify the fragmentation products due to the interaction of very high energy primary ions on different targets. Here we report total and partial cross-sections measured at GSI (Gesellschaft fur Schwerionenforschung), Darmstadt, for 500 MeV/n energy $^{12}C$ beam incident on water (in flasks), polyethylene, lucite, silicon carbide, graphite, aluminium, copper, iron, tin, tantalum and lead targets. The results are compared to the predictions of GEANT4 (v4.9.4) and FLUKA (v11.2) Monte Carlo simulation programs.Comment: 10pages, 13figures, 4table

The Generalized Stochastic Microdosimetric Model: the main formulation

The present work introduces a rigorous stochastic model, named Generalized Stochastic Microdosimetric Model (GSM2), to describe biological damage induced by ionizing radiation. Starting from 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 radiation in a cell nucleus. The resulting probability distribution is not required to satisfy any a priori assumption. Furthermore, we generalized the master equation to consider damage induced by a continuous dose delivery. In addition, spatial features and damage movement inside the nucleus have been taken into account. In doing so, we provide a general mathematical setting to fully describe the spatiotemporal damage formation and evolution in a cell nucleus. Finally, we provide numerical solutions of the master equation exploiting Monte Carlo simulations to validate the accuracy of GSM2. Development of GSM2 can lead to improved modeling of radiation damage to both tumor and normal tissues, and thereby impact treatment regimens for better tumor control and reduced normal tissue toxicities