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

Abstract

Abstract 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 ion beams and for a high number of cell-lines. We compare the prediction of ANAKIN to the only two radiobiological models for Relative Biological Effectiveness prediction used in clinics, that is the Microdosimetric Kinetic Model and the Local Effect Model (LEM version III), showing how ANAKIN has higher accuracy over the all considered cell survival fractions. 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.