An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model
This work addresses the need for more accurate and explainable predictions of cell survival in radiobiology, which could improve clinical radiation therapy planning, though it is incremental as it builds on existing AI and radiobiological methods.
The researchers developed ANAKIN, an AI model for predicting radiation-induced cell killing, and validated it on 513 experiments from the PIDE database, showing it achieves higher accuracy than existing clinical models like MKM and LEM III across multiple biological endpoints.
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.