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

TL;DR

This paper introduces ANAKIN, an AI model that predicts radiation-induced cell killing with high accuracy, outperforming existing models, and provides explainability to understand biological patterns in the predictions.

Contribution

The paper presents ANAKIN, a novel AI-based model for cell killing prediction that surpasses existing radiobiological models in accuracy and incorporates explainability techniques.

Findings

ANAKIN accurately predicts biological endpoints across various radiation types and cell lines.

ANAKIN outperforms the Microdosimetric Kinetic Model and Local Effect Model in prediction accuracy.

Explainability analysis reveals ANAKIN captures known biological effects like overkilling.

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 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.