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.

An Artificial Intelligence-Based Model for Cell Killing Prediction: Development, Validation and Explainability Analysis of the ANAKIN Model / Cordoni, Francesco G; Missiaggia, Marta; Scifoni, Emanuele; La Tessa, Chiara. - In: PHYSICS IN MEDICINE AND BIOLOGY. - ISSN 0031-9155. - 2023, 68:8(2023), p. 085017. [10.1088/1361-6560/acc71e]

An Artificial Intelligence-Based Model for Cell Killing Prediction: Development, Validation and Explainability Analysis of the ANAKIN Model

Cordoni, Francesco G
Primo
;
Missiaggia, Marta;Scifoni, Emanuele;La Tessa, Chiara
2023-01-01

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.
2023
8
Cordoni, Francesco G; Missiaggia, Marta; Scifoni, Emanuele; La Tessa, Chiara
An Artificial Intelligence-Based Model for Cell Killing Prediction: Development, Validation and Explainability Analysis of the ANAKIN Model / Cordoni, Francesco G; Missiaggia, Marta; Scifoni, Emanuele; La Tessa, Chiara. - In: PHYSICS IN MEDICINE AND BIOLOGY. - ISSN 0031-9155. - 2023, 68:8(2023), p. 085017. [10.1088/1361-6560/acc71e]
File in questo prodotto:
File Dimensione Formato  
An artificial intelligence-based model for cell killing prediction development, validation and explainability analysis of the ANAKIN model.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 1.35 MB
Formato Adobe PDF
1.35 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/378527
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact