A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in Lu-DOTATATE therapy.
Fiche publication
Date publication
septembre 2023
Journal
Medical physics
Auteurs
Membres identifiés du Cancéropôle Est :
Pr IMPERIALE Alessio
Tous les auteurs :
Plachouris D, Eleftheriadis V, Nanos T, Papathanasiou N, Sarrut D, Papadimitroulas P, Savvidis G, Vergnaud L, Salvadori J, Imperiale A, Visvikis D, Hazle JD, Kagadis GC
Lien Pubmed
Résumé
Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship. Data sets of consistent and reliable inter-center dosimetry findings are required to characterize this relationship.
Mots clés
177Lu-DOTATATE, dose prediction model, dosimetry, dosiomics, machine learning, radiomics, radiotoxicity, regression model, treatment planning
Référence
Med Phys. 2023 09 18;: