A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in Lu-DOTATATE therapy.

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

https://www.ncbi.nlm.nih.gov/pubmed/37722718

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;: