Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma.

Date publication

janvier 2025

Journal

European journal of nuclear medicine and molecular imaging

Auteurs

Membres identifiés du Cancéropôle Est :
Pr VERGER Antoine , Dr BUND Caroline

Tous les auteurs :
Ahrari S, Zaragori T, Zinsz A, Hossu G, Oster J, Allard B, Al Mansour L, Bessac D, Boumedine S, Bund C, De Leiris N, Flaus A, Guedj E, Kas A, Keromnes N, Kiraz K, Kuijper FM, Maitre V, Querellou S, Stien G, Humbert O, Imbert L, Verger A

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/39821662

Résumé

Radiomics-based machine learning (ML) models of amino acid positron emission tomography (PET) images have shown efficiency in glioma prediction tasks. However, their clinical impact on physician interpretation remains limited. This study investigated whether an explainable radiomics model modifies nuclear physicians' assessment of glioma aggressiveness at diagnosis.

Mots clés

Explainable machine learning, Glioma, Interpretability, Positron emission tomography, Radiomics

Référence

Eur J Nucl Med Mol Imaging. 2025 01 17;: