Article (Scientific journals)
Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography.
Lucia, François; Lovinfosse, Pierre; Schick, U et al.
2023In Cancer Radiotherapie
Peer Reviewed verified by ORBi
 

Files


Full Text
1-s2.0-S1278321823001129-main.pdf
Publisher postprint (881.75 kB)
Download

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
Apprentissage automatique; Apprentissage profond; Artificial intelligence; Deep learning; Intelligence artificielle; Machine learning; Radiation therapy; Radiomics; Radiomique; Radiothérapie; Radiology, Nuclear Medicine and imaging; Oncology
Abstract :
[en] Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Lucia, François ;  Département de Physique Médicale > Service médical de médecine nucléaire et imagerie onco
Lovinfosse, Pierre ;  Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Schick, U;  Radiation Oncology Department, CHU de Brest, 29200 Brest, France, LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
Le Pennec, R;  Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
Pradier, O;  Radiation Oncology Department, CHU de Brest, 29200 Brest, France, LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
Salaun, P-Y;  Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
Hustinx, Roland  ;  Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Bourbonne, V;  Radiation Oncology Department, CHU de Brest, 29200 Brest, France, LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
Language :
English
Title :
Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography.
Publication date :
20 July 2023
Journal title :
Cancer Radiotherapie
ISSN :
1278-3218
eISSN :
1769-6658
Publisher :
Elsevier BV, France
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 25 August 2023

Statistics


Number of views
115 (5 by ULiège)
Number of downloads
70 (1 by ULiège)

Scopus citations®
 
0
Scopus citations®
without self-citations
0
OpenCitations
 
0

Bibliography


Similar publications



Contact ORBi