Performance of Conventional and Machine-Learning Approaches for the Diagnosis of Tumor Recurrence on MRI after Radiation Therapy of Brain Metastases

S, A., B, R., S, J., L, A., R, S. de C., C, B., F, B., M, E. H., GCTE, G., … M, L. (2021). Performance of Conventional and Machine-Learning Approaches for the Diagnosis of Tumor Recurrence on MRI after Radiation Therapy of Brain Metastases. Austin Journal of Radiology, 8(7). https://doi.org/10.26420/austinjradiol.2021.1151
Authors:
Samy Ammari
Behnam Rabiee
S Juvina
Lara Antonios
Sallé de Chou R
Corinne Balleyguier
F. Bidault
El Haik M
Garcia GCTE
M. Laurence
Elaine Johanna Limkin
S. Bockel
Mohamed Khettab
Charlotte Robert
Alexandre Carré
Sylvain Reuzé
Nathalie Lassau
Émilie Chouzenoux
Laurent Dercle
Affiliated Authors:
Lara Antonios
Sallé de Chou R
Corinne Balleyguier
Elaine Johanna Limkin
Charlotte Robert
Nathalie Lassau
Émilie Chouzenoux
Laurent Dercle
Columbia Affiliation:
Subjects:
Radiomics in Medical Imaging Analysis (OpenAlex Topic)
Management of Brain Metastases in Cancer Patients (OpenAlex Topic)
Positron Emission Tomography Imaging in Oncology (OpenAlex Topic)
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Publication Type:
Article
Unique ID:
10.26420/austinjradiol.2021.1151
DOI:
10.26420/austinjradiol.2021.1151
Journal:
Austin Journal of Radiology
Publication Date:
Data Source:
OpenAlex
Source Link:
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