Multicontrast MRI-based radiomics for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with early triple negative breast cancer.
Breast cancer
Multi-contrast MRI
Radiomics
Triple negative breast cancer
Journal
Magma (New York, N.Y.)
ISSN: 1352-8661
Titre abrégé: MAGMA
Pays: Germany
ID NLM: 9310752
Informations de publication
Date de publication:
Dec 2021
Dec 2021
Historique:
received:
07
07
2020
accepted:
03
07
2021
revised:
04
06
2021
pubmed:
14
7
2021
medline:
12
11
2021
entrez:
13
7
2021
Statut:
ppublish
Résumé
To assess pre-therapeutic MRI-based radiomic analysis to predict the pathological complete response to neoadjuvant chemotherapy (NAC) in women with early triple negative breast cancer (TN). This monocentric retrospective study included 75 TN female patients with MRI (T1-weighted, T2-weighted, diffusion-weighted and dynamic contrast enhancement images) performed before NAC. For each patient, the tumor(s) and the parenchyma were independently segmented and analyzed with radiomic analysis to extract shape, size, and texture features. Several sets of features were realized based on the 4 different sequence images. Performances of 4 classifiers (random forest, multilayer perceptron, support vector machine (SVM) with linear or quadratic kernel) were compared based on pathological complete response (defined on the excised tissues), on 100 draws with 75% as training set and 25% as test. The combination of features extracted from different MR images improved the classifier performance (more precisely, the features from T1W, T2W and DWI). The SVM with quadratic kernel showed the best performance with a mean AUC of 0.83, a sensitivity of 0.85 and a specificity of 0.75 in the test set. MRI-based radiomics may be relevant to predict NAC response in TN cancer. Our results promote the use of multi-contrast MRI sources for radiomics, providing enrich source of information to enhance model generalization.
Identifiants
pubmed: 34255206
doi: 10.1007/s10334-021-00941-0
pii: 10.1007/s10334-021-00941-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
833-844Subventions
Organisme : LABEX PRIMES (FR)
ID : ANR-11-LABX-0063
Organisme : LyriCAN
ID : INCa_INSERM_DGOS_12563
Informations de copyright
© 2021. European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).
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