Non-invasive assessment of breast cancer molecular subtypes with multiparametric magnetic resonance imaging radiomics Journal Article

   

Authors:	Leithner, D.; Mayerhoefer, M. E.; Martinez, D. F.; Jochelson, M. S.; Morris, E. A.; Thakur, S. B.; Pinker, K.
Article Title:	Non-invasive assessment of breast cancer molecular subtypes with multiparametric magnetic resonance imaging radiomics
Abstract:	We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77-0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75-0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.
Keywords:	magnetic resonance imaging; breast; brain; mri; features; international expert consensus; primary therapy; molecular subtypes; diffusion-weighted; cancer; radiomics; gadolinium deposition
Journal Title:	Journal of Clinical Medicine
Volume:	9
Issue:	6
ISSN:	2077-0383
Publisher:	MDPI
Date Published:	2020-06-01
Start Page:	1853
Language:	English
ACCESSION:	WOS:000554691400001
DOI:	10.3390/jcm9061853
PROVIDER:	wos
PMCID:	PMC7356091
PUBMED:	32545851
Notes:	Article -- Source: Wos

https://synapse.mskcc.org/synapse/works/has_related_data_chk/170199