Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer

Background It has been believed that traditional handcrafted radiomic features extracted from magnetic resonance imaging (MRI) of tumors are normally shallow and low-ordered. Recent advancement in deep learning technology shows that the high-order deep radiomic features extracted automatically from...

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Veröffentlicht in:	Biomarker research Jg. 11; H. 1; S. 9 - 11
Hauptverfasser:	Liu, Qian, Hu, Pingzhao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 24.01.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Biomedical and Life Sciences Biomedicine Breast cancer Cancer Research Classification Deep learning Denoise autoencoder Disease management Epidermal growth factor ErbB-2 protein Estrogen Estrogen receptors Gene expression Genes Genomics Lymph nodes Magnetic resonance imaging Medical imaging Medical imaging equipment Medical research Medicine, Experimental Metastases Metastasis Patients Phenotypes Precision medicine Progesterone Radiogenomic biomarkers Radiogenomics Radiomics RNA Tumors Canada Deep learning Breast cancer Medical imaging Radiogenomics Denoise autoencoder
ISSN:	2050-7771, 2050-7771
Online-Zugang:	Volltext
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