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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Vydáno v:Biomarker research Ročník 11; číslo 1; s. 9 - 11
Hlavní autoři: Liu, Qian, Hu, Pingzhao
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 24.01.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
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
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Shrnutí: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 tumor images can capture tumor heterogeneity in a more efficient way. We hypothesize that MRI-based deep radiomic phenotypes have significant associations with molecular profiles of breast cancer tumors. We aim to identify deep radiomic features (DRFs) from MRI, evaluate their significance in predicting breast cancer (BC) clinical characteristics and explore their associations with multi-level genomic factors. Methods A denoising autoencoder was built to retrospectively extract 4,096 DRFs from 110 BC patients’ MRI. Visualization and clustering were applied to these DRFs. Linear Mixed Effect models were used to test their associations with multi-level genomic features (GFs) (risk genes, gene signatures, and biological pathway activities) extracted from the same patients’ mRNA expression profile. A Least Absolute Shrinkage and Selection Operator model was used to identify the most predictive DRFs for each clinical characteristic (tumor size (T), lymph node metastasis (N), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status). Results Thirty-six conventional radiomic features (CRFs) for 87 of the 110 BC patients provided by a previous study were used for comparison. More than 1,000 DRFs were associated with the risk genes, gene signatures, and biological pathways activities (adjusted P -value < 0.05). DRFs produced better performance in predicting T, N, ER, PR, and HER2 status (AUC > 0.9) using DRFs. These DRFs showed significant powers of stratifying patients, linking to relevant biological and clinical characteristics. As a contrast, only eight risk genes were associated with CRFs. The RFs performed worse in predicting clinical characteristics than DRFs. Conclusions The deep learning-based auto MRI features perform better in predicting BC clinical characteristics, which are more significantly associated with GFs than traditional semi-auto MRI features. Our radiogenomic approach for identifying MRI-based imaging signatures may pave potential pathways for the discovery of genetic mechanisms regulating specific tumor phenotypes and may enable a more rapid innovation of novel imaging modalities, hence accelerating their translation to personalized medicine.
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ISSN:2050-7771
2050-7771
DOI:10.1186/s40364-023-00455-y