Computational Data Science Laboratory (CDS-Lab), ICAR-CNR

Omics Imaging

April 2019

This page has been created as a compendium of the paper

L. Antonelli, M. R. Guarracino, L. Maddalena, and M. Sangiovanni
Integrating imaging and omics data: A review
Biomedical Signal Processing and Control 52, 264-280, 2019.

Omics Imaging is an emerging research field that arises with the recent advances in acquiring high-throughput omics data and multi-modal imaging data, whose primary task is to perform integrative analysis of omics (genomics, transcriptomics, proteomics, other omics) data and structural, functional, and molecular imaging data.
Here, we report the tables summarizing some of its basic ingredients:

- Imaging Data: Some imaging data investigated in omics imaging studies;
- Omics Data: Some omics data types used in omics imaging studies;
- Integrated Resources: Resources collecting omics and imaging data adopted in omics imaging studies;
- Methods: Some statistical machine learning methods in omics imaging studies;
- Applications: Some applications of omics imaging studies;
- Acronyms: Acronyms/abbreviations adopted in this page;
- Literature: Some omics imaging studies.

Besides giving online links to the referred resources, this page aims at providing an updated state-of-the-art, as it will be extended to include newly found/published literature on the subject.

Imaging Data

Imaging data Acronym Type of information Used by
Computerized Tomography CT Structural [1], [2]
diffusion MRI dMRI Structural [4], [5]
Dynamic Contrast-Enhanced MRI DCE-MRI Functional [10], [11], [12]
Functional MRI fMRI Functional [13], [14], [15]
Histological images   Structural [6]
Magnetic Resonance Imaging MRI Structural [3], [7], [8], [9], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25]
Positron Emission Tomography PET Functional [7], [8], [9]
Combined PET and CT PET-CT Structural&functional [1]

Omics Data

Omics category Extracted data Data acronym Used by
Epigenomics DNA methylation   [10]
Genomics Copy Number Variations CNVs [6], [10], [12]
  Rare variants   [21]
  Single Nucleotide Polymorphisms SNPs [4], [5], [7], [8], [9], [12], [13], [14], [15], [17], [20], [22], [24]
Proteomics Protein expression   [12], [25]
Transcriptomics long noncoding RNA expression lncRNA [11]
  messenger RNA expression mRNA [1], [2], [3], [6], [10], [12], [16], [17], [23]
  micro RNA expression miRNA [12], [16]

Integrated Resources

Name Acronym Website Used by
Alzheimer's Disease Neuroimaging Initiative ADNI [7], [9], [13], [21], [24], [25]
Brain Imaging Genetics BIG [22]
Enhancing NeuroImaging Genetics through MetaAnalysis ENIGMA [17]
IMAGEN study IMAGEN [17], [24]
International Multi-centre persistent ADHD CollaboraTion IMpACT [22]
Mind Clinical Imaging Consortium MCIC [15]
NeuroIMAGE project NeuroIMAGE [22]
Parkinson's Progression Markers Initiative PPMI [4]
Pediatric Imaging, Neurocognition, and Genetics study PING [20]
The Cancer Genome Atlas TCGA [3], [6], [10], [12], [16], [19], [23]
The Cancer Imaging Archive TCIA [10]
UKBioBank UKBioBank [5]


Method Used by
Canonical Correlation Analysis (CCA) [15], [16], [24], [25]
Deep learning [23]
Network analysis [6], [13]
Regression analysis [1], [4], [5], [6], [8], [10], [11], [12], [14], [15], [17], [19], [22]
Supervised classification [3], [7], [9], [21]
Unsupervised clustering [2], [3], [18], [20]


Category Type Type acronym Investigated by
Cancers Breast cancer   [10], [11], [12], [19]
  GlioBlastoma Multiforme GBM [3], [16], [18], [23]
  HepatoCellular Carcinoma HCC [2]
  LUng ADenocarcinoma LUAD [6]
  Non-Small Cell Lung Cancer NSCLC [1]
Healthy controls Genetic variations associated with human brain volume   [17], [24]
Neurological diseases Alzheimer's Disease AD [7], [9], [13], [21], [25]
  Parkinson's Disease PD [4]
  Schizophrenia SCZ [5], [15]
Psychiatric disorders Anxiety and Stress Response   [14]
  Attention Deficit Hyperactivity Disorder ADHD [8], [22]
  Specific Learning Disorders SLD [20]


Abbreviation/Acronym Extended
AD Alzheimer's Disease
ADHD Attention Deficit Hyperactivity Disorder
ADNI Alzheimer's Disease Neuroimaging Initiative
BIG Brain Imaging Genetics
CT Computerized Tomography
DCE-MRI Dynamic Contrast-Enhanced MRI
dMRI diffusion MRI
ENIGMA Enhancing NeuroImaging Genetics through MetaAnalysis
fMRI Functional MRI
GBM GlioBlastoma Multiforme
HCC HepatoCellular Carcinoma
IMpACT International Multi-centre persistent ADHD CollaboraTion
LUAD LUng ADenocarcinoma
MCIC Mind Clinical Imaging Consortium
MRI Magnetic Resonance Imaging
NeuroIMAGE NeuroIMAGE project
NSCLC Non-Small Cell Lung Cancer
PD Parkinson's Disease
PET Positron Emission Tomography
PET-CT Combined PET and CT
PING Pediatric Imaging, Neurocognition, and Genetics study
PPMI Parkinson's Progression Markers Initiative
SCZ Schizophrenia
SLD Specific Learning Disorders
TCGA The Cancer Genome Atlas
TCIA The Cancer Imaging Archive
UKBioBank UKBioBank


ID Reference DOI link
[1] O. Gevaert, J. Xu, C. Hoang, et al., Non-small cell lung cancer: Identifying prognostic imaging biomarkers by leveraging public gene expression microarray data-methods and preliminary results, Radiology 264 (2) (2012) 387–396.
[2] E. Segal, et al., Decoding global gene expression programs in liver cancer by noninvasive imaging, Nature Biotechnology 25 (6) (2007) 675–1375 680.
[3] N. Beig, et al., Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma, in: Proc. SPIE, Vol. 10134, 2017, pp. 101341U–101341U–10.
[4] M. Kim, J. Kim, S. Lee, H. Park, Imaging genetics approach to Parkinson's disease and its correlation with clinical score, Scientific Reports 7 (46700).
[5] L. M. Reus, et al., Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank, Scientific Reports 7 (2017).
[6] C. Wang, H. Su, L. Yang, K. Huang, Integrative analysis for lung adenocarcinoma predicts morphological features associated with genetic variations, in: Biocomputing 2017, World Scientific, 2016, pp. 82–93.
[7] J. Peng, L. An, X. Zhu, Y. Jin, D. Shen, Structured sparse kernel learning for imaging genetics based Alzheimer's disease diagnosis, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2016, pp. 70–78.
[8] H. L. Sigurdardottir, et al., Effects of norepinephrine transporter gene variants on NET binding in ADHD and healthy controls investigated by PET, Human Brain Mapping 37 (3) (2016) 884–895.
[9] Z. Zhang, H. Huang, D. Shen, Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction, Frontiers in Aging Neuroscience 6.
[10] W. Guo, H. Li, Y. Zhu, L. Lan, S. Yang, K. Drukker, E. Morris, E. Burnside, G. Whitman, M. L. Giger, et al., Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data, Journal of Medical Imaging 2 (4) (2015).
[11] S. Yamamoto, W. Han, Y. Kim, L. Du, N. Jamshidi, D. Huang, J. H. Kim, M. D. Kuo, Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis, Radiology 275 (2) (2015) 384–392.
[12] Y. Zhu, H. Li, W. Guo, K. Drukker, L. Lan, M. L. Giger, Y. Ji, Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma, Scientific Reports 5 (2015).
[13] C. Gao, J. Kim, W. Pan, for the Alzheimer's Disease Neuroimaging Initiative, Adaptive testing of SNP-brain functional connectivity association via a modular network analysis, in: Biocomputing 2017, World Scientific, 2016, pp. 58–69.  
[14] M. N. Smolka, G. Schumann, J. Wrase, S. M. Grusser, H. Flor, K. Mann, D. F. Braus, D. Goldman, C. Buchel, A. Heinz, Catechol-omethyltransferase val158met genotype affects processing of emotional stimuli in the amygdala and prefrontal cortex, Journal of Neuroscience 25 (4) (2005) 836–842.
[15] P. Zille, V. Calhoun, Y.-P.Wang, Enforcing co-expression in multimodal regression framework, in: Biocomputing 2017, World Scientific, 2016, pp. 105–116.  
[16] P. Zinn, B. Majadan, P. Sathyan, et al., Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme multiforme, PLoS ONE 6 (10), 2011.
[17] S. Desrivi¸res, et al., Single nucleotide polymorphism in the neuroplastin locus associates with cortical thickness and intellectual ability in adolescents, Molecular Psychiatry 20 (2) (2015) 263.
[18] M. Diehn, et al., Identification of noninvasive imaging surrogates for brain tumor gene-expression modules, Proceedings of the National Academy of Sciences of the United States of America 105 (13) (2008) 5213–5218.
[19] M. A. Mazurowski, J. Zhang, L. J. Grimm, S. C. Yoon, J. I. Silber, Radiogenomic analysis of breast cancer: Luminal B molecular subtype is associated with enhancement dynamics at MR imaging, Radiology 273 (2) (2014) 365–372.
[20] C. M. Mehta, J. R. Gruen, H. Zhang, A method for integrating neuroimaging into genetic models of learning performance, Genetic Epidemiology 41 (1) (2017) 4–17.
[21] K. Nho, ADNI, et al., Integration of bioinformatics and imaging informatics for identifying rare PSEN1 variants in Alzheimer's disease, BMC Medical Genomics 9 (Suppl 1), 2016.
[22] A. M. H. Onnink, et al., Enlarged striatal volume in adults with ADHD carrying the 9-6 haplotype of the dopamine transporter gene DAT1, Journal of Neural Transmission 123 (8) (2016) 905–915.
[23] N. F. Smedley, W. Hsu, Using deep neural networks for radiogenomic analysis, in: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 1529–1533.
[24] J. L. Stein, et al., Identification of common variants associated with human hippocampal and intracranial volumes, Nature Genetics 44 (5) (2012) 552–561.
[25] J. Yan, S. L. Risacher, K. Nho, A. J. Saykin, S. Li, Identification of discriminative imaging proteomics associations in Alzheimer's disease via a novel sparse canonical correlation model, in: Biocomputing 2017, World Scientific, 2016, pp. 94–104.