Please confirm you are human (Sign Up for free to never see this)
← Back to Search
Radiogenomics: What It Is And Why It Is Important.
Published 2015 · Medicine
Save to my Library
Download PDFAnalyze on Scholarcy
In recent years, a new direction in cancer research has emerged that focuses on the relationship between imaging phenotypes and genomics. This direction is referred to as radiogenomics or imaging genomics. The question that subsequently arises is: What is the practical significance of elucidating this relationship in improving cancer patient outcomes. In this article, I address this question. Although I discuss some limitations of the radiogenomic approach, and describe scenarios in which radiogenomic analysis might not be the best choice, I also argue that radiogenomics will play a significant practical role in cancer research. Specifically, I argue that the significance of radiogenomics is largely related to practical limitations of currently available data that often lack complete characterization of the patients and poor integration of individual datasets. Radiogenomics offers a practical way to leverage limited and incomplete data to generate knowledge that might lead to improved decision making, and as a result, improved patient outcomes.
This paper references
Cancer Genome Atlas
T. Hampton (2006)
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles
K. Shin (2015)
A Multichannel Markov Random Field Framework for Tumor Segmentation With an Application to Classification of Gene Expression-Based Breast Cancer Recurrence Risk
A. Ashraf (2013)
Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome
J. O'Connor (2014)
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
M. Mazurowski (2014)
Creating and Curating a Terminology for Radiology: Ontology Modeling and Analysis
D. Rubin (2007)
Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape.
S. Yamamoto (2012)
Radiomics: the process and the challenges.
Virendra Kumar (2012)
MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.
D. Gutman (2013)
Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation.
N. Jamshidi (2014)
RadLex: a new method for indexing online educational materials.
C. Langlotz (2006)
Imaging descriptors improve the predictive power of survival models for glioblastoma patients.
M. Mazurowski (2013)
Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results.
O. Gevaert (2012)
Radiogenomics: radiobiology enters the era of big data and team science.
B. Rosenstein (2014)
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
Computer-extracted MR imaging features are associated with survival in glioblastoma patients
M. Mazurowski (2014)
Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features.
O. Gevaert (2014)
Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms
L. Grimm (2015)
Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma.
M. Kuo (2007)
Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging.
Aaron M Rutman (2009)
Establishment of a radiogenomics consortium.
C. West (2010)
Behind the numbers: Decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations.
M. Kuo (2014)
The Cancer Imaging Archive
Mutational heterogeneity in cancer and the search for new cancer-associated genes
M. S. Lawrence (2013)
Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme
P. Zinn (2011)
Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations.
C. Karlo (2014)
This paper is referenced by
Statistical aspects of radiogenomics: can radiogenomics models be used to aid prediction of outcomes in cancer patients?
Boya Ren (2017)
Qualitative Radiogenomics: Association between Oncotype DX Test Recurrence Score and BI-RADS Mammographic and Breast MR Imaging Features.
Genevieve A Woodard (2018)
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.
Yanqi Huang (2016)
CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer
Kai-Yu Sun (2020)
Prognostic Prediction of Glioblastomas By Using Genes and Image Features
Masatoshi Kondo (2018)
Management and Surveillance of Short- and Long-Term Sequelae of Radiation Therapy for the Treatment of Pediatric Brain Tumors
Fred Lam (2020)
Imaging predictors of treatment outcomes in rectal cancer: An overview.
L. Mahadevan (2018)
Lower-Grade Gliomas: Predicting DNA Methylation Subtyping and its Consequences on Survival with MR Features.
Hongdan Zhang (2020)
Using Naïve Bayesian Analysis to Determine Imaging Characteristics of KRAS Mutations in Metastatic Colon Cancer
Yash Pershad (2017)
Relation of peritumoral, prepectoral and diffuse edema with histopathologic findings of breast cancer in preoperative 3T magnetic resonance imaging
A. Gemici (2019)
Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging
M. A. Marino (2020)
Feasibility of genomic profiling with next-generation sequencing using specimens obtained by image-guided percutaneous needle biopsy
M. Sone (2019)
The Hungarian Twin Registry Update: Turning From a Voluntary to a Population-Based Registry.
Á. Tárnoki (2020)
Imaging genotyping of functional signaling pathways in lung squamous cell carcinoma using a radiomics approach
S. H. Bak (2018)
Standardized uptake value (SUVmax) in 18F-FDG PET/CT is correlated with the total number of main oncogenic anomalies in cancer patients
Amin Haghighat Jahromi (2020)
Imaging and the completion of the omics paradigm in breast cancer
D. Leithner (2018)
Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases
Mario Zanfardino (2019)
Comparison of Dual-energy CT and Subtraction CT for Renal Lesion Detection and Characterization
H. Do (2018)
Integrative Bayesian models using Post-selective Inference: a case study in Radiogenomics
Snigdha Panigrahi (2020)
Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT
X. Ma (2018)
Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?
Michael R. Harowicz (2017)
Imaging Informatics: A New Horizon for Radiology in the Era of Artificial Intelligence, Big Data, and Data Science
J. K. Kim (2019)
Integrative Radiogenomics Approach for Risk Assessment of Post-Operative Metastasis in Pathological T1 Renal Cell Carcinoma: A Pilot Retrospective Cohort Study
H. I. Lee (2020)
Editorial for "MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors".
T. Araki (2020)
Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?
Michael R. Harowicz (2017)
Radiogenomics Profiling for Glioblastoma-related Immune Cells Reveals CD49d Expression Correlation with MRI parameters and Prognosis
Hye Rim Cho (2018)
Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer
Ming Fan (2017)
Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data
M. Mazurowski (2017)
A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models
Ashirbani Saha (2018)
Development and validation of MMR prediction model based on simplified clinicopathological features and serum tumour markers
Yinghao Cao (2020)
Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.
Jay Kumar Raghavan Nair (2020)
CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study
J. Ou (2019)See more