ArticleMedical physics2014
Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers.
Article in Medical physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- The epidemiologic factors associated with breast density: A review.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2022Review
- [Multimodal, multiparametric and genetic breast imaging].Der Radiologe · 2021Review
- Combining molecular and imaging metrics in cancer: radiogenomics.Insights into imaging · 2020Review
- Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes.Scientific reports · 2019Article
- Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling.Cancer imaging : the official publication of the International Cancer Imaging Society · 2019Article
- Detecting the "gist" of breast cancer in mammograms three years before localized signs of cancer are visible.The British journal of radiology · 2019Article
- Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis.Oncotarget · 2018Review
- Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status.Breast cancer research : BCR · 2016Article
- Article
- Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.Breast cancer research : BCR · 2016Review
- Imaging genomics in cancer research: limitations and promises.The British journal of radiology · 2016Review
- Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma.Scientific reports · 2015Article
- Special Section Guest Editorial:Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine.Journal of medical imaging (Bellingham, Wash.) · 2015Article
- Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography.BMC cancer · 2015Article
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Authors and funding
10 authors.
Funding
Abstract
purposeIn this pilot study, the authors examined associations between image-based phenotypes and genomic biomarkers. The potential genetic contribution of UGT2B genes to interindividual variation in breast density and mammographic parenchymal patterns is demonstrated by performing an association study between image-based phenotypes and genomic biomarkers [single-nucleotide polymorphism (SNP) genotypes].
methodsThis candidate-gene approach study included 179 subjects for whom both mammograms and blood DNA samples had been obtained. The full-field digital mammograms were acquired using a GE Senographe 2000D FFDM system (12-bit; 0.1 mm-pixel size). Regions-of-interest, 256 × 256 pixels in size, selected from the central breast region behind the nipple underwent computerized image analysis to yield image-based phenotypes of mammographic density and parenchymal texture patterns. SNP genotyping was performed using a Sequenom MassArray System. One hundred twenty three SNPs with minor allele frequency above 5% were genotyped for the UGT2B gene clusters, and used in the study. The association between the image-based phenotypes and genomic biomarkers was assessed with the Pearson correlation coefficient via thePLINK software, and included permutation and correction for multiple SNP comparisons.
resultsFrom the phenotype-genotype association analysis, a parenchyma texture coarseness feature was found to be correlated with SNP rs451632 after multiple test correction for the multiple SNPs (p = 0.022). The power law β, which is used to characterize the frequency component of texture patterns, was found to be correlated with SNP rs4148298 (p = 0.035).
conclusionsThe authors' results indicate that UGT2B gene variation may contribute to interindividual variation in mammographic parenchymal patterns and breast density. Understanding the relationship between image-based phenotypes and genomic biomarkers may help understand the biologic mechanism for image-based biomarkers and yield a future role in personalized medicine.
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