The DOI matches its Crossref record; 140 of the 140 references it cites were found in a registry or an index.
Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment
Abstract
Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. © RSNA, 2018
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“In oncology applications, the radiological images and genomic information of cancer patients have been integrated for enhanced prognosis prediction and patient classification [17].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [17]
Citations
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Self-citation
Metadata
- Authors
- Pinker, K., Chin, J., Melsaether, A. N., Morris, E. A., & Moy, L.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Venue
- Radiology
- Published
- 2018-06
- Volume
- 287
- Issue
- 3
- Pages
- 732-747
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2803669118
- Cited by
- 275
- References
- 140
- Citations by year
- 2026: 18 · 2025: 27 · 2024: 31 · 2023: 44 · 2022: 43 · 2021: 42 · 2020: 35 · 2019: 24 · 2018: 11
- FWCI
- 4.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Radiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- precision medicine, radiogenomics, radiomics, breast cancer, tumor heterogeneity, metastatic breast cancer, benign versus malignant differentiation, risk stratification, cancer screening, multimodal data integration, systems biology
- MeSH terms
- Breast Neoplasms, Female, Humans, Image Interpretation, Computer-Assisted, Mammography, Radiology, Biomarkers, Tumor, Genetic Predisposition to Disease, Genomics, Precision Medicine
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