Quantitative imaging biomarker
Citation, DOI & article data
Quantitative imaging biomarkers are validated, standardized characteristics based on quantifiable features of biomedical imaging that can be reliably and objectively measured on a ratio or interval scale. The utility of quantitative imaging biomarkers lies in providing information beyond what can be seen visually to increase the accuracy of diagnosis, prediction and/or monitoring, which in some cases helps patients avoid invasive procedures (e.g. biopsies and surgeries).
Producing quantitative biomarkers can require techniques that allow reproducibility across different machines from different settings, such as the use of standardized calibration phantoms. Nonetheless, the development of such biomarkers often begins with the use of existing archived images. Finding and measuring quantitative imaging biomarkers is a practical application of radiomics.
Examples of quantitative imaging biomarkers already in clinical use include:
- proton density fat fraction for non-alcoholic fatty liver disease 1
- quantitative CT bone densitometry measurements in the lumbar spine for osteoporosis 2
Efforts are underway to standardize hippocampal volumetry 3, and thus make properly measured hippocampus volume a true QIB for Alzheimer disease. Ongoing research in radiogenomics suggests that in some cancers there is a relationship between gene expression and 'imaging phenotype' that could be captured in quantitative imaging biomarkers 4.
An emerging role for quantitative imaging biomarkers is for 'opportunistic screening' for diseases other than those imaging was completed for. Quantitative imaging biomarkers related to not only coronary artery calcium 2 but epicardial and thoracic fat in regular CTs 5 as well breast arterial calcification 6 are under investigation as markers for cardiovascular disease risk.
- 1. Sonja Kinner, Scott B. Reeder, Takeshi Yokoo. Quantitative Imaging Biomarkers of NAFLD. (2016) Digestive Diseases and Sciences. 61 (5): 1337. doi:10.1007/s10620-016-4037-1 - Pubmed
- 2. Fernando U. Kay, Orhan K. Oz, Suhny Abbara, Eduardo J. Mortani Barbosa, Jr, Prachi P. Agarwal, Prabhakar Rajiah. Translation of Quantitative Imaging Biomarkers into Clinical Chest CT. (2019) RadioGraphics. 39 (4): 957-976. doi:10.1148/rg.2019180168 - Pubmed
- 3. Wolf D, Bocchetta M, Preboske GM, Boccardi M, Grothe MJ. Reference standard space hippocampus labels according to the European Alzheimer's Disease Consortium-Alzheimer's Disease Neuroimaging Initiative harmonized protocol: Utility in automated volumetry. (2017) Alzheimer's & dementia : the journal of the Alzheimer's Association. 13 (8): 893-902. doi:10.1016/j.jalz.2017.01.009 - Pubmed
- 4. Katja Pinker, Fuki Shitano, Evis Sala, Richard K. Do, Robert J. Young, Andreas G. Wibmer, Hedvig Hricak, Elizabeth J. Sutton, Elizabeth A. Morris. Background, current role, and potential applications of radiogenomics. (2018) Journal of Magnetic Resonance Imaging. 47 (3): 604. doi:10.1002/jmri.25870 - Pubmed
- 5. Dey D, Dey NR, Dey LD, Dey BDS. Epicardial and thoracic fat - Noninvasive measurement and clinical implications. (2012) Cardiovascular Diagnosis and Therapy. 2 (2): 85-93. doi:10.3978/j.issn.2223-3652.2012.04.03 - Pubmed
- 6. Trimboli RM, Codari M, Guazzi M, Sardanelli F. Screening mammography beyond breast cancer: breast arterial calcifications as a sex-specific biomarker of cardiovascular risk. (2019) European journal of radiology. 119: 108636. doi:10.1016/j.ejrad.2019.08.005 - Pubmed