Development of imaging biomarkers and generation of big data
Several image processing algorithms have emerged to cover unmet clinical needs but their application to radiological routine with a clear clinical impact is still not straightforward. Moving from local to big infrastructures, such as Medical Imaging Biobanks (millions of studies), or even more, Federations of Medical Imaging Biobanks (in some cases totaling to hundreds of millions of studies) require the integration of automated pipelines for fast analysis of pooled data to extract clinically relevant conclusions, not uniquely linked to medical imaging, but in combination to other information such as genetic profiling. A general strategy for the development of imaging biomarkers and their integration in the cloud for the quantitative management and exploitation in large databases is herein presented. The proposed platform has been successfully launched and is being validated nowadays among the early adopters’ community of radiologists, clinicians, and medical imaging researchers.
KeywordsImaging biomarkers Big data Cloud Radiology Radiomics Image processing
Compliance with ethical standards
Conflict of interest
AAB and LMB are shareholders of QUIBIM SL, a company dedicated to the analysis of imaging biomarkers.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 1.Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR (2016) DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. Peer J 24(4):e2057CrossRefGoogle Scholar
- 4.European Society of Radiology (ESR) (2013) ESR statement on the stepwise development of imaging biomarkers. Insights. Imaging. 4:147–152Google Scholar
- 7.Hamel S. eMetrics Summit. 2013. San Francisco, CA, USAGoogle Scholar
- 8.“big, adj. and adv.” OED Online. Oxford University Press, June 2016. Web. 26 June 2016Google Scholar
- 9.Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data radiology. Radiol Soc N Am 278:563–577Google Scholar
- 10.McIntyre RS, Cha DS, Jerrell JM, Swardfager W, Kim RD, Costa LG, Baskaran A, Soczynska JK, Woldeyohannes HO, Mansur RB, Brietzke E, Powell AM, Gallaugher A, Kudlow P, Kaidanovich-Beilin O, Alsuwaidan M (2014) Advancing biomarker research: utilizing ‘Big Data’ approaches for the characterization and prevention of bipolar disorder. Bipolar Disord 16:531–547CrossRefPubMedGoogle Scholar
- 11.Manikis GC, Kontopodis E, Nikiforaki K, Marias K, Papanikolaou N (2017) Imaging biomarker model-based analysis. Imaging Biomarkers. Springer International Publishing, Cham, pp 71–86Google Scholar
- 12.Alberich-Bayarri A, Ruiz-Martínez E, Hernández-Navarro R, Tomás-Cucarella J, García-Castro F (2017) A proposed imaging biomarkers analysis platform architecture for integration in clinics. Imaging Biomarkers. Cham: Springer International Publishing. pp 159–167Google Scholar