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Big data in oncologic imaging

  • ONCOLOGY IMAGING
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Cancer is a complex disease and unfortunately understanding how the components of the cancer system work does not help understand the behavior of the system as a whole. In the words of the Greek philosopher Aristotle “the whole is greater than the sum of parts.” To date, thanks to improved information technology infrastructures, it is possible to store data from each single cancer patient, including clinical data, medical images, laboratory tests, and pathological and genomic information. Indeed, medical archive storage constitutes approximately one-third of total global storage demand and a large part of the data are in the form of medical images. The opportunity is now to draw insight on the whole to the benefit of each individual patient. In the oncologic patient, big data analysis is at the beginning but several useful applications can be envisaged including development of imaging biomarkers to predict disease outcome, assessing the risk of X-ray dose exposure or of renal damage following the administration of contrast agents, and tracking and optimizing patient workflow. The aim of this review is to present current evidence of how big data derived from medical images may impact on the diagnostic pathway of the oncologic patient.

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Authors and Affiliations

  1. Department of Surgical Sciences, University of Torino, A.O.U. Città della Salute e della Scienza, via Genova 3, 10126, Turin, Italy

    Daniele Regge

  2. Department of Radiology, Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142, km 3.95, Candiolo, 10060, Turin, Italy

    Daniele Regge, Simone Mazzetti & Valentina Giannini

  3. Department of Medical Physics, Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142, km 3.95, Candiolo, 10060, Turin, Italy

    Christian Bracco & Michele Stasi

Authors
  1. Daniele Regge
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  2. Simone Mazzetti
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  3. Valentina Giannini
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  4. Christian Bracco
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  5. Michele Stasi
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Corresponding author

Correspondence to Simone Mazzetti.

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No funding was received for this work.

Conflict of interest

Daniele Regge declares that he has no conflict of interest. Simone Mazzetti declares that he has no conflict of interest. Valentina Giannini declares that she has no conflict of interest. Christian Bracco declares that he has no conflict of interest. Michele Stasi declares that he has no conflict of interest.

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Regge, D., Mazzetti, S., Giannini, V. et al. Big data in oncologic imaging. Radiol med 122, 458–463 (2017). https://doi.org/10.1007/s11547-016-0687-5

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  • DOI: https://doi.org/10.1007/s11547-016-0687-5

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