Big data in oncologic imaging.
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.
Department of Surgical Sciences, University of Torino, A.O.U. Città della Salute e della Scienza, via Genova 3, 10126, Turin, Italy.|Department of Radiology, Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142, km 3.95, Candiolo, 10060, Turin, Italy.
Daniele
D
Regge
Department of Radiology, Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142, km 3.95, Candiolo, 10060, Turin, Italy. simone.mazzetti@ircc.it.
Simone
S
Mazzetti
Department of Radiology, Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142, km 3.95, Candiolo, 10060, Turin, Italy.
Valentina
V
Giannini
Department of Medical Physics, Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142, km 3.95, Candiolo, 10060, Turin, Italy.
Christian
C
Bracco
Department of Medical Physics, Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142, km 3.95, Candiolo, 10060, Turin, Italy.
Michele
M
Stasi
D057225:Data Mining; D006801:Humans; D009369:Neoplasms / Q000000981:diagnostic imaging*; D000069079:Radiation Exposure
Big data; Imaging databases; Oncologic imaging; Quantitative imaging biomarkers; Renal damage; X-ray dose