Radiomics: the process and the challenges.
"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Virendra
V
Kumar
Yuhua
Y
Gu
Satrajit
S
Basu
Anders
A
Berglund
Steven A
SA
Eschrich
Matthew B
MB
Schabath
Kenneth
K
Forster
Hugo J W L
HJ
Aerts
Andre
A
Dekker
David
D
Fenstermacher
Dmitry B
DB
Goldgof
Lawrence O
LO
Hall
Philippe
P
Lambin
Yoganand
Y
Balagurunathan
Robert A
RA
Gatenby
Robert J
RJ
Gillies
D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods; D008490:Medical Informatics / Q000379:methods; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods; D018787:Radiation Oncology / Q000379:methods; D015203:Reproducibility of Results; D012307:Risk Factors; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods