Radiomics: the facts and the challenges of image analysis

被引：654
作者：
Rizzo S. [1 ]
Botta F. [2 ]
Raimondi S. [3 ]
Origgi D. [2 ]
Fanciullo C. [4 ]
Morganti A.G. [5 ]
Bellomi M. [6 ]
机构：
[1] Department of Radiology, IEO, European Institute of Oncology, IRCCS, Milan, IT
[2] Medical Physics, European Institute of Oncology, Milan
[3] Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan
[4] Università degli Studi di Milano, Postgraduate School in Radiodiagnostics, Milan
[5] Radiation Oncology Center, School of Medicine, Department of Experimental, Diagnostic and Specialty Medicine – DIMES, University of Bologna, Bologna
[6] Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan
来源：
European Radiology Experimental | / 2卷 / 1期
关键词：
Biomarkers; Clinical decision-making; Image processing (computer-assisted); Radiomics; Texture analysis;
D O I：
10.1186/s41747-018-0068-z
中图分类号：
学科分类号：
摘要：
Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging. © 2018, The Author(s).
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