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Review
. 2017 Feb;90(1070):20160665.
doi: 10.1259/bjr.20160665. Epub 2016 Dec 12.

Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures

Ruben T H M Larue  1 , Gilles Defraene  2 , Dirk De Ruysscher  1 , Philippe Lambin  1 , Wouter van Elmpt  1
Affiliations
Review

Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures

Ruben T H M Larue et al. Br J Radiol. 2017 Feb.

Abstract

Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.

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Figures

Figure 1.
Figure 1.
An overview of the radiomics workflow and corresponding topics addressed in this review. PET, positron emission tomography.
Figure 2.
Figure 2.
Positron emission tomography (PET) radiomic features and their dependency on the delineation method in oesophageal cancer: the 50% maximum standardized uptake value (SUVmax) delineation is used as reference. Fuzzy locally adaptive Bayesian (FLAB) delineation was implemented as described previously.
See this image and copyright information in PMC

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