Radiomics: extracting more information from medical images using advanced feature analysis

Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar, Sara Carvalho, Ruud G P M van Stiphout, Patrick Granton, Catharina M L Zegers, Robert Gillies, Ronald Boellard, André Dekker, Hugo J W L Aerts, Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar, Sara Carvalho, Ruud G P M van Stiphout, Patrick Granton, Catharina M L Zegers, Robert Gillies, Ronald Boellard, André Dekker, Hugo J W L Aerts

Abstract

Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

Conflict of interest statement

Conflict of interest statement

None declared.

Copyright © 2011 Elsevier Ltd. All rights reserved.

Figures

Fig. 1
Fig. 1
Different sources of information, e.g. demographics, imaging, pathology, toxicity, biomarkers, genomics and proteomics, can be used for selecting the optimal treatment.
Fig. 2
Fig. 2
Multilevel imaging: anatomical, functional, and molecular imaging.
Fig. 3
Fig. 3
(A) Two representative 3-D representations of a round tumour (top) and spiky tumour (bottom) measured by computed tomography (CT) imaging. (B) Texture differences between non-small cell lung cancer (NSCLC) tumours measured using CT imaging, more heterogeneous (top) and more homogeneous (bottom). (C) Differences of FDG-PET uptake, showing heterogeneous uptake.
Fig. 4
Fig. 4
The Radiomics workflow. On the medical images, segmentation is performed to define the tumour region. From this region the features are extracted, e.g. features based on tumour intensity, texture and shape. Finally, these features are used for analysis, e.g. the features are assessed for their prognostic power, or linked with stage, or gene expression.

Source: PubMed

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