ReviewUnravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging
Introduction
Tumour heterogeneity in cancers has been observed at the histological and genetic levels,1, 2, 3 and increased levels of intra-tumour genetic heterogeneity4, 5, 6, 7, 8 have been reported to be associated with adverse clinical outcomes.9, 10 In oncological imaging, phenotypic heterogeneity between and within tumours of a given patient is readily apparent and various imaging features are routinely described subjectively in radiology reports.11 Recently, however, imaging research has focused increasingly on the newly emergent field of radiomics, which is defined as a high-throughput process in which a large number of shape, edge, and texture metrics are extracted and quantified objectively and in a reproducible form12, 13, 14 (Fig 1). These quantitative metrics can provide important insights into tumour phenotype and as well as the interaction of the tumour with its microenvironment, defined as “habitat imaging”.11, 15 In the effort to delineate the biological and clinical implications of these new quantitative metrics, radiomic metrics obtained from magnetic resonance imaging (MRI), including diffusion-weighted (DW) and dynamic-contrast-enhanced (DCE) MRI sequences, computed tomography (CT), and combined 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/CT have been further correlated with genomics data, a process defined as radiogenomics.16 Radiogenomics and outcome data can be meaningfully mined with the goal of developing robust biomarkers that may potentially aid cancer diagnosis, improve assessment of treatment response, and better predict patient outcome.
Section snippets
Potential value of radiomics and radiogenomics
To date, several studies have focused on the correlation and integration of radiomics with genomics (defined as the systematic study of the complete DNA sequences [genome] of organisms17) and proteomics (defined as the systematic study of the complete complement of proteins [proteome] of organisms18) data, and their results continue to support the notion that radiomic metrics may perform relatively well as surrogates of molecular alterations and protein expression found in tissue samples.
Challenges and future directions
Radiogenomics is still in its early phases and its implementation requires completion of a series of steps to make it usable in daily clinical practice. First, it requires standardisation of imaging protocols, including image acquisition and post-processing, as well as robust segmentation algorithms that require minimal operator input.13 As with any study, a radiogenomics study must be validated against a set of independent data, to ensure its reproducibility. Radiomic analyses require large
Conclusion
In conclusion, complementary innovations in massive parallel sequencing, proteomics, and imaging that allow spatial and temporal quantification of tumour heterogeneity and its changes during drug treatment will provide the basis for the realisation of precision oncology.
Acknowledgements
This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748. The funding source had no involvement in the writing of the review and in the decision to submit the review for publication.
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