Radiomics: Rethinking the role of clinical imaging

Radiomics has the potential to improve the management of cancer patients, but further research is required before it can be adopted into routine clinical practice.
  1. James PB O'Connor  Is a corresponding author
  1. University of Manchester, United Kingdom
  2. The Christie Hospital, United Kingdom

The pressing need to improve cancer survival rates continuously motivates scientists to uncover the genetic and molecular composition of tumors, and use this information to develop more effective cancer therapies. Since tumors differ in their biological makeup, treatments can now often be tailored towards individual patients, in a strategy termed ‘personalized medicine’ (Schilsky, 2010). However, personalized medicine can only improve patient outcomes if there are reliable tests available to identify and analyze the biology of individual tumors, and detect which patients benefit from a given therapy (La Thangue and Kerr, 2011).

Medical imaging techniques underpin much decision-making in cancer medicine and can be used to determine, among other things, the size of a tumor and if it has spread into the surrounding tissue or other parts of the body. These and other tumor characteristics act as prognostic indicators and can help clinicians assign treatments for patients and monitor their response to therapy. However, only a few imaging techniques have been approved for use as companion diagnostic tools or as predictive biomarkers that can identify which patients are likely to benefit from a specific therapy (O'Connor et al., 2017). Now, in eLife, a team of researchers led by Hugo Aerts, Robert Gillies and Philippe Lambin – including Patrick Grossmann as first author – report on how an image analysis approach called radiomics could be used to enhance personalized medicine (Grossmann et al., 2017).

Radiomics uses computer algorithms to process the data collected by different medical imaging techniques and is becoming increasingly popular in cancer imaging research. One of its key characteristics is that radiomics recognizes that digital medical images are not only pictures – they are also are complex data. Radiomic analyses extract and measure an array of ‘features’ that describe the image texture and distributions of individual voxel values – the units that make up a 3D image – within a tumor. Each voxel represents a tiny amount of tissue and contains around 105 to 107 neoplastic and stromal cells, depending on tumor type and voxel dimensions.

Features can be descriptive terms derived from radiology reports or mathematical quantities. Feature extraction requires several complex steps in a defined pipeline: defining the regions of interest and segmenting three-dimensional images, extracting features and converting images into ‘mineable’ data. Finally, data from imaging techniques, and from biofluids and tumor tissue, are combined into models to predict patient prognosis or benefit from a specific therapy (Brady et al., 2016; Gillies et al., 2016).

Grossmann et al. – who are based at various insitiutes in the USA, Candada and The Netherlands – used radiomics to extract over 600 image features in computer tomography images from 262 patients with non-small cell lung cancer. They next examined how these features relate to the molecular characteristics of the tumors (such as the modulation of immune pathways and tumor suppressor proteins) and whether radiomic features predicted if the patients recovered after treatment. The findings were validated on an independent set of data from 89 other patients. Grossmann et al. discovered a connection between the radiomic phenotype of a tumor, the signaling pathways inside cells that drive how cancer develops, and clinical treatment outcomes. Moreover, by combining the data from radiomic, molecular and clinical studies, the overall chance of a patient surviving the cancer could be better predicted than when using clinical data alone.

The study by Grossmann et al. demonstrates the potential strength of radiomics, especially when combined with genomic and clinical information. One of the main benefits is that medical imaging data acquired in routine healthcare can be used in a new way to inform clinicians about the biology of a tumor and provide potential prognostic or predictive information. Imaging data from a large number of patients can be investigated rapidly. Further, radiomic image analysis is a non-invasive procedure that provides three-dimensional reconstructions of a tumor and avoids the problems associated with samples obtained through invasive biopsies (O'Connor et al., 2015).

However, several challenges need to be overcome before radiomics can be used as a companion diagnostic or predictive tool. Significant statistical problems can arise from analysis of such ‘big data’, with overfitting leading to relationships being seen were there are none (false discovery; Limkin et al., 2017). Once relationships are discovered, these findings need to be replicated in prospective studies.

Moreover, the terminology used for the derived image features needs to be standardized. At present, different investigators use their own names for the features, which makes it difficult to compare data between studies. In addition, the number of features incorporated into radiomic analyzes appears to vary even within individual research groups, leading to a phenomenon termed 'biomarker drift'. Biomarkers only become fit for guiding clinical decision-making when they become fixed in their acquisition, analysis and quality assurance processes, all defined clearly in standardized operating procedures (O'Connor et al., 2015).

Finally, ‘Picture Archiving and Communications System’ platforms must be adapted to integrate with radiomics algorithms in order to enable widespread clinical use. This will require coordinated efforts from clinical researchers and imaging scientists, along with additional computational power and infrastructure.

While considerable effort is required to rise to these challenges, the potential rewards are great. If enough evidence of the predictive power of radiomics can be produced, then radiomics may become a viable solution to help deliver personalized medicine in the clinic.

References

Article and author information

Author details

  1. James PB O'Connor

    James PB O'Connor is in the Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom and at The Christie Hospital, Manchester, United Kingdom

    For correspondence
    james.o'connor@manchester.ac.uk
    Competing interests
    No competing interests declared

Publication history

  1. Version of Record published: September 6, 2017 (version 1)

Copyright

© 2017, O'Connor

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,141
    Page views
  • 206
    Downloads
  • 10
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. James PB O'Connor
(2017)
Radiomics: Rethinking the role of clinical imaging
eLife 6:e30563.
https://doi.org/10.7554/eLife.30563
  1. Further reading

Further reading

    1. Cancer Biology
    Ning Yang, Xuebo Lu ... Kangdong Liu
    Research Article Updated

    Human esophageal cancer has a global impact on human health due to its high incidence and mortality. Therefore, there is an urgent need to develop new drugs to treat or prevent the prominent pathological subtype of esophageal cancer, esophageal squamous cell carcinoma (ESCC). Based upon the screening of drugs approved by the Food and Drug Administration, we discovered that Arbidol could effectively inhibit the proliferation of human ESCC in vitro. Next, we conducted a series of cell-based assays and found that Arbidol treatment inhibited the proliferation and colony formation ability of ESCC cells and promoted G1-phase cell cycle arrest. Phosphoproteomics experiments, in vitro kinase assays and pull-down assays were subsequently performed in order to identify the underlying growth inhibitory mechanism. We verified that Arbidol is a potential ataxia telangiectasia and Rad3-related (ATR) inhibitor via binding to ATR kinase to reduce the phosphorylation and activation of minichromosome maintenance protein 2 at Ser108. Finally, we demonstrated Arbidol had the inhibitory effect of ESCC in vivo by a patient-derived xenograft model. All together, Arbidol inhibits the proliferation of ESCC in vitro and in vivo through the DNA replication pathway and is associated with the cell cycle.

    1. Cancer Biology
    2. Computational and Systems Biology
    Pan Cheng, Xin Zhao ... Teresa Davoli
    Research Article

    How cells control gene expression is a fundamental question. The relative contribution of protein-level and RNA-level regulation to this process remains unclear. Here, we perform a proteogenomic analysis of tumors and untransformed cells containing somatic copy number alterations (SCNAs). By revealing how cells regulate RNA and protein abundances of genes with SCNAs, we provide insights into the rules of gene regulation. Protein complex genes have a strong protein-level regulation while non-complex genes have a strong RNA-level regulation. Notable exceptions are plasma membrane protein complex genes, which show a weak protein-level regulation and a stronger RNA-level regulation. Strikingly, we find a strong negative association between the degree of RNA-level and protein-level regulation across genes and cellular pathways. Moreover, genes participating in the same pathway show a similar degree of RNA- and protein-level regulation. Pathways including translation, splicing, RNA processing, and mitochondrial function show a stronger protein-level regulation while cell adhesion and migration pathways show a stronger RNA-level regulation. These results suggest that the evolution of gene regulation is shaped by functional constraints and that many cellular pathways tend to evolve one predominant mechanism of gene regulation at the protein level or at the RNA level.