Radiomics: the bridge between medical imaging and personalized medicine

Key Points

  • Radiomics is becoming increasingly more important in medical imaging

  • The explosion of medical imaging data creates an environment ideal for machine-learning and data-based science

  • Radiomics-based decision-support systems for precision diagnosis and treatment can be a powerful tool in modern medicine

  • Large-scale data sharing is necessary for the validation and full potential that radiomics represents

  • Standardized data collection, evaluation criteria, and reporting guidelines are required for radiomics to mature as a discipline

Abstract

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

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Figure 1: Flowchart depicting the workflow of radiomics and the application of the RQS.
Figure 2: Radiomics in cardiology.
Figure 3: Radiomics digital phantom data.
Figure 4: Radiogenomics analysis can reveal relationships between imaging phenotypes and gene-expression patterns.
Figure 5: Schematic overview of a clinical decision-support system graphical user interface illustrating the concept of delta-radiomics.
Figure 6: Schematic diagram of the CAT system.
Figure 7: Overview of the methodological processes for RLHC and how the radiomics workflow fits into the development of a CDSS.

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Acknowledgements

The authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015, no. 694812) and the QuIC-ConCePT project, which is partly funded by EFPI A companies and the Innovative Medicine Initiative Joint Undertaking (IMI JU) under Grant Agreement no. 115151. This research is also supported by the Dutch Technology Foundation STW (grant no. 10696 duCAT & P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. Authors also acknowledge financial support from the National Institute of Health (NIH-USA U01 CA 143062–01, Radiomics of NSCLC), EU 7 th framework program (EURECA, ARTFORCE – no. 257144, REQUITE – no. 601826), SME phase 2 (EU proposal 673780 – RAIL), the European Program H2020 (BD2Decide – PHC30-689715, ImmunoSABR – no. 733008, PREDICT - ITN no. 766276), Kankeronderzoekfonds Limburg from the Health Foundation Limburg and the Dutch Cancer Society (KWF UM 2011–5020, KWF UM 2009–4454, KWF MAC 2013–6425, KWF MAC 2013–6089) and Alpe d'HuZes-KWF (DESIGN), Center for Translational Molecular Medicine (TraIT), EUROSTARS (SeDI, CloudAtlas, and DART), Interreg V-A Euregio Meuse-Rhine (“Euradiomics”) and Varian Medical Systems (VATE and ROO).

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Correspondence to Philippe Lambin.

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Competing interests

A.D., leader of the Knowledge Engineering division at MAASTRO, A.J., T.L., J. v- S. and S.W. declare they receive financial support from Varian Medical Systems, a company developing a rapid learning health-care system. R.L. is a salaried employee of, and T.D. consults for ptTheragnostic B.V., a company developing biomarkers and software to individualize radiotherapy treatment. R.T.H.L. and P.L. are co-inventors of radiomics patents (EP2793164A1, US20160203599A1, and WO 2016060557A1).

Supplementary information

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Glossary

Phantom studies

An artificial structure that imitates human tissue properties is scanned on multiple machines to characterize scan output against a known physical standard.

Calibration-in-the-large

Describes whether the predictions deviate systematically (intercept), whereas the calibration slope should ideally be equal to 1.

The independence assumption

The definition in terms of conditional probabilities is that the probability of B is not changed by knowing that A has occurred. Statistically independent variables are always uncorrelated, but the converse is not necessarily true.

Feature discretization

The process of converting continuous features to discrete binned interval features.

Bootstrapping

Measures the accuracy (defined in terms of bias, variance, confidence intervals, prediction error, etc.) to characterize the sample distribution by way of repeated random sampling methods.

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Lambin, P., Leijenaar, R., Deist, T. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14, 749–762 (2017). https://doi.org/10.1038/nrclinonc.2017.141

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