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.
Acknowledgements from the authors
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).