Olivier Morin, Martin Vallières, Arthur Jochems, Henry C Woodruff, Gilmer Valdes, Steve E Braunstein, Joachim E Wildberger, Javier E Villanueva-Meyer, Vasant Kearney, Sue S Yom, Timothy D Solberg, Philippe Lambin

The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for transformative data science research for more personalized cancer treatments. In the last 5 years, accumulating evidence has indicated that non-invasive advanced imaging analytics, i.e. radiomics, can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using home-grown software have extracted engineered and deep quantitative features on three-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in a large number of early publications missing key information and/or utilizing underpowered patient datasets, without production of generalizable results. Quantitative imaging research is complex and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics, in particular, require a renewed focus on optimal study design/reporting practices, standardization, interpretability, data sharing and clinical trials. Standardization of image acquisition, feature calculation and statistical analysis (i.e. machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, working principles and fundamental changes to the current scientific approach are proposed, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.