Responsible Radiomics Research for Faster Clinical Translation

It is now recognized that intratumoral heterogeneity is associated with more aggressive tumor phenotypes leading to poor patient outcomes (1). Medical imaging plays a central role in related investigations, because radiologic images are routinely acquired during cancer management. Imaging modalities such as 18F-FDG PET, CT, and MRI are minimally invasive and would constitute an immense source of potential data for decoding tumor phenotypes (2). Computer-aided diagnosis methods and systems exploiting medical images have been developed for decades, but their wide clinical implementation has been hampered by false-positive rates (3). As a consequence, routine clinical exploitation of images still consists mostly of visual or manual assessments. Today, the development of machine-learning techniques and the rise of computational power allow for the exploitation of a large number of quantitative features (4). This ability has led to a new incarnation of computer-aided diagnosis, “radiomics,” which refers to the characterization of tumor phenotypes via the extraction of highdimensional mineable data—for example, morphologic, intensitybased, fractal-based, and textural features—from medical images and whose subsequent analysis aims at supporting clinical decision making. A first proof-of-concept study dedicated to the prediction of tumor outcomes using PET radiomics-based multivariable models built via machine learning was published in 2009 (5). The term radiomics was then first used in 2010 to describe how imaging features can reflect gene expression (6). Other early radiomics studies followed (7,8), including some highlighting early on that the reliability of existing features is affected by acquisition protocol, reconstruction, test–retest consistency, preprocessing, and segmentation (9–13). The overall framework of radiomics was then explicitly described in 2012 (14), and in the years that followed, this emerging field experienced exponential growth (15). In the context of precision oncology, the radiomics workflow for the construction of predictive or prognostic models consists of 3 major steps (Fig. 1A): medical image acquisition, computation of radiomics features, and statistical analysis and machine learning. To apply the models to new patients for treatment personalization, a prospective model evaluation (preferably in a multicenter setup) is necessary. Radiomics research has already shown great promise for supporting clinical decision making. However, the fact that radiomicsbased strategies have not yet been translated to routine practice can be partly attributed to the low reproducibility of most current studies. The workflow for computing features is complex and involves many steps (Fig. 1B), often leading to incomplete reporting of methodologic information (e.g., texture matrix design choices and gray-level discretization methods). As a consequence, few radiomics studies in the current literature can be reproduced from start to end. Other major issues include the limited number of patients available for radiomics research, the high false-positive rates (similar to those of analogous computer-aided diagnosis methods), and the reporting of overly optimistic results, all of which affect the generalizability of the conclusions reached in current studies. Medical imaging journals are currently overwhelmed by a large volume of radiomics-related articles of variable quality and associated clinical value. The aim of this editorial is to present guidelines that we think can improve the reporting quality and therefore the reproducibility of radiomics studies, as well as the statistical quality of radiomics analyses. These guidelines can serve not only the authors of such studies but also the reviewers who assess their appropriateness for publication.