Criteria for the translation of radiomics into clinically useful tests

Abstract

Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit–risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.

Abstract

Despite a considerable increase in research output over the past decades, the translation of radiomic research into clinically useful tests has been limited. In this Review, the authors provide 16 key criteria to guide the clinical translation of radiomics with the hope of accelerating the use of this technology to improve patient outcomes.

Key points

• Despite tens of thousands of radiomic studies, the number of settings in which radiomics is used to guide clinical decision-making is limited, in part owing to a lack of standardization of the radiomic measurement extraction processes and the lack of evidence demonstrating adequate clinical validity and utility.

• Processes to acquire and process source images and extract radiomic measurements should be established and harmonized.

• A radiomic model should be tested on external data not used for its development or, if no such dataset is available, tested using proper internal validation techniques.

• Model outputs should be shown to guide disease management decisions in a way that leads to a favourable risk–benefit balance for patients.

• Clinical performance should be assessed periodically in its intended clinical setting (task and population) after model lockdown.

• A list of 16 criteria for the optimal development of a radiomic test has been compiled herein and should hopefully guide the implementation of future radiomic analyses.