Abstract
In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.
Highlights
Adrenal glands can be affected by a great variety of different disorders, including, but not limited to, acute illnesses, functional abnormalities, infectious diseases, and oncological processes, with diagnostic imaging playing a relevant role in most cases [1,2,3]
Using the radiomics quality score (RQS), a scoring system proposed by Lambin and colleagues and designed to assess the crucial steps in radiomics pipelines, previous investigations have found that radiomics studies are heterogeneous in terms of quality [18]
Beyond the growing interest in radiomics and its many promises, the clinical translation of actual real-life applications must be grounded in methodologically rigorous studies and high-quality evidence
Summary
Adrenal glands can be affected by a great variety of different disorders, including, but not limited to, acute illnesses, functional abnormalities, infectious diseases, and oncological processes, with diagnostic imaging playing a relevant role in most cases [1,2,3]. In the effort to overcome the limitations of conventional image assessment and further increase the value of diagnostic imaging, a complex multi-step post-processing approach defined as radiomics has been proposed [10]. The premise behind this rapidly growing field is that medical images can be mined to extract quantitative data possibly related to the pathophysiology of a biological tissue [11].
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