The long and winding road of radiomics: learnings from two meta-analyses of the radiomics quality score
Abstract
The high-throughput extraction of radiomics features from medical images for predictive modelling holds great promise to improve the clinical management of patients. Previous meta-analyses into the radiomics quality score (RQS) applied in the literature have shown that after more than a decade of investigation, issues with workflow standardisation, model reproducibility, validation, and data accessibility persist and impede the clinical translation of radiomics-based models. These systematic findings have informed a timely review of the best practices and pitfalls to avoid within radiomics and predictive modelling, with a focus on realistic radiomics modelling in the context of limited sample sizes. Each section covers a radiomics topic that encompasses one or more RQS criteria and is broken into subsections as follows: (1) a discussion of the background and recommendations on the respective topic, (2) key findings from our meta-analyses and discovered pitfalls, and (3) a succinct list of actionable items that reflect best practice. New and emerging quality appraisal tools and the future direction of radiomics are also discussed.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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