Methodological appraisal practices in AI-Based radiomics research in paediatric brain tumour Imaging: A Meta-Research study of systematic reviews.
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BACKGROUND: Artificial intelligence-based radiomics offers a potential adjunct to the current clinical management of paediatric brain tumours by enabling prediction of key diagnostic features. However, despite promising research, integration into clinical practice remains limited. This may in part reflect limitations in the underlying evidence base. Previous research has identified inconsistent methodological quality in primary studies and systematic reviews, as well as inadequate risk of bias assessment of primary studies within reviews. Given these concerns, rigorous appraisal of the available evidence base is essential to determine whether current findings are reliable and suitable for clinical translation. This study aimed to characterise the methodological appraisal approaches used in systematic reviews of artificial intelligence-based radiomics research in paediatric brain tumour imaging, with particular attention to how review authors select and apply tools for risk of bias, methodological quality, and reporting completeness. METHODS: We performed a meta-research study of systematic reviews. PubMed, Embase, Web of Science, Scopus and Medline were searched in March 2024 without date limits. Study selection followed PRISMA 2020 guidelines. Reviews of artificial intelligence-based radiomics applications in predominately paediatric populations with primary brain tumours were included. We evaluated the methodological appraisal approaches used, including tools assessing risk of bias, methodological quality and reporting completeness. RESULTS: Seven systematic reviews met inclusion criteria. Among these, three reviews (3/7) did not employ any formal methodological appraisal tool, and only one provided a rationale for this omission. Four reviews (4/7) applied at least one formal appraisal tool using five distinct instruments in total. Two (2/5) of these instruments were designed specifically for artificial intelligence-based research. The Quality Assessment of Diagnostic Accuracy Studies 2 was the most used tool, applied across two reviews. CONCLUSION: Methodological appraisal is applied inconsistently in systematic reviews of artificial intelligence-based radiomics research in paediatric neuro-oncology. This may reduce confidence in the current evidence base and hinder clinical translation. While some reviews used formal appraisal tools, these were usually conventional tools that may not fully capture methodological considerations specific to artificial intelligence-based research. Greater use of consistent, transparent, and artificial intelligence-aware appraisal approaches, alongside improved reporting in primary studies, is needed to support more reliable evidence synthesis and translation of radiomics into clinical practice.