Standardized algorithm for developing MRI-based radiomics models for detecting Grade Group ≥ 1 and ≥ 3 prostate cancer: recommendations from a systematic review, meta-analysis, and methodological quality assessment (RQS/METRICS)

Purpose of the study. Based on a synthesis of systematic review data using standard meta-analysis methodology, to evaluate the current state of radiomics model development, analyze sources of methodological heterogeneity, and present a standardized evidence-based algorithm to improve the quality and reproducibility of future studies.

Materials and methods. A systematic search was conducted in the PubMed/MEDLINE, Embase, Scopus, Web of Science, and Cochrane Library databases for studies published between January 2020 and December 2025. Keywords related to radiomics, magnetic resonance imaging (MRI), and prostate cancer (PCa) were used. According to the PICOS criteria, 27 studies involving 5,945 patients were included. Diagnostic performance was assessed using a random-effects meta-analysis, while methodological quality was evaluated independently by two reviewers using the Radiomics Quality Score (RQS) and METRICS instruments.

Results. Twenty-seven studies were included. The pooled diagnostic performance of radiomics models was high (AUC = 0.847); however, substantial heterogeneity was observed (I² = 70.32 %). The mean RQS score was 15.2 ± 3.4 (42.3 % of the maximum possible score), while the mean METRICS score was 67.5 ± 9.0 %. Systematic methodological weaknesses were identified, including retrospective study design (96.3 %), lack of external validation (77.8 %), absence of clinical utility assessment (70 %), and failure to report missing-data handling procedures (100 %). Meta-regression demonstrated that a high risk of bias was associated with inflated AUC estimates (p = 0.009).

Conclusion. The currently reported high performance of PCa radiomics models should be interpreted with caution because of methodological limitations. The proposed standardized algorithm, incorporating recommendations on prospective study design, validation, data processing, and model development, provides a practical methodological guidance for future research. Its implementation may be key to improving methodological rigor, reproducibility, and the clinical translational value of future studies in this field.

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