Radiomics and the Image Biomarker Standardisation Initiative (IBSI): A Narrative Review Using a Six-Question Map and...

Radiomics can quantify image-derived tumor heterogeneity and support diagnosis, prognosis, and treatment assessment, yet adoption has been limited by poor reproducibility across scanners, protocols, and software. The Image Biomarker Standardisation Initiative (IBSI) was founded to harmonize feature definitions and preprocessing. This narrative review explains what IBSI standardizes, how it is implemented, and what clinicians need to know, using a six-question map (who, why, what, how, when, where) and an implementation framework linking standardized pipeline blocks to compliance tools. Targeted searches (PubMed/Google Scholar, 2010-2025) emphasized IBSI manuals, consensus statements, multicenter evaluations, and clinically oriented studies. Because this is a narrative review, no quantitative synthesis (meta-analysis or meta-regression) was performed; numerical examples (e.g., an entropy coefficient-of-variation illustration) are descriptive. IBSI specifies the mathematics of radiomic features and upstream steps (interpolation/re-segmentation, intensity discretization, convolutional filtering, and feature aggregation) and provides compliance resources (digital phantoms, benchmark values, validation checklists/portals). These measures improve cross-software agreement - especially for first-order and shape features - while clarifying residual variability in higher-order textures. IBSI complements QIBA (acquisition/reconstruction profiles) and DICOM (data/metadata standards) and is now embedded in widely used platforms (e.g., PyRadiomics, LIFEx), enabling more stable multicenter workflows and integration with machine-learning models. IBSI functions as a clinical quality infrastructure for radiomics. Adopting its standardized pipeline and compliance framework reduces inter-software variability, strengthens the generalizability of predictive and prognostic models, and supports regulatory readiness. Continued refinement of filters and texture metrics, together with transparent reporting and shared datasets, will further enhance reproducibility and clinical trust.