Habitat imaging before transarterial chemoembolization in hepatocellular carcinoma: Ready for clinical decision-making?

Table 2 Major barriers to clinical implementation of habitat imaging in hepatocellular carcinoma

Barrier	Why it matters	Possible solution
Acquisition variability	Differences in scanner platforms, sequence parameters, contrast timing, and image reconstruction can alter extracted features and reshape habitat maps	Protocol harmonization, Image Biomarker Standardisation Initiative-compliant preprocessing, and multicenter temporal validation
Segmentation dependence	Habitat analysis is strongly influenced by how the tumor and its subregions are delineated	Semi-automated or automated segmentation, robustness testing, and inter-reader reproducibility studies
Feature instability	High-dimensional pipelines may generate brittle signatures and hidden surrogates, including tumor volume-related effects	Pre-specified analytical pipelines, feature stability filtering, external validation, and calibration reporting
Biological uncertainty	Imaging-defined habitats remain only partly linked to underlying pathology and tumor biology	Radiology-pathology correlation, radiogenomic integration, and correlation with post-treatment necrosis patterns
Limited interpretability	Opaque models are difficult to trust when major treatment decisions are involved	Transparent feature reporting, explainable outputs, and clinically readable summaries
Workflow burden	Manual segmentation and non-standard software reduce feasibility in routine multidisciplinary practice	Streamlined software, automated processing, and tumor-board-ready reporting
Endpoint mismatch	Short-term objective response does not always translate into meaningful clinical benefit	Study designs incorporating liver function preservation, treatment transition planning, time to treatment failure, and survival outcomes