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

Table 1 Representative studies evaluating imaging-based pretreatment prediction of transarterial chemoembolization response in hepatocellular carcinoma

Ref.	Approach	Outcome	Main message	Main limitation
Kong et al[30], 2021	Pretreatment MRI radiomics nomogram	Early tumor response after TACE	MRI radiomics improved pretreatment response assessment beyond conventional imaging alone	Single-center retrospective design; no habitat-based analysis
Chen et al[31], 2021	Clinical-radiomic model	Objective response to first TACE	Integration of clinical and radiomic variables improved pretreatment stratification	Limited external generalizability
Peng et al[32], 2021	Radiomics plus deep learning	Initial response in intermediate-stage HCC	Hybrid artificial intelligence models may outperform simpler predictive approaches	Limited interpretability and black-box behavior
Vosshenrich et al[33], 2021	CT texture analysis with nested decision-tree model	Response to TACE	Quantitative CT texture analysis showed predictive potential before treatment	No multicenter validation
İnce et al[34], 2023	MRI clinicoradiomic machine-learning models	Local response after TACE	Addition of clinical variables improved performance compared with radiomics alone	Retrospective design and manual workflow
Xi et al[25], 2024	Contrast-enhanced MRI clinical-radiomics model	Short-term efficacy of DEB-TACE	Combined model outperformed imaging-only approaches	Findings limited to the drug-eluting bead setting
Li et al[37], 2024	Tumor growth pattern plus intra- and peritumoral radiomics	Initial TACE response	Peritumoral information improved predictive performance	Retrospective design and center-specific imaging workflow
Lv et al[24], 2026	Habitat imaging plus radiomics plus clinical features	Early response to TACE	Explicit modeling of intratumoral habitats improved predictive discrimination	Requires prospective validation and stronger biological grounding

MRI: Magnetic resonance imaging; TACE: Transarterial chemoembolization; HCC: Hepatocellular carcinoma; CT: Computed tomography; DEB-TACE: Drug-eluting bead transarterial chemoembolization.

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