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

Abstract

Transarterial chemoembolization is an important treatment for intermediate-stage hepatocellular carcinoma. However, a significant challenge in daily clinical practice is identifying which patients are most likely to benefit from this treatment. Conventional imaging and standard clinical and radiological variables often do not capture the full biological complexity of these tumors. This is especially true for the intratumoral heterogeneity that can affect both treatment sensitivity and resistance. Habitat imaging offers a different approach to this challenge by partitioning the tumor into spatially distinct subregions that may reflect variations in perfusion, necrosis, cellularity and treatment vulnerability. The recent study suggests that magnetic resonance imaging-based habitat analysis may improve early response prediction when integrated with radiomic and clinical features. At the same time, this potential should be interpreted with caution. Important limitations remain, including protocol dependence, segmentation variability, limited multicentre reproducibility, uncertain biological correlates and the lack of prospective utility studies. In our view, the relevance of habitat imaging lies not only in improving predictive models, but also in its potential to support treatment allocation before therapy, reduce futile transarterial chemoembolization and strengthen multidisciplinary decision-making. This opinion review discusses the biological rationale, the currently available evidence, the main practical barriers and the further steps required before habitat imaging can become a clinically useful tool in hepatocellular carcinoma.

Key Words: Habitat imaging; Hepatocellular carcinoma; Transarterial chemoembolization; Magnetic resonance imaging; Radiomics; Tumor heterogeneity; Treatment response; Precision medicine

Core Tip: Habitat imaging offers a different way of reading magnetic resonance imaging in hepatocellular carcinoma, moving beyond simple lesion description and trying to capture the tumor’s internal spatial heterogeneity. Its value lies not only in the possibility of predicting response after transarterial chemoembolization, but also in helping clinicians think more carefully about pretreatment selection and multidisciplinary treatment planning. At present, however, this approach is still constrained by important limitations, including methodological fragility, poor standardization, and the lack of solid prospective validation. The key issue is no longer whether habitat imaging is conceptually attractive, but whether it is robust enough to become genuinely useful in clinical practice.

INTRODUCTION

Transarterial chemoembolization (TACE) remains a cornerstone in the management of intermediate-stage hepatocellular carcinoma and still holds a central place in current treatment algorithms and practice guidelines[1-4]. That said, its role is not framed in the same way across Western and Eastern recommendations, which reflects how much treatment allocation continues to rely on clinical judgment[5-7]. In daily practice, one of the most difficult aspects is that the true value of TACE often becomes clear only afterwards. Some patients who appear to be appropriate candidates based on liver function, tumor burden, stage, and conventional magnetic resonance imaging (MRI) or computed tomography derive little benefit. In contrast, others experience significant and sometimes lasting disease control. This difference is not just a clinical issue. It probably indicates underlying biological differences. Tumors grouped within the same stage may differ substantially in vascular architecture, necrotic burden, cellular density, and microenvironmental behaviour, all of which may influence sensitivity to ischemic and cytotoxic injury[4,6,8-11]. Heterogeneity has also been linked to variations in perfusion and in the surrounding microenvironment[12,13].

Conventional pretreatment assessment remains essential, but it has clear limits. A single lesion may include viable hypervascular areas, poorly perfused regions, internal necrosis, and more aggressive peripheral components, yet much of this complexity is often reduced to a single overall radiologic impression when the tumor is approached as a homogeneous target[14-17]. Radiomics began to challenge this reductionist view by showing that medical imaging contains considerably more information than can be captured by visual interpretation alone[14-18]. Later radiomic studies reinforced that concept across several tumor types[19-23]. Habitat imaging takes the argument a step further. Instead of describing the lesion only through global texture or intensity-based features, it attempts to identify spatially distinct subregions within the tumor and to characterize how these different components are distributed and related to one another[18,20,21].

Seen in this light, the article by Lv et al[24] is of interest not just because it introduces another predictive model, but because it applies a biologically plausible idea to a question that is highly relevant in practice. In their multicentre cohort, habitat features derived from contrast-enhanced MRI, when combined with radiomic and clinical variables, improved the prediction of early response to TACE compared with simpler models. The appeal of such a result is immediate. If biologically unfavourable tumors could be recognized before treatment, patient selection might improve, follow-up strategies could be adapted earlier, and alternative therapeutic options might be considered in a more timely way. The key question is whether habitat imaging is mature enough to support decision-making outside the research setting or whether it is still more of a promising idea than a practical tool for everyday clinical use.

In this opinion review, we argue that habitat imaging deserves serious attention in hepatocellular carcinoma, but it also needs careful evaluation. The concept is biologically plausible, technically innovative and clinically appealing. However, it is not ready for routine use. What matters now is not just whether these models can show strong discrimination in retrospective datasets, but whether they can be standardized, validated across different settings, explained in biologically meaningful ways, and integrated into the multidisciplinary approach that guides hepatocellular carcinoma care.

WHY DOES RESPONSE PREDICTION BEFORE TACE REMAIN AN UNMET NEED?

Predicting response before TACE remains difficult not because the field has lacked effort, but because both the disease and the treatment are inherently heterogeneous. Hepatocellular carcinoma is rarely a uniform entity. Even within the Barcelona Clinic Liver Cancer intermediate-stage category, patients can differ substantially in tumor number, lesion size, vascular behaviour, liver reserve, portal hypertension, inflammatory background, and competing risks related to cirrhosis[1-4]. TACE itself is also far from being a single, standardized intervention. Conventional lipiodol-based procedures, drug-eluting bead approaches, selective or superselective techniques, different embolization endpoints, retreatment strategies, and local institutional practice all introduce variability[5-7]. In that setting, it is hardly surprising that treatment response is so inconsistent.

Several clinical tools have been developed to make this uncertainty more manageable. Prognostic scores, retreatment scores, and benefit-risk frameworks have all been proposed to identify those patients most likely to benefit from repeated transarterial therapy[6-9]. Additional refinements have followed the same logic[10,11]. These tools remain useful, not least because they remind us that TACE should not be offered automatically to every patient with intermediate-stage disease. At the same time, most of them are built around variables such as tumor burden, alpha-fetoprotein, liver function, or biochemical changes observed after treatment. They tell us something important, but they do not really describe the internal biology of the target lesion. In practical terms, they often help identify the frail patient, the one with extensive disease, or the one who has already shown an unfavourable course after initial therapy. They are much less helpful in explaining why two tumors that look broadly similar on imaging may behave very differently from the outset[8-11].

This matters because the consequences of a wrong decision are significant. Ineffective TACE may delay a switch to systemic therapy or other locoregional strategies. It can expose the patient to repeated procedures that offer little oncological benefit, worsen liver function, and complicate the timing of future treatments. In contemporary multidisciplinary care, an ideal pretreatment model should do more than predict radiologic response at an early, fixed time point. It should address a more fundamental and clinically relevant question: Whether transarterial therapy is a reasonable choice from a biological perspective.

Imaging is naturally central to this problem because, in routine practice, it remains the closest non-invasive approximation of whole-tumor biology. Yet standard imaging assessment still relies largely on visual interpretation and on a relatively limited set of semantic features. These are essential, but they only partly reflect the internal spatial complexity of the lesion. A large hypervascular tumor may still contain regions with very different susceptibility to embolization. The same can be true for lesions with rim enhancement, internal hemorrhage, or mixed signal patterns on diffusion-weighted imaging. Radiomics emerged in part from dissatisfaction with this purely visual approach and has repeatedly shown promising results for pretreatment response prediction in hepatocellular carcinoma undergoing TACE[17,19,25-27]. More recent studies and broader syntheses have reinforced that impression[28,29]. Additional work has extended these observations across computed tomography, MRI, and combined clinical-radiomic pipelines[30-33]. More recent MRI-based and habitat-oriented models have pushed the field further[24,34-39]. The limitation, however, is that most radiomic pipelines still treat the tumor as a single region of interest. Habitat imaging tries to move beyond this by modelling spatial heterogeneity directly, rather than averaging it into a single summary.

WHAT DOES HABITAT IMAGING ADD BEYOND CONVENTIONAL MRI AND STANDARD RADIOMICS?

Conventional radiomics extracts quantitative features from a segmented lesion and uses them to build associations with diagnosis, prognosis, or treatment response[14-17]. Over time, this framework has expanded considerably, both methodologically and computationally[19-23]. This has been an important step forward, because it moved imaging beyond a purely descriptive role and toward a more quantitative form of phenotyping. Even so, standard radiomic workflows still tend to treat the tumor as a single object. Even when many features are extracted, they usually come from a global segmentation and end up reducing spatial diversity to one overall statistical summary. That may be acceptable for some purposes, but it becomes less persuasive when intratumoral heterogeneity is likely to be central to treatment response.

Habitat imaging begins from a different idea: That a lesion should not be viewed as a single radiologic entity, but rather as a more complex structure composed of subregions with distinct imaging behaviour[18,40,41]. In practical terms, the tumor is divided into multiple subregions on the basis of voxel-level or superpixel-level enhancement and texture patterns. These subregions, or “habitats”, can be described according to their internal characteristics, proportions, and sometimes spatial relationships. The intuition behind this is relatively straightforward: A strongly arterialized area is unlikely to carry the same biological meaning as a low-enhancement core or a heterogeneous peripheral ring. If these subregions can be identified reproducibly, they may provide a closer approximation to biologically relevant features such as aggressiveness, hypoxia, necrosis, vascular complexity, or differential treatment sensitivity[41-45].

This is particularly relevant in the setting of TACE: TACE works through a combination of arterial occlusion and local drug delivery, and its effectiveness depends heavily on tumor vascularity, microcirculation, embolization susceptibility, and the coexistence of viable and poorly perfused compartments. A single global tumor feature may miss much of this internal complexity. Habitat analysis, by contrast, is designed to preserve it. That is probably its main conceptual advantage over standard radiomics. It does not simply ask whether a lesion is heterogeneous overall. It asks where that heterogeneity is located and how it is organized within the tumor. A simplified conceptual workflow of this process, from multiphasic MRI acquisition to pretreatment therapeutic allocation, is summarized in Figure 1.

Figure 1 Conceptual workflow of habitat imaging before transarterial chemoembolization. Habitat imaging preserves spatial heterogeneity rather than averaging it away. A practical pipeline moves from multiphasic magnetic resonance imaging acquisition to lesion segmentation, habitat partitioning, integration with radiomic and clinical information, and finally pretreatment therapeutic allocation. The intended clinical value is to improve selection for transarterial chemoembolization, avoid futile procedures, and support multidisciplinary decision-making. HCC: Hepatocellular carcinoma; MRI: Magnetic resonance imaging; TACE: Transarterial chemoembolization. The figure was prepared with the help of ChatGPT and subsequently reviewed and approved by the authors. ChatGPT was used only to assist visual drafting and graphic arrangement, including the organization of boxes, arrows, icons, and overall layout. The scientific concepts, wording, figure logic, and final content were entirely defined, reviewed, and approved by the authors.

What makes this framework attractive is not only its technical sophistication, but also its clinical plausibility. In many respects, interventional oncologists already think in terms of habitats, even if they do not explicitly use that language. When reviewing a pretreatment scan, they often describe viable rims, central necrosis, mosaic architecture, patchy arterial supply or peripheral washout. Habitat imaging seeks to formalize this implicit visual reasoning and translate it into a reproducible quantitative model. Its potential lies not only in improving prediction, but also in aligning image analysis more closely with the way clinicians already interpret tumor behavior.

At the same time, moving from an appealing concept to a clinically useful tool is far from straightforward. Habitat pipelines are highly sensitive to methodology. Results may vary depending on image acquisition quality, phase timing, preprocessing, segmentation strategy, clustering algorithm, and feature selection. Different analytic choices can generate different habitat maps from the same lesion. Experience from the radiomics literature has already shown that standardization and reproducibility cannot be treated as secondary issues[20-23]. There is also a real risk of mistaking technical instability for biological signal, especially when models are developed in relatively small retrospective cohorts. For this reason, habitat imaging is probably best viewed not as a replacement for radiomics, but as a more demanding extension of it. If the field wants to argue that imaging can truly capture tumor ecology, then those ecological maps also have to prove that they are robust, interpretable, and reproducible across different centres.

HOW STRONG IS THE CURRENT EVIDENCE IN HEPATOCELLULAR CARCINOMA?

The literature on radiomics and artificial intelligence for predicting outcomes after TACE in hepatocellular carcinoma has now grown enough to justify cautious optimism. A number of single-centre and multicentre studies have shown that pretreatment features derived from computed tomography or MRI can predict objective response, early recurrence, or survival after TACE more effectively than conventional clinical models alone[26-29]. Several influential original studies support this broader impression[30-33]. More recent work has expanded the field further through clinicoradiomic models, deep-learning approaches, and the incorporation of peritumoral information[34-36]. Additional MRI-based and habitat-oriented reports have moved in the same direction[37-39]. The reviews and meta-analyses recently published tell a fairly consistent story: The signal seems to be real, combined radiomic-clinical models usually perform better than isolated clinical variables, and the reported results are often promising, even if methodological quality remains uneven and prospective validation is still limited[26,27].

Among MRI-based studies, Kong et al[30] showed that a pretreatment MRI radiomics nomogram could help predict tumor response after TACE. Chen et al[31] reported that a clinical-radiomic model improved prediction of objective response to first TACE. Peng et al[32] suggested that combining radiomics with deep learning might further improve predictive performance in intermediate-stage disease. İnce et al[34] later showed that clinicoradiomic models based on pretreatment MRI performed better than radiomics alone when selected clinical variables were added. More recent studies have pushed this approach further by incorporating peritumoral features, tumor growth patterns, or specific treatment settings such as drug-eluting bead TACE, again with generally favourable but still retrospective results[25,35-39]. Representative studies evaluating imaging-based pretreatment prediction of TACE response in hepatocellular carcinoma are summarized in Table 1.

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.

What makes the study by Lv et al[24] stand out from much of the earlier literature is that it explicitly models intratumoral heterogeneity through habitat analysis rather than relying only on whole-tumor radiomic summaries. Their framework combined MRI-based habitat features, radiomic features, and clinical variables, and was evaluated across training, internal validation, and external validation cohorts. That point matters, because external validation remains one of the weakest aspects of this field. The authors found that the combined model outperformed simpler alternatives, supporting the idea that spatial heterogeneity carries additional predictive information. Just as importantly, the study helped move habitat imaging from a concept explored in other tumor types into a more concrete hepatocellular carcinoma setting with clear therapeutic relevance.

The biological plausibility of this approach is also supported by work outside hepatocellular carcinoma. Habitat imaging has already been explored in breast cancer, glioma, ovarian cancer, and oral cancer[40,41], where it has been used to predict treatment response, molecular status, platinum resistance, or nodal involvement[42-46]. Although these studies differ substantially in disease type, endpoint, and methodology, they point in the same general direction: Spatially resolved imaging may capture clinically relevant information that is lost when a lesion is simplified into a single averaged structure. In hepatocellular carcinoma, a recent study has also linked advanced radiomic or habitat-related analysis to biologically significant features such as microvascular invasion probability and tertiary lymphoid structure assessment, suggesting that these imaging maps may reflect more than computational pattern recognition alone[43].

At the same time, the evidence should not be overstated. Recent work has also extended this line of investigation to the prediction of TACE refractoriness, including habitat-based approaches combined with arterial enhancement mapping, again with promising but still pre-implementation implications[47]. Most available studies are still retrospective, many come from limited geographic or technical settings, and a large proportion rely on imaging protocols and segmentation workflows that are not easily reproduced outside the original institution[26-29,34-40]. Sample sizes are often modest compared to the number of extracted features. External validation, when present, is usually geographic rather than temporal or prospective. Calibration, decision-curve analysis, and implementation-oriented endpoints are reported inconsistently. There is also a common tendency to emphasize discrimination metrics more than clinical transportability. A model with a high area under the curve is not automatically a model that changes management in a meaningful way.

For these reasons, the current evidence supports two conclusions at the same time. On the one hand, habitat imaging is no longer a speculative idea. It is supported by a growing and biologically coherent body of literature. On the other hand, it is still best regarded as a pre-implementation technology. The field has clearly moved beyond proof of concept, but it has not yet reached the point of routine clinical decision support.

WHAT ARE THE MAIN BARRIERS BEFORE CLINICAL ADOPTION?

The first barrier is technical reproducibility. Habitat maps can only be as reliable as the images and algorithms used to generate them. Variations in scanner vendor, field strength, sequence parameters, motion, contrast timing, reconstruction, intensity normalization, and lesion segmentation may all influence radiomic outputs[20-23]. These are not issues unique to habitat imaging, but they become even more critical when the tumor is divided into multiple subregions. In that context, what looks like a minor preprocessing difference may do more than slightly alter a feature value; it may change the habitat map itself. This is why standardization efforts such as Image Biomarker Standardisation Initiative matter so much, and why methodological shortcuts remain problematic[20,46]. The principal translational barriers and possible solutions are summarized in Table 2.

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

A second barrier is biological interpretability. Habitat imaging is attractive partly because it seems biologically intuitive, but intuition is not enough. If a model divides a lesion into several subregions, clinicians will naturally want to know what those regions represent. Are they showing viable hypervascular tissue, necrosis, fibrosis, hypoxia, inflammatory infiltrates, or just changes related to the imaging phase and signal intensity? Without consistent comparisons between radiology and pathology, the biological meaning of these areas is still somewhat unclear. This does not disprove the method, but it does limit confidence in how the results are interpreted[18,40]. Similar issues have come up in studies of other tumor types, and the wider radiomics literature has often pointed out the same conflict between performance and interpretability[41-45,48-51].

A third issue is model fragility. Radiomics has already shown that strong retrospective performance does not always lead to dependable generalization. The field has recognized this through ongoing concerns about overfitting, hidden surrogates like tumor volume, data leakage, unstable feature selection, and a lack of external validation. Habitat imaging is not immune to these problems. If anything, its added complexity may make them even harder to control when methodological rigor is lacking. High-dimensional models developed in relatively small retrospective cohorts can appear impressive on paper while remaining fundamentally unstable. For this reason, the cautionary lessons already learned in radiomics should be applied directly to habitat imaging rather than treated as a separate methodological issue[21-23].

Another major barrier is workflow integration. Even a technically sound model has limited value if it cannot be used within the pace and constraints of real multidisciplinary practice. Most decisions about hepatocellular carcinoma are made under time pressure. In these situations, radiologists, hepatologists, surgeons, oncologists and interventional radiologists need information that is not only correct but also clear and useful. A habitat pipeline that relies on extensive manual segmentation, non-standard software and unclear model outputs is unlikely to be widely accepted, regardless of how well it performs academically. Successful translation will depend not only on predictive accuracy but also on automation, reasonable turnaround times, clear outputs, and thresholds that can realistically be discussed in a tumor board instead of only in a computational environment.

Finally, there is the issue of endpoint selection: Many current studies focus on short-term objective response, which makes sense since it can be measured and is relevant in practice, but this approach does not capture the full value of a treatment decision. A biomarker that can predict early imaging response but does not help with treatment choices, maintain liver function, reduce unnecessary treatments, or influence outcomes that patients care about may still have limited value. If habitat imaging is to be meaningful in clinical settings, it should not only improve predictions but also assist clinicians in making better decisions. This is a tougher standard, but in the end, it is the one that matters most.

WHERE SHOULD THE FIELD GO NEXT?

The next phase of habitat imaging in hepatocellular carcinoma should probably focus less on showing that yet another retrospective model can achieve a high area under the curve, and more on whether the approach can withstand the demands of real clinical use. In our view, three priorities deserve particular attention. A practical roadmap from promising retrospective prediction to clinically meaningful adoption is outlined in Figure 2.

Figure 2 Translational roadmap from promising prediction to clinical adoption. The current literature supports promising retrospective predictive performance, but the field will only mature through prospective multicenter validation, stronger biological correlation, and workflow-ready decision support. Clinical adoption should be judged by better treatment allocation and not only by higher discrimination metrics. MVI: Multivariate index; AUC: Area under the receiver operating characteristic curve. The figure was prepared with the help of ChatGPT and subsequently reviewed and approved by the authors. ChatGPT was used only to assist visual drafting and graphic arrangement, including the organization of boxes, arrows, icons, and overall layout. The scientific concepts, wording, figure logic, and final content were entirely defined, reviewed, and approved by the authors.

First, prospective multicentre validation needs to become the rule rather than the exception. At this point, the field has generated enough retrospective signal to justify that step. What is still missing are protocol-aware datasets, temporal validation, and pre-specified analytical pipelines tested across institutions using different scanners and treating different patient populations[26-29]. More recent reviews, together with the multicentre study by Lv et al[24], point in the same direction. This is probably the only realistic way to separate genuinely transportable imaging features from findings that are mainly driven by local technical conditions.

Second, habitat imaging needs to be more directly connected to biology. Correlation with pathology, microvascular invasion, immune contexture, transcriptomic profiles, or patterns of post-treatment necrosis would help the field move beyond purely statistical associations and towards findings that are more biologically credible[40-42]. Translational work in other tumor types suggests this same need[43-45,48]. Even a partial biological anchor would make a difference. Clinicians are far more likely to trust and use a model when they have at least some sense of what it is capturing, rather than simply being told that it performs well.

Third, future studies need to go beyond mere prediction and examine whether information about habitats improves decision-making. The aim is not only to determine whether habitat imaging can predict early responses better than traditional models, but also whether it changes treatment choices, reduces unnecessary TACE, enables earlier shifts to other strategies, or helps maintain liver function by avoiding ineffective retreatments. In other words, the goal should not just be better modelling; it should be better care.

There is also room for broader integration. Habitat-derived features may become more useful when combined with semantic imaging markers, laboratory data, liver function measures, circulating biomarkers, or other artificial intelligence-based approaches[28,29]. Broader reviews on artificial intelligence in hepatocellular carcinoma imaging suggest a similar path and indicate that multimodal integration will likely shape the next generation of decision-support tools. Still, added complexity does not always lead to benefits. Models that are methodologically elegant but difficult to interpret, explain or use in practice rarely survive outside the research setting. The tools that are likely to be important will be those that maintain enough rigor for methodologists while also being simple enough for clinicians to use confidently.

At present, habitat imaging sits in an interesting middle ground. It is no longer just an exploratory idea, but it is not yet a routine clinical instrument. That is not really a weakness. If anything, it suggests that the field has matured enough to deserve serious attention, while still requiring a fair degree of humility about what has and has not yet been proven.

CONCLUSION

Habitat imaging captures something that clinicians have long suspected but have not really been able to measure with precision: Hepatocellular carcinoma is not biologically uniform, and its internal spatial heterogeneity may influence response to TACE. The study by Lv et al[24] is important because it brings this intuition into a clinically relevant MRI framework and shows that explicitly modelling intratumoral heterogeneity can improve predictive performance.

In our view, the real value of habitat imaging is not that it promises a definitive answer, but that it helps frame the pretreatment question in a more biologically credible way. The issue is no longer only whether a lesion is hypervascular or technically amenable to treatment. It is whether its internal organization suggests a true biological susceptibility to transarterial therapy. That is a more relevant question, and probably a more clinically useful one.

At the same time, current research shows that predictive performance alone is not enough. For habitat imaging to be clinically relevant, it must show consistency across platforms, demonstrate clearer biological grounding and real usefulness in multidisciplinary decision-making. The challenge is not just to create better models. It is to develop tools that clinicians can trust, understand, and use in the daily care of hepatocellular carcinoma.

At present, habitat imaging should still be regarded as promising rather than practice-changing. The field requires stronger standardization, deeper biological validation and prospective studies that demonstrate actual clinical utility. Until then, habitat imaging should assist in scientific development and enrich multidisciplinary discussion, but it should not replace clinical judgment. If these next steps are achieved, it may become one of the most meaningful links between quantitative imaging and tailored treatment in hepatocellular carcinoma.