Path towards personalized care of hepatocellular carcinoma: a nomogram for survival prediction

Hepatocellular carcinoma (HCC) remains a significant global health challenge, particularly in Asia, where its incidence is high (1). The tumour heterogeneity and diverse risk factors require the development of reliable tools for accurately predicting patient outcomes (2). In this context, the study by Liu et al. represents a valuable contribution to the field (3). The authors address a critical need for improved prognostic tools in HCC management. The authors have constructed and validated a nomogram based on a large, real-world clinical dataset. This offers a practical tool for clinicians to estimate individual survival probabilities. While the overall survival (OS) of HCC patients remains poor, accurate prediction models can guide treatment decisions by improving risk stratification, thus potentially improving survival or functional outcomes. This editorial will delve into the strengths and limitations of the nomogram developed by Liu et al., highlighting its potential clinical utility and discussing avenues for future research.

Strengths of the study

The study by Liu et al. possesses several notable strengths. The large sample size of 1,128 HCC patients enhances the statistical power of the study and increases the generalizability of the findings. The use of real-world data, as opposed to data from highly controlled clinical trials, strengthens the study’s relevance to everyday clinical practice (4).

Nomograms are graphical tools that integrate diverse variables to provide an individualized estimate of a patient’s probability of experiencing a specific outcome, such as survival or recurrence (5). By translating complex statistical models into a user-friendly format, nomograms empower clinicians to rapidly assess risk and guide treatment decisions at the point of care. The authors constructed and validated their nomogram, employing a range of statistical techniques to assess its predictive accuracy and clinical relevance. The multivariate Cox regression analysis identified five independent risk factors for HCC survival: HCC screening status, tumour size, presence of alcoholic liver disease (ALD), Child-Pugh classification, and therapy method. These variables were incorporated into a nomogram.

The nomogram’s performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) of 0.868 indicates excellent discrimination, while the calibration curves demonstrate good agreement between predicted and observed survival probabilities. The DCA further confirms the nomogram’s clinical utility, demonstrating its net benefit compared to “treat all” or “treat none” strategies.

The authors have also addressed the issue of potential multicollinearity among the risk factors, employing variance inflation factor analysis to ensure that the independent prognostic value of each variable is not compromised by its correlation with other variables. This rigorous approach strengthens the validity of the nomogram and provides confidence in its ability to accurately predict survival outcomes.

Limitations and future directions

The study by Liu et al. is not without limitations, which the authors acknowledge. Single-centre retrospective analysis may limit the generalizability of the study findings to patients in other parts of the world (6). Similarly, nomograms, like any predictive tool, are only as reliable as the data it’s built upon, the ‘bias in bias out’ phenomenon alluded to by Wong et al. (7). Additionally, nomograms provide an average risk estimate, and individual patient outcomes can differ significantly. Therefore, clinicians should always consider the unique circumstances of each patient when making treatment decisions. Furthermore, the study’s exclusion of novel biomarkers, such as circulating tumour DNA, exosomal microRNAs, and interactions of tumour microenvironment may reduce its predictive accuracy and clinical utility (8).

While the authors have demonstrated the independent prognostic value of these variables, further research is needed to elucidate the underlying mechanisms by which they influence HCC outcomes (9,10). It is likely that screening status and treatment extent (conservative or expectant treatment) are likely indirect surrogates of tumour number, size, multifocality, and other determinants rather than root causes of poor survival. In this regard, it is important to consider lead-time bias. Patients who undergo HCC screening are more likely to be diagnosed at an earlier stage of the disease, when curative treatment options are more effective, thus conferring survival advantage to screened populations.

Similarly, the choice of treatment modality may be influenced by a variety of factors, including patient preferences, comorbidities, and the availability of specialized expertise. This is an important consideration as HCC treatment is determined not only by oncologic assessment but also by inherent liver function. Thus, it is possible that patients who undergo conservative or expectant treatment may have more advanced disease or liver dysfunction in addition to possible underlying medical conditions that preclude more aggressive interventions. These factors may confound the association between treatment modality and survival outcomes.

Tumour size and volume

Giant HCCs, defined as ≥10 cm in diameter, are often associated with more aggressive tumour characteristics with lower 1-year overall survival compared to non-giant HCC (69.5% vs. 90.1%). Non-giant HCC showed a significantly lower hazard ratio [HR, 0.53, 95% confidence interval (CI): 0.50–0.55, P<0.001] (10). Thus, the authors’ study is relevant as it adds to the body of evidence which endorses limitations of existing HCC staging systems that do not account for size differences and future revisions must include size differences to better prognosticate HCC patients. If the prognostic impact is due to size alone or due to tumour volume, or a combination of both, needs to be determined by prospective studies. Logically, larger diameter tumour has a higher cell count and a greater tendency for proximity to major inflow and outflow structures, vascular invasion, innate mutation risk, along with higher tumour heterogeneity; all of which increases risk of inadequate treatment response or increased risk of recurrence, and hence lower survival outcomes. Figure 1 provides a summary of some of the considerations of large diameter HCC in its management and oncologic impact.

Figure 1 Impact of large tumour size on hepatocellular carcinoma outcomes.

Disease-free survival (DFS) and OS

Liu et al. have only reported OS and not DFS, which is unusual for oncological studies. While it is possible that this was due to lack of data, it is essential to discuss. We do not consider this as major limitation. As the majority of HCC recurrences are only detected at surveillance imaging with patients being asymptomatic, using DFS as a surrogate for OS is less important in context of HCC management. Further, authors should be commended for reporting the 1-year OS which is often overlooked in contemporary surgical oncology, with neither surgeons nor medical oncologists consistently tracking or reporting it (11). This metric is crucial, as it identifies a patient subgroup (approximately 10–15%) who might be better managed non-operatively, potentially alleviating suffering inherent to perioperative morbidity, and also potentially reducing healthcare costs. This is especially relevant given that trans-arterial chemoembolization combined with radiofrequency ablation can achieve comparable oncologic results to surgical resection in the selected group of HCC patients (12).

Personalized cancer care

The study by Liu et al. underscores the importance of personalized approaches to HCC management. The nomogram provides a valuable tool for stratifying patients based on their individual risk profiles, allowing clinicians to tailor treatment decisions and surveillance strategies accordingly. However, the path towards truly personalized care requires a more comprehensive understanding of the molecular and genetic attributes of HCC (13).

Emerging technologies, such as next-generation sequencing and radiomics, hold promise for identifying novel biomarkers and predicting treatment response in HCC patients. Integrating these emerging technologies into clinical practice requires a collaborative effort between clinicians, researchers, and data scientists. Multicentre multi-omic studies are needed to identify and validate novel biomarkers to develop clinically relevant and accurate predictive algorithms. The utility and application of artificial intelligence algorithms may assist personalized medicine advances. It is essential to ensure that these algorithms are transparent, unbiased, and validated in diverse patient populations (14).

Conclusions

The study by Liu et al. offers a valuable nomogram for risk stratification in HCC, representing a step towards personalized management to improve HCC outcomes through tailored, multidisciplinary approaches.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Gastrointestinal Oncology. The article did not undergo external peer review.

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-298/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy of integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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