Are we ready for a molecular diagnostics-driven era in hepatobiliary surgery?

Hepatobiliary surgery is entering into a period of rapid conceptual evolution as molecular diagnostics begin to influence traditional models of perioperative assessment. For decades, surgical candidacy has been determined through parameters such as future liver remnant (FLR) volume, indocyanine green clearance, and biochemical surrogates of synthetic function. These approaches have served the field well, yet they each offer only a partial window into the complexity of liver regeneration and injury. Recent developments in circulating microRNAs, cell-free DNA, and multi-omics profiling suggest that surgical risk stratification may soon adopt a more integrative, biologically informed foundation.

Within this emerging landscape, a recent study evaluating a microRNA-based classifier for predicting posthepatectomy liver failure (PHLF) highlights both the opportunities and ongoing challenges in the transition to molecularly informed surgery (1). The biological rationale is strong. MicroRNAs regulate pathways central to hepatocellular regeneration, inflammation, and metabolic adaptation. Their dysregulation in patients who develop PHLF aligns with evolving mechanistic insights into postoperative liver dysfunction (2). For example, a common guideline is to maintain an FLR ≥30% in normal livers (with higher cutoffs in diseased livers), though prospective validation of this threshold is limited (3). Studies in acute liver failure, drug-induced liver injury, and fibrosis progression further validate microRNAs as quantifiable reflections of hepatic stress and resilience (4,5). Representative molecular diagnostics with clinical relevance to hepatobiliary surgery are summarized in Table 1.

Yet progress also raises difficult questions. Many emerging molecular classifiers perform well in initial derivation cohorts but falter in external validation. Populations need hepatobiliary surgery treatment are markedly heterogeneous. Hepatocellular carcinoma, cholangiocarcinoma, and metastatic colorectal cancer carry distinct biological signatures and regenerative patterns (9). Background liver disease varies from chronic viral hepatitis to advanced metabolic-associated steatohepatitis (10). A microRNA panel that succeeds in one cohort may not maintain the same accuracy in another. Without large-scale, multi-center validation, no biomarker, regardless of sophistication, can be applied with confidence across all surgical contexts (11).

Another factor shaping the adoption of molecular diagnostics is cost. MicroRNA assays and liquid biopsy platforms increase the financial burden of preoperative evaluation. Their predictive benefit must translate into measurable changes in clinical decision making to justify routine use. A modest improvement in discrimination is insufficient if it does not alter operative strategy, guide perioperative monitoring, or reduce complications in a meaningful way. Figure 1 provides a conceptual comparison of conventional imaging-based predictors and emerging molecular diagnostic approaches for predicting PHLF. This tension underscores the broader challenge of incorporating molecular tools into health systems where resources vary dramatically and conventional markers like albumin-bilirubin (ALBI) and aspartate aminotransferase to platelet ratio index (APRI) scores remain freely available (12).

Figure 1 Comparative schematic of traditional vs. molecular diagnostic frameworks for predicting post-hepatectomy liver failure. AI, artificial intelligence; ALBI, albumin-bilirubin; APRI, aspartate aminotransferase to platelet ratio index.

The future of PHLF prediction likely rests not on individual markers but on integrated approaches (13). Composite models that combine clinical parameters, volumetric assessments, conventional biochemical indices, and molecular signatures may better capture the multi-dimensional physiology of postoperative recovery (14). Recent work in acute liver failure demonstrates that multi-parametric models outperform single-marker strategies, suggesting a parallel opportunity for hepatobiliary surgery (6). Advances in machine learning further expand this potential by enabling the synthesis of non-linear relationships across diverse data streams. These algorithms can accommodate clinical variables, imaging-derived features, gene expression profiles, and dynamic biomarker trajectories in ways traditional models cannot.

Emerging fields offer further promise. Cell-free DNA and exosomal RNA profiling may complement microRNA signatures by reflecting ongoing hepatocyte injury and regenerative activity (7). Temporal monitoring of molecular biomarkers during the perioperative period may provide real-time insight into evolving liver physiology (15). Frailty biology and patient-reported outcomes add yet another dimension, emphasizing that recovery extends beyond biochemistry into functional and systemic resilience (8).

These developments invite a balanced perspective. Molecular diagnostics will not replace established tools overnight. Nor should they. Instead, they are likely to augment traditional assessments, refining risk estimates and supporting individualized surgical planning. The most huge advances will come from thoughtful integration rather than wholesale substitution. Equally important is the need to ensure that molecular diagnostics do not amplify disparities in surgical care. Techniques must be accessible, cost-conscious, and adaptable across global practice environments.

Molecular diagnostics are poised to reshape hepatobiliary surgery. The field now faces the task of validating and integrating these tools in a way that sustains scientific rigor while improving clinical outcomes. The promise is substantial, and the direction is clear. The challenge lies in ensuring that innovation is matched with evidence, equity, and thoughtful implementation.

Acknowledgments

Figure 1 was generated using Gemini 3 Pro (Google DeepMind) based on our conceptual design and specific instructions provided by the authors. The AI-generated output was subsequently reviewed, manually refined, and approved by all authors to ensure scientific accuracy.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, HepatoBiliary Surgery and Nutrition. The article has undergone external peer review.

Peer Review File: Available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-2025-1-947/prf

Funding: This study was supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (Nos. 2024ZD0520500 and 2024ZD0520506 to T.Y.), Shanghai Outstanding Academic Leader Program (No. 23XD1424900 to T.Y.), the National Natural Science Foundation of China (Nos. 82425049 and 82273074 to T.Y.), and Shanghai Health and Hygiene Discipline Leader Project (No. 2022XD001 to T.Y.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-2025-1-947/coif). T.Y. serves as an unpaid editorial board member of HepatoBiliary Surgery and Nutrition. T.Y. reports grants from the National Science and Technology Major Project of the Ministry of Science and Technology of China (Nos. 2024ZD0520500 and 2024ZD0520506), Shanghai Outstanding Academic Leader Program (No. 23XD1424900), the National Natural Science Foundation of China (Nos. 82425049 and 82273074), and Shanghai Health and Hygiene Discipline Leader Project (No. 2022XD001). The other 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 or 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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