Patient stratification in hepatocellular carcinoma: impact on choice of therapy

ABSTRACT

Introduction

HCC comprises roughly 60 to 80% of all primary liver cancers and exhibits wide geographical variability. Appropriate treatment allocation needs to include both patient and tumor characteristics.

Areas covered

Current HCC classification systems to guide therapy are either liver function-centric and evaluate physiologic liver function to guide therapy or prognostic stratification classification systems broadly based on tumor morphologic parameters, patient performance status, and liver reserve assessment. This review focuses on different classification systems for HCC, their strengths, and weaknesses, as well as the use of artificial intelligence in improving prognostication in HCC.

Expert opinion

Future HCC classification systems will need to incorporate clinic-pathologic data from a multitude of sources and emerging therapies to develop patient-specific treatment plans targeting a patient’s unique tumor profile.

KEYWORDS:

• Hepatobiliary surgery
• hepatocellular carcinoma
• stratification
• outcomes

Article highlights

• HCC is a highly heterogenous disease and traditional classification systems such as the BCLC and Milan criteria may result in undertreatment of certain patients with favorable tumor biology.

• In general, HCC classification systems to guide therapy are either liver function-centric and evaluate physiologic liver function to guide therapy or prognostic stratification classification systems broadly based on tumor morphologic parameters, patient performance status, and liver reserve assessment.

• The ALBI score may assess hepatic dysfunction and patient fitness for hepatectomy better than CTP score. Additional prospective studies are needed to confirm the utility of the ALBI score.

• The TTV, UTS, and TBS are alternative classification systems that may provide improved prognostic information regarding successful surgical resection or transplantation therapy over the Milan criteria. In addition, the TBS can be used in conjunction the BCLC to provide further therapeutic clarity for select patient populations.

• The advent of NGS technologies and investigations into the TME have further described HCC tumorigenesis, revealing potential therapeutic targets as well as biomarkers that predict therapeutic response.

• Machine-learning algorithms provide the opportunity to more comprehensively evaluate all data sources to improve HCC prognostication and therapy allocation.

• Future HCC classification systems will need to incorporate clinic-pathologic data from a multitude of sources and emerging therapies to develop patient-specific treatment plans targeting a patient’s unique tumor profile

Disclaration of interest

No potential conflict of interest was reported by the author(s).

Conflict of interest

A reviewer on this paper has received fees for congress participation from Roche. The remaining reviewers have no other relevant financial relationships or otherwise to disclose.

Additional information

Funding

This paper received no funding.